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REST, GraphQL, OData, and Falcor describe different ways to structure an API—not four interchangeable protocols. REST is an architectural style; GraphQL is a schema-based query language and execution model; OData standardizes conventions for REST-based data services; and Falcor exposes application data as a path-addressable JSON Graph. The right choice depends on the contract your clients need, how related data is accessed, the standards you must follow, and the service’s operational requirements.
What each API approach actually defines
REST: architectural constraints, not a JSON format
REST is an architectural style defined by Roy T. Fielding in his dissertation, Architectural Styles and the Design of Network-based Software Architectures. Fielding describes REST as a set of constraints that, when applied together, emphasize scalability of component interactions, general interfaces, independent deployment, and intermediaries that can reduce latency, enforce security, or encapsulate legacy systems. This is an architectural rationale, not a measured performance claim.
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An API using HTTP and returning JSON is not automatically REST. The relevant question is whether its design follows REST’s constraints as a whole, rather than whether it uses a familiar transport and data format.
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GraphQL: a typed schema clients query
The September 2025 GraphQL specification defines GraphQL as a query language and execution engine for describing and performing data-model capabilities and requirements in client-server applications. A GraphQL service publishes a schema that describes the types and fields clients may request; requests are validated and executed against that schema.
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Clients select fields, including nested fields on related objects, and the response follows the requested selection shape. The GraphQL queries guide describes query, mutation, and subscription operation types. A schema must support queries, while mutations and subscriptions are optional capabilities, not features guaranteed by every GraphQL service.
Client-selected fields can make it easier to request related data through one operation, but they do not eliminate the service owner’s work. Schema governance, authorization, query-cost controls, and resolver behavior still need deliberate design.
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OData: standardized conventions for REST-based services
OData, or the Open Data Protocol, is a standardized approach for REST-based data services. The official OData documentation identifies version 4.01 materials for the protocol, URL conventions, JSON representation, and Common Schema Language, and says OData has been standardized by OASIS and approved as an ISO/IEC International Standard.
OData therefore does not replace REST with an unrelated architectural style. It supplies protocol and data-service conventions that can support common ways to query, represent, and model services. Check the applicable version and OASIS or ISO/IEC publication when implementation or procurement requirements depend on a specific standard.
Falcor: paths into a virtual JSON Graph
Falcor is a Netflix-documented JavaScript library and data-access approach. It represents application-domain data as a JSON Graph, a JSON convention that can express relationships using references. Its abstract operations are get, set, and call; clients request subsets of the virtual graph through paths. Netflix documents the JSON Graph model and its data sources.
A Falcor Router matches requested paths and can follow graph references to retrieve related values within a request. Its documentation presents the Router as an abstraction over a service layer or REST API. Falcor is described as middleware for communication between application layers, not as a replacement for an application server, database, or MVC framework. See Netflix’s pages on Falcor Router and what Falcor is.
How request and response shape differs
| Approach | Client request model | How related data is reached | What defines the contract |
|---|---|---|---|
| REST | Interactions organized around resources and the service’s interface; response shape depends on the service design. | May use links or service-specific patterns; traversal and request behavior depend on the implementation. | Architectural constraints applied as a whole, rather than a single wire-format specification. |
| GraphQL | A query selects fields from a schema; response data follows the selection. | Nested field selections can traverse related objects. | A published schema and GraphQL’s query and execution model. |
| OData | Requests follow standardized data-service and URL conventions; representation depends on the service and version. | Available conventions and behavior depend on the service’s model and implementation. | The OData protocol and related conventions, including documented version 4.01 materials. |
| Falcor | Operations request paths into a virtual JSON Graph. | Graph references can be followed by the Router to retrieve related values. | The JSON Graph model and its get, set, and call operations. |
Where the options differ in practice
Who controls the response
GraphQL lets a client specify fields in a query. Falcor lets a client ask for paths in a graph. With REST and OData, interactions are commonly organized around resources and service conventions; the exact response shape depends on the API design and, for OData, its representation and version.
How much standardization you need
OData offers a formal protocol and URL-convention suite, with its documentation identifying OASIS and ISO/IEC standardization. GraphQL has a published specification. REST is an architectural style, not one wire-format specification. The Falcor materials cited here are project documentation describing its model and library; they do not establish the project’s current maintenance status or present-day production use.
What the sources do—and do not—establish about performance
GraphQL field selection and Falcor path requests can give clients more direct control over which data they ask for. That describes request behavior, not a guarantee of lower latency, fewer total requests, or lower operating cost. The cited materials do not provide a named, directly comparable performance study for these four approaches. Any performance decision should be tested against the application’s workload and implementation.
How to choose for your application
- Choose REST constraints when a resource-oriented interface and the full REST architectural style fit your system. Evaluate the actual design against the constraints; HTTP endpoints alone do not establish that it is REST.
- Choose GraphQL when clients need schema-governed field selection across related data, and your team is prepared to govern schema changes, authorization, query cost, and resolver behavior.
- Choose OData when standardized conventions for REST-based data services fit your interoperability requirements. Confirm the version and standard relevant to your implementation.
- Choose Falcor when a path-oriented JSON Graph model fits the application and its tooling, and the documented library approach suits your service boundaries.
Before deciding, check which clients must use the API, how identity and authorization will work, what you need to observe and cache, how related data is exposed, and what expertise and long-term support your team can provide. These are engineering decision criteria, not evidence that one approach is universally superior.
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