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Evaluating gRPC as an Alternative to REST for E-commerce and Inventory Management APIs

I. Introduction to API Paradigms: REST and gRPC

The design and implementation of Application Programming Interfaces (APIs) are critical for modern software systems, particularly for complex platforms like e-commerce and inventory management systems such as Sylius and InvenTree. Traditionally, REST (Representational State Transfer) has been the dominant architectural style for building web APIs. However, gRPC (gRPC Remote Procedure Calls) has emerged as a high-performance alternative, especially for microservice architectures. This report provides a comprehensive analysis of gRPC as an alternative to REST, focusing on its potential application in systems akin to Sylius or InvenTree.

A. RESTful APIs: A Brief Overview

REST is an architectural style for designing networked applications, first conceptualized by Roy Fielding. It is not a protocol itself but a set of constraints that, when applied to an architecture, result in desirable properties such as performance, scalability, and modifiability.1 The core principles of REST include:

RESTful APIs predominantly use HTTP/1.1 as their communication protocol and commonly employ JSON (JavaScript Object Notation) or XML (Extensible Markup Language) for data exchange.3 Due to its simplicity, alignment with HTTP standards, and the human-readability of JSON, REST has become the de facto standard for web APIs, especially for public-facing interfaces and inter-service communication in many microservice architectures.1 The stateless nature of RESTful services is a key factor in their ability to scale horizontally.

B. gRPC: An Introduction to High-Performance RPC

gRPC, which stands for gRPC Remote Procedure Calls, is an open-source, high-performance, language-agnostic framework initially developed by Google.5 It evolved from Google’s internal RPC system called Stubby, designed to connect a vast number of microservices within their data centers.6 gRPC was open-sourced in 2015 and has since gained significant traction.

The core concepts underpinning gRPC include:

The key benefits offered by gRPC include its modern design, high performance, lightweight nature, language independence (with tooling for generating client and server code in many languages), comprehensive support for various streaming patterns (unary, server-streaming, client-streaming, and bidirectional-streaming), and reduced network bandwidth usage due to efficient binary serialization.5 These characteristics make gRPC particularly well-suited for communication between microservices, real-time services, and polyglot environments where efficiency is paramount.

The evolution from text-based, more flexible protocols like REST over HTTP/1.1 with JSON towards binary, contract-driven, and performance-optimized protocols like gRPC over HTTP/2 with Protobuf reflects a growing need for efficiency in internal and high-throughput systems. As applications, especially e-commerce and inventory systems, become more distributed and demand lower latency, the architectural choices made at the API layer become increasingly critical. REST gained popularity due to its simplicity and alignment with web standards, with JSON becoming widely adopted for its human readability and ease of use with JavaScript.

However, as systems scaled and microservice architectures became prevalent, the overhead associated with text-based serialization (like JSON parsing) and the limitations of HTTP/1.1 (such as head-of-line blocking and the lack of multiplexing) became significant performance bottlenecks for internal, high-volume communications.3 Google, operating at a massive scale, developed Stubby (gRPC’s predecessor) and subsequently gRPC to address these performance challenges, emphasizing binary protocols (Protobuf) and a more efficient transport mechanism (HTTP/2).6 This indicates a trend where the choice of API technology is increasingly tailored to specific requirements: REST often remains the choice for public APIs requiring broad accessibility, while gRPC is favored for optimized internal communication.

The performance advantages of gRPC are directly linked to its foundational technology choices. HTTP/1.1, commonly used with REST, often necessitates a new connection per request or can suffer from head-of-line blocking if connections are reused sequentially.3 In contrast, HTTP/2, the transport for gRPC, allows multiplexing of multiple requests and responses over a single, long-lived TCP connection, significantly reducing connection setup overhead and latency.3 Furthermore, JSON, the typical data format for REST, is text-based and relatively verbose.17 Protobuf, gRPC’s default, is a binary format defined by a schema, resulting in more compact messages.3 These smaller, binary messages require less bandwidth and are inherently faster to serialize and deserialize compared to their larger, text-based JSON counterparts.3 Thus, the selection of HTTP/2 and Protobuf are primary drivers for gRPC’s raw performance benefits. It’s important to recognize that the emergence of gRPC does not signal the obsolescence of REST. Instead, it signifies a maturing API landscape where the optimal technology choice is increasingly dependent on the specific use case, such as distinguishing between internal microservices communication and public-facing APIs.1

II. Comparative Analysis: gRPC vs. REST

A detailed comparison between gRPC and REST is essential for understanding their respective strengths and weaknesses, particularly when considering them for complex applications like e-commerce or inventory management systems.

A. Performance Deep Dive

Performance is often a primary driver for considering gRPC. This includes latency, throughput, payload size, and overall resource utilization.

Latency and Throughput:

gRPC generally exhibits lower latency and higher throughput compared to REST APIs that typically rely on HTTP/1.1. This performance advantage stems from gRPC’s use of HTTP/2, which supports features like request/response multiplexing over a single TCP connection, persistent connections, and header compression (HPACK).3 These features reduce the overhead associated with establishing new connections for each request and minimize the amount of data transmitted for headers. In contrast, HTTP/1.1 can suffer from head-of-line blocking, where a slow request can hold up subsequent requests on the same connection, and incurs higher connection setup costs if new connections are made frequently.3

Several benchmarks and studies corroborate these performance claims. For instance, some tests suggest gRPC can be 7 to 10 times faster than REST for specific payloads and scenarios, particularly in microservice architectures where efficient inter-service communication is crucial.4 A comparative study involving gRPC, REST, and SOAP demonstrated that gRPC consistently outperformed the other two protocols in terms of both throughput and latency, especially for smaller message sizes. The study also highlighted gRPC’s superior scalability when handling an increasing number of concurrent clients.22 For e-commerce platforms, these performance gains could translate into faster API responses for critical operations such as product lookups, real-time inventory checks, and order processing, ultimately enhancing user experience and system efficiency.

Payload Size and Serialization Efficiency:

A significant contributor to gRPC’s performance is its use of Protocol Buffers for data serialization. Protobuf defines data structures in .proto files and serializes this structured data into a compact binary format. This binary representation is typically much smaller than the text-based JSON format commonly used in REST APIs.3 Smaller payloads mean less data needs to be transmitted over the network, reducing bandwidth consumption and speeding up data transfer.

Furthermore, the process of serializing data into Protobuf binary format and deserializing it back into language-specific objects is generally faster and more CPU-efficient than parsing and generating JSON text.3 For applications dealing with large volumes of data or complex data structures, such as detailed product information or extensive order histories in an e-commerce system, this efficiency in serialization and payload size can lead to substantial performance improvements and cost savings, especially in bandwidth-constrained environments like mobile applications.

Resource Utilization (CPU, Memory):

The efficiency of binary serialization with Protobuf typically results in lower CPU utilization compared to the parsing of text-based JSON, which can be computationally more intensive.13 Additionally, HTTP/2’s header compression mechanisms, like HPACK, reduce the size of transmitted headers, which can contribute to lower memory footprints, especially when many small requests are being made.20 Reduced CPU and memory usage translates directly to lower operational costs in cloud environments, as fewer or smaller server instances might be required. It also means better capacity utilization on existing hardware infrastructure.

B. Client-Server Communication Models

The way clients and servers interact differs significantly between gRPC and REST, particularly concerning streaming capabilities.

gRPC Streaming Capabilities:

gRPC natively supports several modes of streaming, built upon the capabilities of HTTP/2 3:

Streaming is a fundamental and powerful feature of gRPC, offering a significant advantage over REST, which typically relies on less integrated solutions like polling or separate WebSocket connections to achieve similar real-time or continuous data exchange functionalities.

REST Request-Response Model:

REST APIs primarily operate on a request-response model, where the client sends a request, and the server sends a response.1 This is typically a unary interaction. While effective for many use cases, this model can be inefficient for scenarios requiring real-time updates or continuous data flow. Achieving such functionality with REST often involves workarounds like:

While these methods work, they are not inherent to the REST architectural style and can introduce additional complexity and overhead. The simplicity of REST’s request-response model is well-understood, but it can be a limiting factor for modern e-commerce applications that increasingly demand real-time interactions, such as live stock updates or instant order status notifications.

C. Data Serialization: Protocol Buffers vs. JSON

The choice of data serialization format profoundly impacts API performance, efficiency, and development practices.
Protocol Buffers (gRPC):

Protocol Buffers (Protobuf) are gRPC’s default mechanism for serializing structured data. Key characteristics include:

The strict schema and code generation offered by Protobuf significantly reduce the likelihood of integration errors and can accelerate development by providing ready-to-use client and server stubs. The binary format is a cornerstone of gRPC’s performance characteristics.

JSON (REST):

JSON is the most common data format for REST APIs. Its key characteristics are:

JSON’s human-readability, ease of use with web technologies, and broad ecosystem support are its primary advantages, making it an excellent choice for public APIs and browser-based clients where interoperability and developer accessibility are key.

D. Error Handling Mechanisms

Effective error handling is crucial for robust distributed systems. gRPC and REST approach this differently.

gRPC Status Codes:

gRPC defines a set of standardized status codes to indicate the outcome of an RPC.33 These are integer codes accompanied by an optional string message providing more details. Some common gRPC status codes include:

Beyond these basic codes and messages, gRPC supports a richer error model using google.rpc.Status and error_details.proto. This allows servers to return structured error details, such as ErrorInfo (providing domain, reason, and metadata), RetryInfo (guiding clients on retry attempts), QuotaFailure, or BadRequest (detailing specific violations in the request).35 This structured approach provides more context to the client, enabling more sophisticated and automated error handling. For instance, RetryInfo can explicitly tell a client whether an operation is safe to retry and after what delay, which is very useful for building resilient systems.

REST HTTP Status Codes:

REST APIs rely on standard HTTP status codes to communicate the outcome of requests.2 These are well-understood and universally supported by HTTP clients and intermediaries. Common examples include:

While HTTP status codes provide a general indication of the outcome, detailed error information is typically conveyed within the JSON response body. However, the structure and content of these JSON error payloads are not standardized across all REST APIs, which can make consistent and robust error handling more challenging for clients integrating with multiple services. Clients often have to parse custom error formats for each API.

In comparison, gRPC’s error model, particularly with the google.rpc.Status extensions, offers a more granular and standardized way to convey detailed, structured error information. This can be highly beneficial for programmatic error handling in complex microservice environments, allowing clients to make more intelligent decisions based on the specific error details provided.

E. API Caching Strategies and Challenges

Caching is a vital strategy for improving API performance and reducing server load. However, the approaches and effectiveness differ between REST and gRPC.

REST Caching:

REST APIs can effectively leverage standard HTTP caching mechanisms, especially for idempotent GET requests.36 These include:

This standardized HTTP caching is well-supported by web browsers, Content Delivery Networks (CDNs), and reverse proxies, making it relatively straightforward to implement effective caching for read-heavy REST APIs.

gRPC Caching Challenges:

Caching gRPC responses presents more significant challenges due to several inherent characteristics:

Amazon CloudFront, for example, explicitly states that gRPC traffic is considered non-cacheable and bypasses its regional edge caches, routing requests directly to the origin.38 This underscores the difficulty of leveraging standard CDN caching for gRPC.

gRPC Caching Strategies:

Despite these challenges, caching can be implemented for gRPC services, though it often requires more application-specific or infrastructure-aware approaches:

The performance benefits of gRPC, stemming from HTTP/2 and Protocol Buffers, are clear.3 However, these choices introduce complexities, particularly in debugging binary payloads 1 and implementing effective caching strategies.14 REST, with its simpler text-based JSON and reliance on HTTP/1.1, offers broader tooling and easier debugging but may fall short in high-performance scenarios. This implies a fundamental trade-off: the pursuit of maximum performance with gRPC may necessitate investment in specialized tools and more complex caching architectures. For internal services where performance is paramount, this trade-off is often justifiable. For public-facing APIs, the ease of use and mature ecosystem of REST might be more advantageous.

A key architectural distinction is gRPC’s native support for various streaming modalities 3, which is a paradigm shift from REST’s predominantly request-response nature.1 This allows for continuous data flows and real-time updates without the complexities of polling or managing separate WebSocket connections. For platforms like Sylius or InvenTree, this could fundamentally change how features like real-time inventory tracking, live order status updates, or collaborative product management are architected and experienced by users.

Furthermore, gRPC’s use of Protocol Buffers enforces a strict, pre-defined contract through .proto files, which are compiled into client and server code.3 This strong typing is highly beneficial for ensuring consistency and reducing integration errors, especially in complex microservice environments. JSON, often used with REST, is inherently more flexible and typically does not require schema pre-compilation 1, which can be an advantage for public APIs needing rapid iteration. However, this flexibility can also lead to ambiguity if not rigorously managed with tools like OpenAPI. gRPC’s approach inherently mitigates such ambiguity.

A significant consideration for e-commerce platforms, which often rely heavily on caching for performance (especially for product catalogs and frequently accessed static data), is the inherent difficulty in caching gRPC responses using standard HTTP mechanisms.14 REST APIs, particularly their GET endpoints, integrate seamlessly with CDNs, browser caches, and reverse proxies.36 gRPC’s typical use of POST for all RPCs, combined with binary and potentially dynamic streaming content, makes it challenging for these standard intermediaries to cache effectively.14 Therefore, adopting gRPC for public-facing, read-heavy e-commerce APIs like product catalogs would necessitate careful design of caching strategies, potentially involving application-level caching or specialized gRPC-aware gateways. This added complexity could offset some of the raw performance gains in such scenarios.

Table 1: Feature Comparison: gRPC vs. REST

FeaturegRPCREST
ProtocolHTTP/2 3Typically HTTP/1.1 (can use HTTP/2) 1
Data FormatProtocol Buffers (binary) 3JSON, XML, plain text (text-based) 1
Communication StyleUnary, Server-streaming, Client-streaming, Bidirectional-streaming 3Primarily Request-Response (Unary) 1
PerformanceHigh throughput, low latency 3Generally lower performance than gRPC for comparable tasks 4
Payload SizeSmall, compact (binary) 3Larger, verbose (text) 17
Schema DefinitionContract-first with .proto files (strongly typed) 5Often schema-less or uses external definitions (e.g., OpenAPI) 17
Code GenerationBuilt-in, multi-language 2Relies on third-party tools (e.g., Swagger Codegen) 1
CachingChallenging with standard HTTP caches; requires specific strategies 14Leverages standard HTTP caching (Cache-Control, ETags) 36
Browser SupportRequires gRPC-Web and a proxy for direct browser use 1Native browser support 1
ToolingGood, especially for code generation; debugging binary can be harder 5Mature and extensive ecosystem 1
Error HandlingDefined status codes, supports rich error details (google.rpc.Status) 33Standard HTTP status codes, custom error payloads in body 2
Typical Use CasesInternal microservices, real-time streaming, polyglot systems, performance-critical APIs 1Public APIs, web services, simple CRUD operations 1

Table 2: gRPC Status Codes vs. HTTP Status Codes

ScenariogRPC Status Code (Name)gRPC Code (Number)Corresponding HTTP Status Code(s)Description
SuccessOK0200 OK, 201 Created, 204 No ContentOperation completed successfully. 34
Client ErrorINVALID_ARGUMENT3400 Bad RequestClient specified an invalid argument (e.g., malformed request). 34
NOT_FOUND5404 Not FoundRequested entity/resource was not found. 34
ALREADY_EXISTS6409 ConflictEntity client tried to create already exists. 34
PERMISSION_DENIED7403 ForbiddenCaller does not have permission. 34
UNAUTHENTICATED16401 UnauthorizedRequest lacks valid authentication credentials. 34
Server ErrorINTERNAL13500 Internal Server ErrorInternal server error; some invariant expected by the system has been broken. 34
UNAVAILABLE14503 Service UnavailableThe service is currently unavailable (transient). 34
UNIMPLEMENTED12501 Not ImplementedOperation is not implemented or not supported. 34
Request CancelledCANCELLED1(No direct equivalent, client-side)Operation was cancelled, typically by the caller. 34
Timeout/DeadlineDEADLINE_EXCEEDED4408 Request Timeout, 504 Gateway TimeoutDeadline expired before operation could complete. 34
Resource LimitRESOURCE_EXHAUSTED8429 Too Many Requests, 507 Insufficient StorageA resource quota was exhausted or file system is full. 34
State IssueFAILED_PRECONDITION9412 Precondition Failed, 409 ConflictSystem not in a state required for the operation (e.g., deleting a non-empty directory). 34
ABORTED10409 ConflictOperation aborted due to concurrency issue (e.g., transaction abort). 34
Data IssueDATA_LOSS15(No direct equivalent, severe server error)Unrecoverable data loss or corruption. 34

III. Implementing Sylius-like/InvenTree-like APIs with gRPC

Translating the rich functionalities of e-commerce platforms like Sylius or inventory management systems like InvenTree into a gRPC-based API involves careful consideration of service definitions, message structures, and leveraging gRPC’s unique features like streaming.

A. Mapping E-commerce/Inventory Resources to gRPC Services

The first step in designing a gRPC API is to conceptualize the system’s resources and operations as services and RPC methods. Unlike REST, which is resource-oriented, gRPC is action-oriented.

Conceptualizing Resources as Services:

Core entities found in platforms like Sylius (e.g., Products, Taxons/Categories, Customers, Orders, Payments, Shipments, Inventory Sources 41) and InvenTree (e.g., Parts, Part Categories, Stock Items, Stock Locations, Suppliers, Purchase Orders, Sales Orders, Build Orders 43) would be mapped to distinct gRPC services. For example:

Defining Messages with Protocol Buffers:

The data structures associated with these services (requests and responses) are defined as messages in .proto files. This involves translating the fields and relationships of entities from Sylius or InvenTree into Protobuf message definitions.

Handling relationships between entities is achieved using nested messages or by including IDs that reference other messages (services). For example, an Order message might contain a customer_id field or a nested Customer message. Protobuf’s support for repeated fields allows for lists of sub-entities, like line items in an order.15 Primitive data types (string, int32, bool, float, etc.) and complex types like enums (for status fields) and nested messages are well-supported by Protobuf’s type system.15

The structured nature of models in platforms like Sylius (which uses Doctrine entities) and InvenTree (Django models) generally maps well to Protobuf definitions. The primary challenge and opportunity lie in designing idiomatic gRPC services that are not merely a one-to-one translation of existing REST endpoints but truly leverage the strengths of the RPC paradigm, such as more complex, business-process-oriented operations.

B. Leveraging gRPC Streaming for Real-Time E-commerce/Inventory Operations

gRPC’s native streaming capabilities offer significant advantages over traditional REST for implementing real-time features in e-commerce and inventory management systems.

These streaming capabilities enable more dynamic, responsive, and engaging user experiences compared to the traditional request-response limitations of REST.

C. Authentication and Authorization in a gRPC E-commerce Context

Securing gRPC APIs in an e-commerce or inventory context is paramount. gRPC provides mechanisms for both authentication (verifying identity) and authorization (determining access rights).

Implementing robust authentication and authorization is critical. gRPC’s support for TLS and metadata-based tokens, combined with server-side interceptors for enforcing RBAC, provides a solid foundation for securing APIs in e-commerce and inventory management systems.

When migrating from REST to gRPC, a frequent pitfall is to simply translate RESTful resource-oriented endpoints (e.g., GET /products/{id}, PUT /products/{id}) into equivalent RPC methods (e.g., GetProduct(id), UpdateProduct(product_data)). While feasible, this approach may not fully harness gRPC’s strengths. A more effective strategy involves redesigning the API around actions and business capabilities, which aligns more naturally with the RPC paradigm.1 For instance, instead of separate calls to check inventory, create an order, and process payment, a single RPC like ProcessNewOrder(OrderRequest) could encapsulate this entire business transaction, feeling more atomic from the client’s perspective. This implies that designing a gRPC API for systems like Sylius or InvenTree should involve a degree of rethinking the API surface to leverage RPC semantics, rather than a direct translation of existing REST endpoints.

The definition of Protobuf messages also requires careful consideration of granularity. If messages are too fine-grained, it can lead to chatty APIs requiring multiple calls to assemble a complete view of data. Conversely, if messages are too coarse (e.g., a Product message always including every conceivable detail like full descriptions, all variant information, all images, and all reviews), fetching lists of such objects can become inefficient due to over-fetching, an issue also present in REST if not managed. Since gRPC does not offer a client-driven field selection mechanism like GraphQL, API designers must carefully craft different message types (e.g., ProductSummary, ProductDetail) or distinct RPC methods for various data views to balance message completeness with transmission efficiency.

For systems managing inventory across multiple locations, such as those supported by Sylius Plus 56 or complex InvenTree deployments, gRPC’s streaming capabilities offer a powerful mechanism for maintaining data consistency. Server-side or bidirectional streaming can be used to propagate inventory changes in real-time to various clients (e.g., point-of-sale systems, other warehouse interfaces, e-commerce frontends). This proactive push model is significantly more efficient than traditional REST-based polling, reduces the likelihood of clients working with stale data, and is crucial for preventing overselling or displaying incorrect product availability, thereby enhancing operational accuracy.

Table 3: Mapping Sylius/InvenTree Resources to gRPC Services (Conceptual)

Resource (Sylius/InvenTree)Potential gRPC Service NameExample Unary RPCsExample Streaming RPCsKey Protobuf Message(s)
Product / PartProductServiceGetProduct(GetProductRequest) returns (Product) <br> ListProducts(ListProductsRequest) returns (ListProductsResponse) <br> CreateProduct(CreateProductRequest) returns (Product) <br> UpdateProduct(UpdateProductRequest) returns (Product)StreamProductUpdates(ProductSubscriptionRequest) returns (stream Product)Product, ProductVariant, ProductAttribute, ProductTranslation, GetProductRequest, ListProductsRequest, CreateProductRequest, UpdateProductRequest, ProductSubscriptionRequest
Category / TaxonCategoryServiceGetCategory(GetCategoryRequest) returns (Category) <br> ListCategories(ListCategoriesRequest) returns (ListCategoriesResponse)Category, CategoryTranslation, GetCategoryRequest, ListCategoriesRequest
CustomerCustomerServiceGetCustomer(GetCustomerRequest) returns (Customer) <br> CreateCustomer(CreateCustomerRequest) returns (Customer)Customer, Address, GetCustomerRequest, CreateCustomerRequest
Order (Sales/Purchase)OrderServiceCreateOrder(CreateOrderRequest) returns (Order) <br> GetOrder(GetOrderRequest) returns (Order) <br> UpdateOrderStatus(UpdateOrderStatusRequest) returns (Order)TrackOrder(TrackOrderRequest) returns (stream OrderStatusUpdate)Order, OrderItem, ShipmentInfo, PaymentInfo, Adjustment, CreateOrderRequest, GetOrderRequest, UpdateOrderStatusRequest, TrackOrderRequest, OrderStatusUpdate
Inventory / Stock ItemInventoryServiceGetStockItem(GetStockItemRequest) returns (StockItem) <br> AdjustStock(AdjustStockRequest) returns (StockItem) <br> ListStockItems(ListStockRequest) returns (ListStockResponse)StreamStockUpdates(StockUpdateRequest) returns (stream StockItem) <br> BulkUpdateStock(stream StockItem) returns (BulkUpdateResponse)StockItem, StockLocation, BatchInfo, SerialNumber, GetStockItemRequest, AdjustStockRequest, ListStockRequest, StockUpdateRequest, BulkUpdateResponse
Build Order (InvenTree)BuildServiceCreateBuildOrder(CreateBuildOrderRequest) returns (BuildOrder) <br> GetBuildOrder(GetBuildOrderRequest) returns (BuildOrder) <br> AllocateStockToBuild(AllocateStockRequest) returns (BuildOrder)StreamBuildProgress(BuildOrderRequest) returns (stream BuildProgressUpdate)BuildOrder, BuildLine, BuildOutput, CreateBuildOrderRequest, GetBuildOrderRequest, AllocateStockRequest, BuildProgressUpdate

Note: This table is conceptual and intended to illustrate how resources might be mapped. Actual service and message definitions would require detailed design based on specific platform functionalities from Sylius 41 and InvenTree 43, and gRPC best practices.3

IV. Potential Pitfalls and Challenges in Choosing gRPC

While gRPC offers compelling advantages in performance and streaming, its adoption is not without challenges. Understanding these potential pitfalls is crucial for making an informed decision.

A. Browser Support and gRPC-Web

One of the most significant challenges for gRPC in applications with web frontends is its limited direct browser support. Modern browsers do not provide the low-level control over HTTP/2 frames that native gRPC clients require.1 To bridge this gap, gRPC-Web was developed. gRPC-Web allows browser-based JavaScript clients to communicate with gRPC services, but it typically requires an intermediary proxy (such as Envoy or Nginx with a gRPC-Web module).3 This proxy translates gRPC-Web requests (which are often sent over HTTP/1.1 and have a specific framing) into native gRPC (HTTP/2) requests that the backend service can understand, and translates responses back.
This proxy layer, while functional, introduces additional complexity to the deployment architecture and can become another point of failure or a source of latency, potentially offsetting some of gRPC’s directness benefits for web clients.39 Furthermore, not all gRPC features, such as full bidirectional streaming or client-side streaming in certain configurations, might be perfectly supported or as performant through all gRPC-Web implementations and proxies. For e-commerce platforms like Sylius or inventory systems like InvenTree, where web-based user interfaces are common for customers and administrators, this is a critical architectural consideration.

B. Network Infrastructure Complexity

The underlying technologies of gRPC, HTTP/2 and persistent connections, can introduce complexities in network infrastructure setup and management.

These operational aspects mean that running gRPC at scale can be more demanding than managing traditional REST APIs, requiring deeper expertise in HTTP/2 and binary protocols.

C. Learning Curve and Ecosystem

The transition to or adoption of gRPC can present a learning curve and ecosystem differences compared to the more established REST paradigm.

The investment in training development teams and potentially adopting new or specialized tooling needs to be factored into any decision to use gRPC.

D. Schema Evolution and Versioning

Protocol Buffers are designed to support schema evolution, allowing for changes to message definitions over time while maintaining a degree of compatibility.16 For example, adding new optional fields to a message or deprecating existing ones can often be done in a backward and forward-compatible manner, meaning old clients can still talk to new servers and new clients to old servers (ignoring the new/deprecated fields).

However, managing breaking changes—such as changing the data type of an existing field, renaming a field, or removing a required field—still requires careful API versioning strategies, much like with REST APIs. Common approaches include introducing new versions of a service (e.g., ProductServiceV2), adding new RPC methods for modified functionality, or versioning at the package level within .proto files. While Protobuf’s field numbering system helps in handling non-breaking changes smoothly, a disciplined approach to API versioning is essential when incompatible changes are necessary.

E. Limited Edge Caching

As detailed in Section II.E, standard HTTP edge caching solutions (CDNs, reverse proxies) are significantly less effective for gRPC APIs compared to REST APIs.14 This is primarily because gRPC typically uses HTTP POST for all operations, and binary payloads are opaque to generic caches. This can be a substantial drawback for public-facing APIs that serve a high volume of cacheable content, such as product catalogs or category listings in an e-commerce system. The inability to effectively leverage existing, widespread caching infrastructure might necessitate more complex application-level caching or the use of specialized gRPC-aware caching proxies, potentially diminishing some of the performance benefits for certain types of requests.

Many of the challenges associated with gRPC, such as direct browser support limitations, complex firewall traversal, and difficulties with standard edge caching, are more pronounced for APIs intended for external-facing consumption. For internal microservice communication, where the network environment is more controlled and clients are typically other services rather than web browsers, these issues are often less significant or can be more easily mitigated. Internal networks can be configured to fully support HTTP/2 without restrictive proxies, and caching strategies might lean more towards application-level or distributed caches (e.g., Redis, Memcached) rather than relying on HTTP edge caches. This distinction is crucial when evaluating gRPC for platforms like Sylius or InvenTree, which possess both internal business logic and external-facing API components.

The availability and maturity of development, debugging, and observability tools play a significant role in the ease of adopting and operating gRPC. While REST benefits from a vast and mature ecosystem of tools (e.g., Postman, Swagger/OpenAPI UI, numerous versatile HTTP client libraries, and well-understood browser developer tools), gRPC often requires more specialized utilities. For instance, inspecting binary Protobuf payloads necessitates tools like grpcurl or Wireshark with appropriate dissectors 32, and testing or exploratory API interaction may rely more heavily on generated client code if comprehensive GUI tools are not readily available or adopted. Although gRPC’s code generation capabilities are a strong point 3, the surrounding ecosystem for tasks like advanced mocking, contract testing, and interactive API exploration is still evolving relative to the REST ecosystem. Consequently, teams adopting gRPC must be prepared to invest in learning and integrating these gRPC-specific tools, which can act as a barrier or require a dedicated effort to overcome.

Furthermore, while gRPC is engineered for high performance, the associated architectural complexity can translate into increased operational overhead and demand more specialized expertise. A simple REST API can often be deployed using standard web servers and load balancers. In contrast, a gRPC service, particularly if it needs to be accessible to web clients, might necessitate a gRPC-Web proxy 39, an HTTP/2-aware load balancer 3, and potentially a service mesh for advanced features like traffic management, observability, and security. Debugging issues within such a distributed environment, especially with binary protocols, can be more intricate and time-consuming.32 Therefore, the total cost of ownership for a gRPC-based API solution might be higher in certain scenarios due to this operational complexity, even if the raw performance metrics for individual RPC calls are superior.

V. Migration Strategies and Case Studies

Migrating existing RESTful APIs, such as those potentially powering Sylius or InvenTree, to gRPC is a significant undertaking that requires careful planning and execution. A complete, abrupt switch is rarely feasible or advisable.

A. Approaches to Migrating RESTful APIs to gRPC

Several strategies can be employed to transition from REST to gRPC, often favoring gradual and incremental approaches:

A full migration is a complex project. Gateways and gradual approaches are generally preferred to minimize disruption, reduce risk, and allow teams to gain experience with gRPC incrementally.

B. Case Studies and Examples

While the concept of migrating to gRPC for performance benefits is widely discussed, detailed public case studies, especially in the e-commerce and inventory management domains, are somewhat limited in the provided materials.

The absence of detailed, domain-specific public migration stories is an important observation. It suggests that while gRPC is a strong candidate for new microservices within these domains, full-scale migrations of existing, large REST APIs might be less common, more recent, or less publicly documented.

C. Considerations for Sylius and InvenTree

Both Sylius and InvenTree are established platforms with existing RESTful APIs.

For both platforms, introducing gRPC would be a significant architectural addition rather than a simple replacement of their current REST-centric API approaches. The decision would need to weigh the performance benefits against the added complexity and development effort.

A key pattern enabling incremental adoption of gRPC in systems with existing REST APIs is the API Gateway.74 This pattern is crucial because it allows organizations to harness gRPC’s performance benefits for internal communications while maintaining REST compatibility for external or legacy clients. A gateway can receive external REST requests and translate them to internal gRPC calls, and vice-versa. This approach de-risks the migration process, allowing for a phased rollout. Backend services can be refactored or newly developed using gRPC, while existing REST clients continue to function seamlessly by interacting with the gateway. This provides a practical evolutionary path, allowing platforms like Sylius or InvenTree to modernize their internal architecture without immediately breaking their established external API contracts.

The lack of comprehensive, end-to-end public case studies detailing the migration of large e-commerce or inventory management platforms from REST to gRPC—including specifics on challenges, engineering effort, and quantifiable outcomes—is a notable finding. While general benefits of gRPC are often cited 6, and Dropbox’s internal Courier migration is mentioned 6, there’s a scarcity of detailed narratives like “Major E-commerce Platform X migrated its Product API from REST to gRPC and achieved Y% latency reduction and Z% cost savings.” This suggests that such large-scale migrations in this specific domain might be less common, still in early phases, or not extensively documented publicly, which is a significant point for any team considering such a move.

It’s also critical to understand that “refactoring” from REST to gRPC is not a mere protocol switch. It often necessitates substantial redesign of service logic and data models to align with RPC principles and Protobuf’s strict schema requirements.76 REST APIs are typically resource-centric, whereas gRPC APIs are service and method-oriented.1 Data structures in JSON are flexible, while Protobuf messages are strongly typed and schema-defined.16 A naive approach of creating a gRPC method for every existing REST endpoint (e.g., GetProductById, UpdateProductById) might fail to leverage gRPC’s full potential, such as its streaming capabilities or the ability to define more complex, business-process-oriented operations. A proper migration involves rethinking service boundaries and operations, which is a more profound refactoring effort than simply altering the communication layer.

VI. Conclusion and Recommendations

The choice between gRPC and REST for API design, particularly in the context of e-commerce and inventory management systems like Sylius or InvenTree, is not straightforward and involves a careful evaluation of trade-offs. Both paradigms offer distinct advantages and come with their own set of challenges.

A. Summary of Trade-offs

B. When to Choose gRPC for E-commerce/Inventory Systems

gRPC is a strong contender in specific scenarios within e-commerce and inventory management:

C. When REST Remains a Stronger Choice

Despite gRPC’s advantages, REST continues to be a more suitable choice in other contexts:

D. Hybrid Approaches

A hybrid approach, leveraging the strengths of both gRPC and REST, is often the most pragmatic solution for complex systems like Sylius or InvenTree. This typically involves:

E. Final Recommendations for Sylius/InvenTree Contexts

For platforms like Sylius and InvenTree, the following recommendations emerge:

  1. Prioritize gRPC for New Internal Microservices: When developing new backend microservices or decomposing existing monoliths, gRPC should be strongly considered for inter-service communication to benefit from its performance and strong contracts.
  2. Leverage gRPC Streaming for Real-Time Features: For new or enhanced features requiring real-time data flow (e.g., live inventory updates across channels, real-time order status notifications, collaborative tools), gRPC’s streaming capabilities offer a more efficient and integrated solution than REST-based polling or WebSockets alone.
  3. Approach Migration of Existing REST APIs Cautiously: For existing, stable REST APIs that are widely consumed, a full migration to gRPC should only be undertaken if there’s a compelling performance bottleneck or a clear need for gRPC’s unique features that cannot be adequately addressed with REST.
  4. Utilize API Gateways for Incremental Adoption: If migrating parts of the backend to gRPC, implement an API Gateway that can expose a consistent REST interface to existing clients while routing requests to new internal gRPC services. This facilitates a gradual, less disruptive transition.
  5. Thoroughly Evaluate Impact: Before committing to widespread gRPC adoption, conduct thorough evaluations of its impact on:
    • Caching strategies: Especially for product catalogs and other frequently accessed, cacheable data.
    • Browser client integration: The need for gRPC-Web and proxy layers.
    • Developer workflow and tooling: The learning curve and integration with existing CI/CD and monitoring systems.
    • Third-party integrations: How external systems will interact with potentially new gRPC interfaces.

The decision between gRPC and REST is not a binary choice of one being universally “better” than the other. It is highly contextual and depends on specific project requirements, the nature of the API consumers (internal services versus public web clients), existing infrastructure, and team expertise. gRPC clearly excels in performance for internal, high-throughput, low-latency scenarios, particularly where its native streaming capabilities can be leveraged.4 Conversely, REST maintains its strengths in simplicity, broad compatibility, and its suitability for public APIs.1 E-commerce and inventory platforms like Sylius and InvenTree have diverse API needs, encompassing internal processing logic, external APIs for storefronts and mobile applications, and integrations with third-party services. Consequently, a blanket recommendation for one protocol over the other is ill-advised; a hybrid strategy often represents the most pragmatic path forward.

For established systems such as Sylius or InvenTree, which already possess mature REST APIs and ecosystems built around them, the API Gateway pattern that supports transcoding between REST and gRPC emerges as a critical enabling technology.74 Such a gateway allows for the incremental adoption of gRPC for backend services. This means internal service communication can be refactored to benefit from gRPC’s performance advantages, while existing external REST clients continue to operate without disruption by interacting with the gateway. This provides a practical route for architectural evolution rather than a high-risk “big bang” migration.

Ultimately, the decision to adopt gRPC should be driven by a clear, demonstrable business value derived from its specific advantages—such as extreme low latency, high throughput, or native streaming capabilities—that cannot be adequately or efficiently met by REST or its common extensions. gRPC introduces complexities in areas like tooling, debugging, and network configuration.14 These complexities must be justified by tangible benefits. If an existing REST API adequately meets performance and functional requirements, the cost-benefit analysis of a migration to gRPC might not be favorable. However, for new features demanding real-time data streams (e.g., live multi-channel inventory dashboards, collaborative editing of product specifications) or where internal service communication is a proven performance bottleneck, gRPC’s strengths become compelling. The choice must be strategic, based on specific needs and a clear understanding of the trade-offs, rather than solely on the novelty of the technology or isolated benchmark figures.

Works cited

  1. REST API vs. gRPC API : What’s the difference? - Document360, accessed May 10, 2025, https://document360.com/blog/grpc-vs-rest/
  2. What Is the Difference Between gRPC and REST? - FS.com, accessed May 10, 2025, https://www.fs.com/blog/what-is-the-difference-between-grpc-and-rest-2735.html
  3. Understanding gRPC Concepts, Use Cases & Best Practices, accessed May 10, 2025, https://www.infracloud.io/blogs/understanding-grpc-concepts-best-practices/
  4. gRPC vs. REST: Key Similarities and Differences - DreamFactory Blog, accessed May 10, 2025, https://blog.dreamfactory.com/grpc-vs-rest-how-does-grpc-compare-with-traditional-rest-apis
  5. Overview for gRPC on .NET | Microsoft Learn, accessed May 10, 2025, https://learn.microsoft.com/en-us/aspnet/core/grpc/?view=aspnetcore-9.0
  6. gRPC - Wikipedia, accessed May 10, 2025, https://en.wikipedia.org/wiki/GRPC
  7. gRPC vs REST - ByteSizeGo, accessed May 10, 2025, https://www.bytesizego.com/blog/grpc-vs-rest
  8. gRPC vs REST - Difference Between Application Designs - AWS, accessed May 10, 2025, https://aws.amazon.com/compare/the-difference-between-grpc-and-rest/
  9. What is a gRPC API and how does it work? - Mulesoft, accessed May 10, 2025, https://www.mulesoft.com/api-university/what-grpc-api-and-how-does-it-work
  10. gRPC: Main Concepts, Pros and Cons, Use Cases - AltexSoft, accessed May 10, 2025, https://www.altexsoft.com/blog/what-is-grpc/
  11. HTTP/1 vs HTTP/2 What is the Difference? - Wallarm, accessed May 10, 2025, https://www.wallarm.com/what/what-is-http-2-and-how-is-it-different-from-http-1
  12. HTTP 1 vs. HTTP 1.1 vs. HTTP 2: A Detailed Analysis - DZone, accessed May 10, 2025, https://dzone.com/articles/http-1-vs-http-11-vs-http-2-a-detailed-analysis/?utm_source=ajtdigitally_jarvisnews&utm_medium=website&utm_campaign=ajtd_content_curation
  13. gRPC vs REST: Choosing the best API design approach - LogRocket Blog, accessed May 10, 2025, https://blog.logrocket.com/grpc-vs-rest/
  14. What is gRPC? Meaning, Architecture, Advantages - Wallarm, accessed May 10, 2025, https://www.wallarm.com/what/the-concept-of-grpc
  15. Work with protocol buffers in GoogleSQL | Spanner - Google Cloud, accessed May 10, 2025, https://cloud.google.com/spanner/docs/reference/standard-sql/protocol-buffers
  16. What Is Protobuf? | Postman Blog, accessed May 10, 2025, https://blog.postman.com/what-is-protobuf/
  17. Protobuf vs JSON: Performance, Efficiency, and API Optimization, accessed May 10, 2025, https://www.getambassador.io/blog/protobuf-vs-json
  18. Protobuf vs JSON Comparison - Wallarm, accessed May 10, 2025, https://lab.wallarm.com/what/protobuf-vs-json/
  19. gRPC vs. REST: A Comparative Guide | Keploy Blog, accessed May 10, 2025, https://keploy.io/blog/community/grpc-vs-rest-a-comparative-guide
  20. REST or gRPC? A Guide to Efficient API Design | Zuplo Blog, accessed May 10, 2025, https://zuplo.com/blog/2025/03/24/rest-or-grpc-guide?utm_source=hnblogs.substack.com
  21. gRPC vs HTTP vs REST: Which is Right for Your Application? - Last9, accessed May 10, 2025, https://last9.io/blog/grpc-vs-http-vs-rest/
  22. www.diva-portal.org, accessed May 10, 2025, http://www.diva-portal.org/smash/get/diva2:1887929/FULLTEXT01.pdf
  23. gRPC in Go: Streaming RPCs, Interceptors, and Metadata - VictoriaMetrics, accessed May 10, 2025, https://victoriametrics.com/blog/go-grpc-basic-streaming-interceptor/
  24. How to build a streaming API using gRPC | MuleSoft, accessed May 10, 2025, https://www.mulesoft.com/api-university/how-to-build-streaming-api-using-grpc
  25. gRPC Streaming: Best Practices and Performance Insights - DEV Community, accessed May 10, 2025, https://dev.to/ramonberrutti/grpc-streaming-best-practices-and-performance-insights-219g
  26. Build a highly scalable streaming data API using gRPC - Redpanda, accessed May 10, 2025, https://www.redpanda.com/blog/build-streaming-data-api-grpc
  27. How to Test Performance of gRPC - PFLB, accessed May 10, 2025, https://pflb.us/blog/how-to-test-performance-of-grpc/
  28. gRPC Bidirectional RPC - Tutorialspoint, accessed May 10, 2025, https://www.tutorialspoint.com/grpc/grpc_bidirectional_rpc.htm
  29. Streaming API with gRPC: A Comprehensive Guide - BytePlus, accessed May 10, 2025, https://www.byteplus.com/en/topic/41415
  30. GRPC for Model Serving: Business Advantage - NexaStack, accessed May 10, 2025, https://www.nexastack.ai/blog/grpc-model-serving-ai-inference
  31. The Battle of APIs: gRPC vs REST Explained - Arramton, accessed May 10, 2025, https://arramton.com/blogs/grpc-vs-rest
  32. Six Lessons from Production gRPC | SystemsDigest, accessed May 10, 2025, https://systemsdigest.com/posts/six-lessons-production-grpc
  33. gRPC status codes | YDB, accessed May 10, 2025, https://ydb.tech/docs/en/reference/ydb-sdk/grpc-status-codes
  34. Status Codes | gRPC, accessed May 10, 2025, https://grpc.io/docs/guides/status-codes/
  35. Google Distributed Cloud air-gapped Errors, accessed May 10, 2025, https://cloud.google.com/distributed-cloud/hosted/docs/latest/gdch/apis/errors
  36. Caching Best Practices in REST API Design - Speakeasy, accessed May 10, 2025, https://www.speakeasy.com/api-design/caching
  37. gRPC - Framework for Microservices Communication - Capital One, accessed May 10, 2025, https://www.capitalone.com/tech/software-engineering/grpc-framework-for-microservices-communication/
  38. Using gRPC with CloudFront distributions - Amazon CloudFront, accessed May 10, 2025, https://docs.aws.amazon.com/AmazonCloudFront/latest/DeveloperGuide/distribution-using-grpc.html
  39. Is gRPC Really Better for Microservices Than GraphQL? - WunderGraph, accessed May 10, 2025, https://wundergraph.com/blog/is-grpc-really-better-for-microservices-than-graphql
  40. Mastering gRPC Implementation for High Performance Microservices, accessed May 10, 2025, https://www.growingscrummasters.com/keywords/grpc/
  41. Resource Layer | Sylius - Sylius 2.0 Documentation, accessed May 9, 2025, https://docs.sylius.com/the-book/architecture/resource-layer
  42. API Platform - Sylius, accessed May 9, 2025, https://b2b-suite.demo.sylius.com/api/v2
  43. InvenTree API Schema - InvenTree Documentation, accessed May 9, 2025, https://docs.inventree.org/en/0.17.1/api/schema/
  44. General API Endpoints - InvenTree Documentation, accessed May 9, 2025, https://docs.inventree.org/en/0.17.1/api/schema/general/
  45. Customizing the new Sylius & ApiPlatform integration - Locastic, accessed May 9, 2025, https://locastic.com/blog/customizing-the-new-sylius-apiplatform-integration
  46. accessed December 31, 1969, https://raw.githubusercontent.com/Sylius/Sylius/1.12/src/Sylius/Bundle/ApiBundle/Resources/config/api_resources/Product.xml
  47. accessed December 31, 1969, https://raw.githubusercontent.com/Sylius/Sylius/1.12/src/Sylius/Bundle/ApiBundle/Resources/config/api_resources/Order.xml
  48. Parts and Part Categories - InvenTree Documentation, accessed May 9, 2025, https://docs.inventree.org/en/0.17.1/api/schema/part/
  49. Stock and Stock Locations - InvenTree Documentation, accessed May 9, 2025, https://docs.inventree.org/en/0.17.1/api/schema/stock/
  50. External Order Management - InvenTree Documentation, accessed May 9, 2025, https://docs.inventree.org/en/stable/api/schema/order/
  51. Build Orders - InvenTree Documentation, accessed May 9, 2025, https://docs.inventree.org/en/stable/build/build/
  52. Build Allocation - InvenTree Documentation, accessed May 9, 2025, https://docs.inventree.org/en/0.17.1/build/allocate/
  53. accessed December 31, 1969, https://docs.inventree.org/en/stable/api/schema/build/
  54. accessed December 31, 1969, https://docs.inventree.org/en/0.17.1/api/schema/build/
  55. accessed December 31, 1969, https://docs.inventree.org/en/0.17.1/api/schema/order/
  56. Multi-Source Inventory - Sylius 2.0 Documentation, accessed May 9, 2025, https://docs.sylius.com/the-book/products/multi-source-inventory
  57. What Is Data API? A Complete Guide to Data Integration - Acceldata, accessed May 10, 2025, https://www.acceldata.io/blog/what-is-data-api-a-complete-guide-to-data-integration
  58. Implementing Microservices for Real-Time Inventory Tracking in Global Supply Chains, accessed May 10, 2025, https://www.researchgate.net/publication/387822994_Implementing_Microservices_for_Real-Time_Inventory_Tracking_in_Global_Supply_Chains
  59. How to secure gRPC APIs: A full guide Escape Blog, accessed May 10, 2025, https://escape.tech/blog/how-to-secure-grpc-apis/
  60. How To Install Sylius and Fetch Access Token For API? - Webkul Blog, accessed May 9, 2025, https://webkul.com/blog/how-to-install-sylius-and-fetch-access-token-for-api/
  61. Using API | Sylius - Sylius 2.0 Documentation, accessed May 9, 2025, https://docs.sylius.com/getting-started-with-sylius/using-api
  62. Sylius - Storyblok, accessed May 9, 2025, https://www.storyblok.com/apps/storyblok-gmbh@sylius-fieldtypes
  63. User Management - InvenTree Documentation, accessed May 9, 2025, https://docs.inventree.org/en/0.17.1/api/schema/user/
  64. InvenTree API - Documentation, accessed May 9, 2025, https://docs.inventree.org/en/0.15.7/api/api/
  65. InvenTree API - Documentation, accessed May 9, 2025, https://docs.inventree.org/en/0.16.9/api/api/
  66. Privacy Statement - InvenTree Documentation, accessed May 9, 2025, https://docs.inventree.org/en/latest/app/privacy/
  67. Symfony Api platform how test permissions and roles with auth0, accessed May 9, 2025, https://community.auth0.com/t/symfony-api-platform-how-test-permissions-and-roles-with-auth0/183374
  68. AdminUser - Sylius 2.0 Documentation, accessed May 9, 2025, https://docs.sylius.com/the-book/customers/adminuser
  69. How to create user with ROLE_API_ACCESS in Sylius? - Stack Overflow, accessed May 9, 2025, https://stackoverflow.com/questions/42689751/how-to-create-user-with-role-api-access-in-sylius
  70. Role-Based Access Control Module - Sylius, accessed May 9, 2025, https://sylius.com/blog/sylius-plus-module-overview-advanced-role-based-access-control-module/
  71. gRPC on HTTP/2 Engineering a Robust, High-performance Protocol, accessed May 10, 2025, https://grpc.io/blog/grpc-on-http2/
  72. gRPC and Proxies | Palette, accessed May 10, 2025, https://docs.spectrocloud.com/architecture/grps-proxy/
  73. gRPC protocol | FortiWeb 7.6.2 - Fortinet Document Library, accessed May 10, 2025, https://docs.fortinet.com/document/fortiweb/7.6.2/administration-guide/797650/grpc-protocol
  74. Bridge the gap between gRPC and REST HTTP APIs | Google Cloud Blog, accessed May 10, 2025, https://cloud.google.com/blog/products/api-management/bridge-the-gap-between-grpc-and-rest-http-apis
  75. gRPC API Gateway: Bridging the Gap Between REST and gRPC | Zuplo Blog, accessed May 10, 2025, https://zuplo.com/blog/2025/04/09/grpc-api-gateway
  76. Using refactoring to migrate REST applications to gRPC, accessed May 10, 2025, https://www.researchgate.net/publication/360392975_Using_refactoring_to_migrate_REST_applications_to_gRPC
  77. Comparative Analysis OF GRPC VS. ZeroMQ for Fast Communication - ResearchGate, accessed May 10, 2025, https://www.researchgate.net/publication/389078536_Comparative_Analysis_OF_GRPC_VS_ZeroMQ_for_Fast_Communication
  78. Sylius - Open Source Headless eCommerce Platform, accessed May 9, 2025, https://sylius.com/
  79. Customizing API - Sylius 2.0 Documentation, accessed May 9, 2025, https://docs.sylius.com/the-customization-guide/customizing-api
  80. Developing Plugins - InvenTree Documentation, accessed May 9, 2025, https://docs.inventree.org/en/0.17.1/extend/how_to_plugin/
  81. URLs Mixin - InvenTree Documentation, accessed May 9, 2025, https://docs.inventree.org/en/0.17.1/extend/plugins/urls/
  82. Plugins - InvenTree Documentation, accessed May 9, 2025, https://docs.inventree.org/en/0.15.7/extend/plugins/
  83. User Interface Mixin - InvenTree Documentation, accessed May 9, 2025, https://docs.inventree.org/en/0.17.1/extend/plugins/ui/
  84. Schedule Mixin - InvenTree Documentation, accessed May 9, 2025, https://docs.inventree.org/en/stable/extend/plugins/api/
  85. InvenTree Documentation, accessed May 9, 2025, https://docs.inventree.org/en/latest/
  86. Schedule Mixin - InvenTree Documentation, accessed May 9, 2025, https://docs.inventree.org/en/latest/extend/plugins/api/
  87. accessed December 31, 1969, https://docs.inventree.org/en/stable/extend/plugins/integration/
  88. accessed December 31, 1969, https://docs.inventree.org/en/stable/extend/plugins/api_extend/