The Tool Desk
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You can switch an application between large-language-model APIs without rewriting the whole product by treating the model provider as an interchangeable boundary, not as the application’s internal data model. Put provider-specific client setup, request conversion, response parsing, streaming events, and error mapping behind an interface you own. Then test the behaviors your application actually depends on—structured output, tools, history, multimodal inputs, usage reporting, and failures—against the exact provider, model, endpoint, and adapter combination.
An OpenAI-compatible endpoint or third-party adapter can reduce mechanical changes, but compatibility is not behavioral equivalence. Native SDKs expose more provider features at the cost of more vendor-specific code. The safest migration is therefore selective: standardize the common path, preserve explicit escape hatches for native capabilities, and validate the real workload before rollout.
What does it mean to migrate between LLM APIs?
An LLM API migration changes the service that receives prompts and returns model output while the surrounding application remains in place. The change may involve a new REST endpoint, SDK, authentication scheme, model identifier, request format, response envelope, streaming protocol, tool-calling syntax, or state-management model.
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The difficult part is not replacing one URL. It is preserving the application contract. A customer-support app may depend on JSON that always matches a schema, a coding assistant may depend on tool-call arguments, and a chat product may depend on incremental stream events and conversation history. Two APIs can accept similar messages yet differ in validation, defaults, token accounting, finish reasons, safety responses, or support for images, audio, video, and files.
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Google’s integration guidance describes a feature ceiling for its OpenAI-compatibility layer, Microsoft’s provider documentation shows that capabilities vary by provider and API surface, and Hugging Face documents a compatible endpoint limited to chat completions. Those examples illustrate why “compatible” should be treated as a scoped claim rather than a promise that every workload will behave identically.
How do I inventory the contract before changing providers?
Start with an evidence-based inventory of what the running application sends, receives, stores, and assumes. This prevents a migration from passing a simple text test while breaking production features.
Record the transport and authentication details
- List every endpoint, HTTP method, region or deployment, timeout, retry policy, and proxy.
- Record API-key headers, bearer-token handling, workload identity, key rotation, and any organization or project headers.
- Capture SDK and API versions, pinned package versions, model identifiers, and environment-specific overrides.
- Note whether the provider retains conversation state, files, cached context, uploaded tools, or other server-side resources.
Capture request and response shapes
- Document message roles, content-part types, system instructions, metadata, temperature or sampling controls, token limits, stop conditions, and safety settings.
- Record response text paths, finish or stop reasons, refusal fields, citations, log-probability data, usage counters, and request IDs.
- For structured output, save the exact JSON schema, grammar, response-format flag, and the code that validates or repairs the result.
- For tools, record declarations, tool-choice behavior, parallel-call settings, argument encoding, result messages, and the loop that sends tool results back.
List application behaviors, not just fields
Mark which user-visible behaviors must survive the migration:
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- Conversation history, truncation, summarization, and replay.
- JSON or schema-constrained output.
- Tool calls, multiple calls in one turn, and tool errors.
- Images, audio, video, files, embeddings, moderation, or other model services.
- Usage reporting, quotas, billing labels, tracing, and audit records.
- Timeouts, rate limits, safety refusals, partial responses, and retryable versus permanent errors.
This inventory becomes the migration’s acceptance contract. If a capability is not required by the application, do not spend engineering time forcing it into a universal abstraction. If it is required, test it explicitly.
What boundary should I put between my application and an LLM provider?
Use the smallest application-owned boundary that hides unstable provider details without erasing features you need. In practice, this is usually a client interface plus provider-specific adapters, rather than a giant “universal prompt” object.
Keep these responsibilities behind the boundary
- Client construction: endpoint selection, credentials, regional deployment, SDK initialization, and default headers.
- Request conversion: mapping your internal messages, tools, schemas, media references, and generation settings to the provider format.
- Response parsing: extracting text, structured data, tool calls, finish reasons, usage, citations, and provider metadata.
- Streaming normalization: converting provider-specific chunks or events into your own event types, such as text delta, tool call delta, usage update, completed, and failed.
- Error mapping: translating authentication, validation, rate-limit, timeout, safety, overload, and transient network errors into stable application categories.
- Observability: attaching provider, model, deployment, request ID, latency, token counts, and retry information to your existing logs and traces.
Define an internal contract that is honest
An internal interface might expose a request containing messages, optional tools, an optional output schema, generation controls, and media parts. Its result can contain text, structured data, tool calls, usage, finish status, and provider metadata. Add capability flags or a negotiated feature set rather than silently accepting every option for every backend.
For example, an adapter can return supports_parallel_tool_calls: false and let the application choose a serial strategy. It is safer to reject an unsupported requirement with a clear error than to drop a tool declaration or schema constraint and produce an apparently successful but incorrect answer.
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When is a gateway worth adding?
A gateway or inference aggregator is useful when routing, centralized authentication, policy enforcement, quotas, provider selection, or one observability plane matters. It becomes another operational boundary, however: confirm how it translates requests, exposes stream events, reports usage, preserves provider errors, and handles provider-specific options.
Azure API Management documents both compatible APIs and passthrough APIs for non-compatible endpoints. That distinction is useful architecturally: a gateway can present a common surface where translation is reliable while allowing passthrough access for features that do not map cleanly.
Should I use an OpenAI-compatible API or a provider’s native SDK?
Choose the compatibility path when your actual workload fits its documented surface and code reuse is more valuable than native features. Choose a native SDK or REST/gRPC API when you need provider-specific capabilities, newer API features, or precise control over request behavior.
| Approach | Best fit | Benefits | Trade-offs to verify |
|---|---|---|---|
| Provider-native SDK or API | Workloads requiring current provider features or custom request behavior | Broadest access to that provider’s models and controls; native error and lifecycle support | More vendor-specific code, more migration work, and separate validation for each provider |
| OpenAI-compatible endpoint | A workflow covered by the compatibility surface, usually conventional chat completion | Reuse of familiar client code and a smaller initial code diff | Feature ceiling, different defaults and semantics, and possible gaps for tools, media, schemas, usage, or streaming |
| Third-party adapter or library | Applications needing several providers or library-managed routing | Shared abstractions and provider coverage without building every adapter yourself | Another compatibility layer, dependency updates, and provider-specific behavior to validate |
| Gateway or inference aggregator | Central routing, policy, quotas, or API management | One control plane for credentials, routing, and observability | Translation limits, added latency or failure modes, service policy constraints, and operational ownership |
Google presents compatibility and direct integration as trade-offs rather than a universally superior choice. OpenAI Agents SDK documentation describes its Any-LLM and LiteLLM integrations as best-effort beta and advises validating the exact backend for capabilities your application relies on. Treat those statements as engineering requirements: test the combination you will deploy, not merely the adapter’s feature list.
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Separate portable concepts from provider extensions. Portable concepts include a sequence of messages, text output, a normalized completion status, and basic usage fields. Extensions carry features such as provider-specific caching, reasoning controls, safety configuration, server-side conversations, or proprietary media processing.
Use a capability matrix
Maintain a versioned matrix for each provider-model-endpoint combination. Include support for:
- Text and multimodal input types.
- Structured output and the exact schema constraints.
- Tool calling, parallel calls, tool choice, and streaming tool arguments.
- Streaming event types and whether usage arrives in the final event.
- Conversation state, files, caching, and context-window behavior.
- Usage counters, cost labels, request IDs, and safety or refusal signals.
- Authentication methods, regional availability, rate limits, and model lifecycle status.
Mark each cell as supported, unsupported, conditionally supported, or not stated in the provider documentation. “Not stated” should trigger a test or a provider question; it should not be interpreted as support.
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Expose escape hatches deliberately
Allow an adapter-specific options object or a typed provider extension for features with no portable equivalent. Keep that code at the boundary and label it with the provider and API version. Do not put provider response objects throughout business logic, because that makes the next migration a second rewrite.
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Conversely, do not force a native feature into a misleading common field. If one provider offers server-managed conversation IDs and another requires the application to resend history, represent those as different state strategies behind the same application service rather than pretending the IDs are interchangeable.
How do I migrate prompts, structured output, and conversation history?
Move behavior in layers, starting with deterministic text requests and then adding features that change control flow.
Prompts and message roles
Store prompts as application-owned templates with explicit variables and a canonical internal message format. During conversion, map system, developer, user, assistant, and tool roles according to the target API’s rules. Check whether the provider merges instructions, treats multiple system messages differently, or requires media as separate content parts.
Structured output
Keep the schema in source control and test both valid and adversarial responses. Verify whether the target enforces the schema at generation time, merely requests JSON, or returns text that your application must parse. Test missing fields, extra properties, numbers represented as strings, refusal responses, and truncated output. Never classify a response as successful solely because it is valid JSON; validate the business schema and the model’s finish status.
Conversation state
Decide whether your application or the provider owns history. Application-owned history is more portable but requires token budgeting, truncation, summarization, and secure storage. Provider-managed state can reduce payload size but may not transfer between vendors, regions, models, or API generations. Include state migration or replay in your rollout plan if existing users have long-running conversations.
How do I preserve tool calls and streaming?
Tool calls and streaming are common migration breakpoints because they are protocols, not just fields in a response.
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Tool-call loop
- Convert your internal tool definitions to the target’s declaration format, including names, descriptions, and parameter schemas.
- Send a representative request that should call one tool, then one that should call two tools if your application supports parallel execution.
- Normalize the returned tool name and arguments. Test whether arguments arrive as one JSON value, incremental fragments, or provider-specific content parts.
- Execute the tool under your existing authorization and timeout rules; never grant a new provider adapter broader tool permissions.
- Convert the tool result back into the target’s required message or event format and continue the model turn.
- Test malformed arguments, unknown tool names, tool timeouts, duplicate calls, and a model response that declines to call a tool.
Streaming event handling
Define your own event taxonomy and translate each provider’s stream into it. At minimum, handle text deltas, tool-call deltas, completion, usage, and error events. Test empty chunks, reordered metadata, a stream that ends without a final usage event, disconnects after partial text, and retries that could duplicate visible output. If the target uses server-sent events, WebSockets, or a different framing protocol, keep that transport detail inside the adapter.
How should errors, retries, and usage be normalized?
Normalize error meaning, not merely HTTP status codes. A 429 can mean a short rate limit, a quota exhaustion, or a deployment capacity problem; those cases need different retry and user messaging.
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- Authentication and authorization: fail fast, alert operators, and do not retry unchanged credentials.
- Validation: return a developer-visible error containing the rejected field and provider request ID.
- Rate limit or overload: apply bounded exponential backoff with jitter, honor retry-after information, and cap attempts.
- Timeout or network failure: retry only when the request is safe to repeat; streaming retries require an application-level resume or restart policy.
- Safety refusal or policy block: preserve the refusal category and avoid treating it as an infrastructure outage.
- Partial or malformed output: record the raw provider status securely, mark the result incomplete, and invoke a controlled repair or fallback path.
Usage fields also need a mapping policy. Keep prompt, completion, cached, and total counts separate when available, attach the model and deployment to the record, and mark missing values as unknown rather than zero. Compare billing reports independently; the reviewed documentation does not establish a universal cost winner between providers or adapters.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What tests prove that the migration preserved behavior?
Use a fixture-based test suite built from real, sanitized workloads. A single “hello world” request cannot reveal differences in tools, schemas, safety handling, or streaming.
Build representative fixtures
- Short and long conversations near the context limit.
- Prompts requiring a strict schema, including invalid and refusal cases.
- Single and parallel tool calls with realistic arguments and results.
- Text-only and each media type your product supports.
- Requests that trigger rate limits, timeouts, retries, and cancellation.
- Streaming and non-streaming versions of the same task.
Compare invariants, not exact wording
Model output can vary legitimately across providers. Assert the properties that matter: schema validity, required fields, allowed tool names, argument types, citation or source structure when applicable, completion status, maximum latency, stream event order, and correct handling of errors. Use human review or task-specific evaluators for answer quality rather than declaring one provider superior from a few examples.
Test the deployed combination
Run tests against the exact provider, model identifier, endpoint, region, SDK version, adapter version, and gateway configuration you will release. The adapter documentation may describe broad support while a particular model or deployment lacks a capability. Keep these tests in continuous integration and rerun them when any of those variables changes.
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Provider APIs evolve even when the vendor remains the same. Google’s Gemini API documentation says the Interactions API became the default interface in June 2026 and labels generateContent legacy. That kind of change can affect request construction, state handling, and SDK defaults without any change to your application’s product requirements.
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- Pin SDK and adapter versions, then review release notes before upgrading.
- Verify model availability, retirement dates, regional support, quotas, and deployment names.
- Check authentication requirements and secret scopes in every environment.
- Confirm that logging and tracing do not capture sensitive prompts, files, or tool results.
- Run a shadow or canary route where outputs are evaluated before switching all traffic.
- Keep the previous provider available for a defined rollback window, with idempotency and conversation-state rules documented.
- Record the API version and capability matrix used for each production request so an incident can be reproduced.
Which migration approach fits my application?
Use this decision sequence rather than choosing a tool by popularity.
- Need a provider-native feature? Start with the native SDK or API and isolate it behind your boundary.
- Only need a documented chat workflow? An OpenAI-compatible endpoint may minimize code changes; verify tools, schemas, media, streaming, and usage before committing.
- Need several providers or centralized routing? Evaluate an adapter library or gateway, including its support status, translation behavior, and operational ownership.
- Need both portability and advanced features? Keep a portable core plus explicit provider extensions, and route requests by capability.
- Cannot test the exact backend? Delay the migration or reduce scope; an unverified compatibility claim is not a production plan.
The goal is not to make APIs indistinguishable. It is to make the provider-specific portion small, visible, testable, and replaceable while preserving the behaviors your users and operations depend on.
Frequently Asked Questions
Can I keep the same SDK client and only change the base URL?
Only for the subset of requests the target explicitly supports. Check authentication headers, model naming, response envelopes, streaming frames, tool and schema fields, and error formats before treating a base-URL change as sufficient.
How do I migrate existing conversations between providers?
Export your application’s canonical messages, attachments, tool results, and metadata rather than relying on provider conversation IDs. Re-tokenize or summarize history for the target context limits, and mark any provider-only state that cannot be reproduced.
Should I route every request to the cheapest model after migration?
No. First establish behavioral and operational equivalence for each workload class. Select models using measured quality, latency, context capacity, safety behavior, and workload-specific cost data; the available documentation does not identify a universal winner.
How can I prevent an adapter upgrade from silently changing output?
Pin the adapter and SDK versions, keep golden fixtures, and run contract tests on every upgrade. Alert on changes to schema validity, tool-call rates, refusal categories, usage fields, latency, and stream-event handling.
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