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You can make provider changes without rewriting every model call by putting a stable boundary between your application and the AI service. That boundary can be an adapter or multi-provider SDK inside your app, or a gateway that exposes a shared endpoint. Neither option guarantees that providers behave identically: test the exact features your app uses before switching traffic.

Choose where the provider boundary belongs

The main decision is whether provider-specific details should be handled inside each application or at a shared service. The right choice depends on your language and stack, required model features, and whether your team wants to operate a separate gateway.

Approach Where the boundary lives When it fits Trade-offs to assess
Application adapter or multi-provider SDK Inside the application process, behind an interface you control You want explicit control in application code and do not need a shared service Dependency and upgrade work; how well the abstraction covers your required features; provider configuration and secret management
API gateway At a shared endpoint between your application and model providers Multiple applications or teams benefit from centralized configuration, routing, credentials, or operational controls An additional service to operate; gateway and provider compatibility; request logging and data handling; failure behavior and latency

LiteLLM documents both a Python SDK and a self-hosted proxy. Its documentation also shows an OpenAI-compatible client configured to use the proxy’s base URL, which can let an existing client send requests through that endpoint rather than changing every call site. This only avoids call-site changes if the gateway and destination provider support the requests and responses your application needs. See LiteLLM’s documentation.

Keep the contract as small as your app needs

Define an application-owned interface around the operations your product actually uses. For example, if the application needs text generation and streaming, those are part of its contract; every vendor-specific option does not need to be exposed throughout the codebase. Keep a deliberate escape hatch for provider-specific capabilities that are genuinely necessary, so adopting a shared interface does not conceal important differences.

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What a common API does—and does not—make portable

A common request format can reduce integration work, but it does not establish feature parity. Providers and models can differ in supported operations, options, output shape, limits, and error behavior. LiteLLM’s provider documentation lists many provider families and separately documents provider integrations; a provider appearing on a list is not proof that your specific model and feature combination is supported. Check the current documentation for the source, gateway, and destination provider. See LiteLLM’s provider list.

Inventory the behavior your application depends on, including:

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  • Message or prompt format and expected response shape
  • Streaming and how partial results are delivered
  • Tool or function use and structured-output requirements
  • Images or other multimodal inputs
  • Embeddings, if the application uses them
  • Provider-specific options, token accounting, and error handling

Use the source provider’s API reference to understand the API you are leaving, then inspect the destination provider’s own current API documentation. One provider’s reference cannot establish what another provider supports. The OpenAI API reference describes OpenAI’s API surface, not compatibility with a destination service.

Move provider-specific settings out of call sites

Keep provider and model identifiers, endpoint details, and credentials in configuration rather than scattering them across application logic. That makes a configuration change easier to review and limits how much code is coupled to a particular provider. Never put secrets in client-side code or logs; follow the security guidance for the provider and, if used, the gateway.

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LiteLLM describes provider calls through its Python SDK as well as a self-hosted proxy, so it is one candidate for either boundary. Its vendor documentation also describes streaming, normalized provider errors, callbacks, cost tracking, and gateway controls. Treat these as vendor-described capabilities, and verify that the current product, provider, model, and operation meet your requirements. See LiteLLM’s documentation and its provider documentation.

Compare candidate libraries and gateways against your workload

LiteLLM

Consider its SDK if you want an in-process integration, or its proxy if you want a shared endpoint. LiteLLM says its interface covers 100+ providers and describes gateway features such as virtual keys and budgets; these are vendor-published product claims, not independent verification of coverage or suitability. Confirm the exact integration and feature support you need in the current documentation. See LiteLLM’s main documentation, its provider list, and its proxy and SDK documentation.

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Vercel AI SDK

The official documentation has a “Providers and Models” section describing its provider/model foundation. The material cited here does not establish feature parity, migration guarantees, or a complete current list of supported integrations. If your application is already in its framework ecosystem, check the current integration documentation for both the provider you use and the one you plan to adopt. See Vercel AI SDK documentation.

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Switch in stages and validate real application behavior

Before broad rollout, compare representative requests from your own workload. This is a practical migration method, not a reported benchmark: vendor documentation about normalization, provider diversity, and cost tracking does not prove that a particular destination will preserve your app’s behavior.

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  1. Inventory current calls. Record each model operation and the prompt or message shape, streaming, tools, structured output, multimodal inputs, provider-specific options, token accounting, and error handling it uses.
  2. Write the application contract. Specify the inputs, outputs, and behaviors the rest of your application relies on. Identify any provider-specific capability that must remain available through a controlled escape hatch.
  3. Select the boundary. Use an in-process adapter or SDK when you want integration in application code. Consider a gateway when a centrally operated endpoint, configuration, or routing layer is useful; include its operations and failure modes in your decision.
  4. Configure the destination. Isolate provider and model identifiers, endpoint information, and credentials. Follow the relevant provider and gateway security guidance.
  5. Run representative comparisons. Check output correctness and shape, streaming, tool and structured-output behavior, error mapping, limits, and cost for tasks representative of the application. Do not treat a shared API format as evidence that results or behavior will match.
  6. Roll out deliberately. Start with a limited evaluation or traffic slice, monitor application-level outcomes and provider errors, and keep a practical route back to the previous configuration.

Assess operational requirements separately

Product documentation can describe features, but it cannot establish that a particular deployment meets your security, reliability, performance, or compliance requirements. Evaluate those requirements against the actual setup—including the gateway, provider, logging, and data-handling path—before relying on it in production.

For any candidate, verify the current provider, model, and API operation you intend to call. Coverage and capabilities can change, so a product’s provider list or general description is a starting point for that check, not a substitute for it.

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