Integrate a hosted AI API by putting a server-side adapter between your application and the provider: define the task and data rules, choose an endpoint whose interaction style matches the experience, keep credentials off clients, validate every response, and add bounded retries, rate-limit handling, logging and monitoring before launch.
The same architecture works for OpenAI, Google Gemini, Anthropic and other providers. Keep provider-specific request construction behind a small interface so you can evaluate or replace a model without rewriting your product.
1. Start with the interaction your feature needs
Choose the API surface from the user experience, not from a model name. A request/response call is appropriate when the application can wait for one completed result. Streaming sends partial output as it is generated, which is useful for chat replies or long documents. Realtime interfaces maintain an ongoing, low-latency session for experiences such as voice.
Conventional request and response
Use a normal call for classification, extraction, summarization, background jobs and other tasks where your server can wait, apply validation, then return a finished result.
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Streaming output
Use streaming when showing progressive text improves perceived latency. Your backend should relay chunks to the client, detect a terminated or failed stream, and only mark the message complete after the provider’s final event has been received and validated.
Realtime sessions
Use a realtime surface for continuous interaction such as voice or an always-open assistant. Plan for session expiry, reconnects, interruption, event ordering and higher operational complexity. OpenAI documents Responses and Realtime surfaces; Google’s reference lists Interactions, content generation, streaming and the Live API. Names and capabilities change, so verify the selected endpoint in the OpenAI API overview and Gemini API reference.
2. Define the task before selecting a provider
Write a short contract for the feature. It should state:
- Allowed input types, maximum size and whether files, images or audio are accepted.
- The output contract: plain text, a fixed JSON shape, tool calls or another representation.
- Quality criteria and representative examples, including cases that must be refused or escalated.
- Latency target, expected traffic, peak concurrency and whether work is interactive or asynchronous.
- Data-handling requirements, such as personal-data minimization, retention, residency or contractual restrictions.
- Failure behavior: what the user sees when the provider times out, declines a request or is over quota.
Build a small evaluation set from real tasks and failure cases. Compare candidate models on that set rather than relying on general rankings. Record quality, latency, error rate and token or media usage under a load representative of your application.
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3. Compare provider surfaces and operating constraints
There is no evidence here for a universal fastest, cheapest or most accurate provider. Compare the exact model, account, region and workload you intend to run.
| Provider | Documented surfaces or capabilities | Implementation facts to verify |
|---|---|---|
| OpenAI | API surfaces including Responses and Realtime, with official client libraries or direct HTTP. | Selected endpoint’s modalities, structured-output and tool behavior, account limits, data terms and model lifecycle. See the API overview. |
| Google Gemini | Interactions (described by Google as its standard primitive), generateContent, streamGenerateContent, Live API, batch generation, embeddings and media APIs. | SDK or REST support for the chosen surface, project and model quotas, key type and restrictions, billing requirements and regional terms. See the API reference and quickstart. |
| Anthropic Claude | API references, SDKs and capabilities including text, code and vision. | Current model status, endpoint behavior, quotas, privacy terms and migration path. Anthropic separates active, legacy, deprecated and retired models in its model lifecycle guidance. |
For each candidate, assess interaction shape and modality, language and SDK coverage, authentication options, quota behavior, measured quality on your evaluation set, latency and reliability at expected load, total cost at your token or media volume, privacy and data-location terms, and migration burden.
4. Choose an official SDK or direct HTTP
Use an official SDK when it supports your language and the endpoint features you need. It usually handles request construction, authentication headers and response types more conveniently. Use direct HTTP when the SDK lacks a required surface, you need a thin dependency footprint, or you are building a provider adapter with your own transport layer.
| Decision factor | Official SDK | Direct HTTP |
|---|---|---|
| Endpoint coverage | Check that the SDK exposes the exact model, streaming or realtime operation. | Can call any documented operation your HTTP client can represent. |
| Authentication | Often reads a server environment variable and sets headers for you; confirm its behavior. | You explicitly set the provider’s required header or authorization scheme. |
| Streaming and events | Convenient typed iterators or event objects may be available. | You must parse chunks, events, disconnects and content types. |
| Maintenance | Upgrade the package and review release notes for behavior changes. | Maintain serialization, error parsing, retries and compatibility yourself. |
Google recommends its GenAI SDK for end-user applications while documenting SDK, direct-API and compatibility-layer trade-offs for ecosystem builders in its partner integration guide. The Gemini quickstart shows Python, JavaScript and REST access.
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5. Put the provider behind your backend
The browser or mobile app should call your application, never the provider with a permanent secret. Your backend authenticates the user, applies input and usage policies, calls the provider, validates the result and returns only what the client needs.
Recommended request path
- Client: sends an authenticated request to your feature endpoint.
- Application: checks user authorization, input size, allowed options and any per-user budget.
- Provider adapter: selects a configured model and constructs the provider-specific request.
- Provider: returns a response or stream.
- Application: validates content and structured fields, applies policy, records operational metadata and performs permitted actions.
- Client: receives a stable application response rather than provider-specific internals.
Protect API keys
Store secrets in server-side environment configuration or a secrets-management service, restrict them where the provider allows it, and rotate them if exposed. OpenAI’s documentation states: “Don’t share it with others or expose it in any client-side code such as browsers or apps.” Its API overview describes environment variables and key-management services as server-side approaches.
For Gemini, a key is sent in the x-goog-api-key header. Google’s key guide says that starting May 28, 2026, new AI Studio keys are automatically created as authorization keys; unrestricted standard keys are rejected, while standard keys with explicit restrictions continue to work. This is a dated policy, so check the current console and Gemini API key guidance before deployment.
- Never commit a key to source control, a client bundle, crash report or ticket.
- Use separate development, staging and production credentials.
- Grant the smallest available scope and restrict by project, API, network or application where supported.
- Redact secrets and unnecessary prompt content from logs.
- Revoke and replace a key immediately after suspected exposure.
6. Implement a small provider adapter
Keep the rest of your code independent of vendor request shapes. An adapter can expose operations such as generateAnswer(input, options), streamAnswer(input, options) and createRealtimeSession(options). Internally it maps those operations to the selected SDK or HTTP endpoint.
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async function generateAnswer(input, options) {
validateInput(input, options);
const request = buildProviderRequest(input, options);
const raw = await callProvider(request, { timeoutMs: 20_000 });
const result = validateProviderResponse(raw);
return applyApplicationPolicy(result);
}The method names above are illustrative. Use the selected provider’s current reference for authentication, request fields, streaming events, structured output and tool schemas. Keep model identifiers and other provider settings in configuration rather than scattering them through business logic.
Bound requests and responses
- Set maximum input characters, tokens, files, image dimensions or audio duration before sending data.
- Set an output limit appropriate to the feature; do not allow an unbounded completion.
- Use an explicit timeout and cancel work that the user no longer needs.
- Specify a structured response format when the endpoint supports it, then parse and validate it as untrusted input.
- Pass only the context required for the task and remove secrets or unrelated personal data.
7. Handle errors, rate limits and retries deliberately
Limits are multidimensional and account-specific. Depending on provider, model and tier, they can count requests, tokens, images or audio over minute or daily windows. OpenAI documents requests per minute or day, tokens per minute or day, images per minute and audio minutes per minute in its rate-limit guide. Gemini limits include requests per minute, input tokens per minute and requests per day, applied per project and varying by model and tier; Google directs developers to AI Studio for active values in its rate-limit documentation.
- Read the provider’s response and headers for retryability, request ID and reset information.
- Bound concurrency with a queue or semaphore so bursts do not exhaust the project quota.
- Retry only transient failures, such as an eligible rate-limit or service-unavailable response.
- Use exponential backoff with jitter and a maximum attempt count or deadline.
- Return a clear, non-sensitive error to the client and offer a fallback or later retry for asynchronous work.
Official SDKs may already retry eligible 429 and 503 responses. If your application adds retries, count both layers; otherwise a single user request can multiply into several provider calls and worsen an outage.
| Failure | Application response |
|---|---|
| Invalid input or authentication | Fix the request or configuration; do not retry unchanged. |
| Rate limit or quota exhaustion | Throttle, honor reset guidance, queue when appropriate and surface a controlled busy response. |
| Timeout or transient 5xx | Retry within a deadline with backoff, then fail gracefully. |
| Malformed model output | Reject it, optionally make one bounded repair attempt, and never execute an unvalidated action. |
8. Treat model output as untrusted input
Generated text can be wrong, incomplete or adversarially influenced. Validate required fields, types, lengths, enums and cross-field rules before data reaches the rest of your system. If the response proposes an action, enforce authorization using your normal application identity and policy checks; a model must not grant permissions.
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Tool and action boundaries
- Expose only narrowly scoped tools, with allow-listed operations and argument validation.
- Require confirmation for destructive, financial or externally visible actions when appropriate.
- Use server-side authorization and transaction checks immediately before execution.
- Record the user, tool, arguments after redaction, result and approval path for auditability.
Provider-specific structured-output guarantees and tool behavior differ by endpoint. Confirm the selected surface’s current documentation rather than assuming that a schema prevents every invalid or unsafe value.
9. Decide whether you need a gateway
A gateway can centralize authentication, usage tracking, budgets, rate limits, audit logs and model routing across teams or providers. It also adds another security and reliability boundary: evaluate its access controls, data handling, availability, latency and failure modes. Anthropic’s gateway guide describes these functions and explicitly says Anthropic does not endorse, maintain or audit the third-party proxy discussed there.
For a single small service, a well-tested adapter may be simpler. For many applications or providers, a gateway can standardize policy if its operational cost and additional failure path are acceptable.
10. Monitor the integration in production
Measure request count, latency by endpoint and percentile, timeout and error rates, token or media usage, retry counts, queue depth and quota headroom. Log provider request IDs and model configuration for diagnosis, while excluding unnecessary prompt, response and personal-data content.
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Plan for model and SDK changes
Keep model IDs, API versions, prompts and provider settings configurable. Subscribe to lifecycle notices and test replacements against your evaluation set before switching traffic. Anthropic documents active, legacy, deprecated and retired states and says publicly released model retirements receive at least 60 days’ notice; do not assume another provider follows the same policy. Review the Anthropic deprecation guidance and your chosen provider’s notices.
Quick Recap
11. Pre-launch checklist
- The feature has a written input, output, quality, latency and data-handling contract.
- The selected endpoint supports the required modality and interaction style.
- A representative evaluation set covers normal, boundary and failure cases.
- Clients call your backend; no provider secret is present in shipped code.
- Credentials are restricted, separated by environment and rotatable.
- Input and output bounds, timeouts and cancellation are implemented.
- Concurrency, rate-limit responses and retry multiplication are controlled.
- Structured responses and tool arguments are validated before use.
- Authorization is enforced independently of model output.
- Request IDs, latency, errors, usage and quota signals are observable without logging sensitive content.
- Model, SDK and provider-policy changes have an evaluation and rollback path.
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