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How to Set Up an AI Gateway for OpenAI, Anthropic, and Gemini APIs

By Freedom251 Team · · 13 min read

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Use an AI gateway as a private, authenticated proxy between your applications and OpenAI, Anthropic, and Gemini. The gateway owns provider credentials, exposes stable internal model aliases, enforces tenant quotas and token budgets, applies routing and bounded fallbacks, and emits redacted usage telemetry. Applications receive gateway-issued credentials instead of upstream API keys.

For a small team, a self-hosted LiteLLM service is a practical starting point because it provides a unified OpenAI-compatible interface, virtual keys, provider configuration, and routing. Organizations already operating Kong can use Kong AI Gateway to add AI routing to their existing authentication, rate-limiting, security, and observability policies. Whichever product you choose, treat compatibility as a policy decision: a common chat request is portable, while tools, multimodal inputs, streaming events, safety controls, and response metadata still differ by provider.

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What an AI gateway does

An AI gateway is a controlled proxy layer between an application or SDK and one or more model providers. Anthropic describes this pattern as a centralized proxy between Claude Code and model providers. In practical terms, the gateway terminates the client connection, authenticates the caller, resolves an internal model alias, applies policy, calls the selected provider, and returns a normalized or provider-native response.

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This boundary matters because it separates application identity from provider identity. A mobile app, internal service, or customer integration never needs to receive an OpenAI, Anthropic, or Google credential. You can revoke a client without rotating every provider key, and you can rotate an upstream key without changing every application deployment.

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Responsibilities that belong at the gateway

  • Authentication and tenant identity: validate a gateway key or workload identity and associate the request with a user, project, environment, and tenant.
  • Authorization: decide which aliases, providers, regions, modalities, and token limits the caller may use.
  • Model resolution: map a stable name such as quality-chat to a provider model ID and capability record.
  • Traffic policy: enforce request quotas, token budgets, concurrency caps, timeouts, retries, circuit breakers, and fallback rules.
  • Observability: record provider, model, status, latency, time-to-first-token, token counts, retries, fallbacks, and estimated cost without retaining sensitive prompt content by default.
  • Governance: preserve an audit trail of policy decisions, gateway version, region, and the model actually used.

Reference architecture

Application and SDKs
        |
        | gateway-issued key (never an upstream provider key)
        v
Private AI gateway
  - authentication and tenant identity
  - model alias map
  - routing, retries, bounded fallbacks
  - rate and token budgets
  - redacted logs and metrics
        |
        +--> OpenAI API
        +--> Anthropic API
        +--> Gemini API or Gemini OpenAI-compatible endpoint

Place the gateway on a private network or behind an identity-aware reverse proxy with HTTPS. Restrict outbound traffic to the provider endpoints that the service actually uses. Applications should reach only the gateway address, while the gateway is the only component allowed to reach provider APIs.

Choose an implementation approach

Option Best fit Strengths Trade-offs
LiteLLM self-hosted Teams wanting a fast, code-first multi-provider proxy Unified OpenAI-compatible interface, virtual keys, provider breadth, routing and gateway controls Your team operates deployment, upgrades, security hardening, and compatibility testing
Kong AI Gateway Organizations already standardized on Kong Gateway or Konnect AI routing integrated with existing authentication, rate limiting, security, policy, and observability controls Greater platform complexity; licensing and program terms must be verified for your organization
Custom gateway Narrow workloads with unusual compliance or protocol requirements Complete control over request shape, data handling, and policy decisions Highest maintenance burden as provider APIs, models, and streaming protocols change
Direct provider SDKs plus a thin internal router Applications that need provider-native features first Maximum feature fidelity for each provider More client integration work and less uniform enforcement of shared policy

Do not select a gateway solely because it advertises a common endpoint. Confirm that it can expose virtual credentials, tenant-level quotas, model routing, redacted telemetry, provider-specific escape hatches, and the API versions you intend to pin. A gateway that normalizes requests but cannot preserve required tool or multimodal behavior can create a more difficult production problem than separate SDK integrations.

Prepare the trust boundary before deployment

Separate environments and identities

Create distinct staging and production provider accounts or key sets. Issue separate gateway credentials for people, services, and environments, with the narrowest model and quota permissions each requires. Keep a record of who owns each credential, when it was created, and the procedure for revocation.

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Protect upstream credentials

Store provider keys in a secret manager or inject them as runtime secrets. They must not be committed to source control, embedded in browser or mobile code, returned in gateway responses, or written to ordinary request logs. Rotate and revoke upstream keys independently from gateway-issued client credentials.

Define network controls

Allow inbound traffic only from your applications, identity-aware proxy, or private network. Permit outbound connections only to the OpenAI, Anthropic, and Gemini endpoints required by your configured routes. Validate provider TLS certificates and ensure that a routing mistake cannot send traffic to an unapproved host.

Set up the gateway step by step

1. Select the gateway boundary

  1. Choose a private deployment location close to the applications that generate most traffic.
  2. For a small team, deploy LiteLLM as a private service and place it behind HTTPS, an identity-aware reverse proxy, or a private network boundary.
  3. If your organization already operates Kong, evaluate Kong AI Gateway so AI routes inherit the gateway policies, authentication, and observability controls you already administer.
  4. Define one public or private gateway address for clients. Do not expose provider endpoints as an alternate path unless that bypass is an intentional, separately governed integration.

2. Create a model catalog and aliases

Give applications stable names rather than provider-specific IDs. Typical aliases include fast-chat, quality-chat, vision, and embeddings. For every alias, record the provider model IDs that can serve it and the capabilities that determine eligibility:

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  • Context-window size and maximum input and output tokens
  • Text, image, audio, or other supported modalities
  • Tool calling and the exact tool schema accepted
  • Structured-output behavior and schema constraints
  • Streaming protocol and event format
  • Provider region, data-retention behavior, and safety controls
  • Current provider pricing data used for internal cost estimates

Keep the alias stable when you change providers. Review mapping changes as configuration, test them against the contract suite, and document any semantic difference. A name such as quality-chat promises a capability class, not that every underlying model produces identical wording or tool behavior.

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3. Configure provider credentials

  1. Create one credential set per provider and environment.
  2. Inject those credentials into the gateway runtime through your secret manager.
  3. Associate each credential with only the provider routes that need it.
  4. Verify that a client request containing an upstream-looking key is rejected or ignored rather than forwarded as authority.
  5. Test rotation by replacing a staging key, confirming traffic continues, and revoking the old key.

4. Issue gateway credentials to clients

Create gateway-issued keys or workload identities for each service, user group, and environment. Attach tenant, project, alias, region, and quota permissions to the identity. A client should be able to call the gateway with its own credential, but it should never be able to discover or retrieve the provider key used on the server side.

5. Normalize only the common request subset

An OpenAI-shaped chat request is a useful common denominator for basic text generation. Normalize the fields that have equivalent meaning across the providers, then preserve provider-specific fields instead of silently dropping them. Message roles, tool calls, streaming events, multimodal parts, caching controls, safety settings, and response metadata are not interchangeable.

Expose a native-provider escape hatch in the gateway API or route advanced requests through a provider adapter. Make the selected path explicit in telemetry so an operator can tell whether a request used the normalized contract or a native feature.

6. Add deterministic routing first

  1. Route each alias to one primary provider and model while you establish baseline error, latency, quality, and cost measurements.
  2. Only after those metrics are available, add weighted routing or priority tiers.
  3. For every fallback, declare the required modality, tool support, structured-output behavior, context size, region, safety policy, and retention requirements.
  4. Reject fallback when the candidate cannot satisfy those requirements; a successful HTTP response from an incompatible model is still a failed request.

7. Bound retries and failover

Retry rate-limit responses and transient server errors with bounded exponential backoff and a maximum attempt count. Do not retry malformed requests, authentication failures, or content-policy decisions. Use a circuit breaker to stop sending traffic to a provider that is consistently unhealthy, and include the breaker state in metrics.

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Kong documents routing requests across multiple LLM providers with failover and load balancing, while LiteLLM documents model routing and gateway controls. Those features implement mechanics; your team must define when a retry or fallback is safe and verify that the alternate model is semantically suitable.

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8. Enforce budgets before the upstream call

Apply per-tenant request limits, token budgets, maximum input and output tokens, and concurrency caps before spending a provider request. Return a clear gateway error when a quota is exceeded. Keep separate limits for interactive traffic, batch work, and administrative tests so a single workload cannot consume the entire allocation.

Design a portable model contract

Document the fields that every alias accepts and the fields that require a native route. A useful contract specifies timeout behavior, whether streaming is available, how usage is reported, the shape of tool calls, and which errors are retryable.

Request or response feature Portable gateway treatment When to use a native route
Plain text chat Normalize messages, model alias, temperature or equivalent controls, token limits, and streaming choice where the providers expose compatible behavior When a provider-specific generation control materially changes output or safety behavior
Tool calls Accept a common schema only after contract tests confirm argument and event mapping When tool definitions, parallel calls, or streaming events differ
Vision and multimodal parts Route only to aliases whose capability record explicitly includes the required modality When content-part encoding, file references, or limits differ
Structured output Advertise support per alias and validate the returned object against the requested schema When provider schema constraints or refusal behavior are not equivalent
Provider-native grounding, caching, or files Do not silently translate or discard the feature Use the provider adapter or direct native API

Use Gemini through the gateway deliberately

OpenAI-compatible Gemini endpoint

Google documents an OpenAI-compatible Gemini endpoint for clients that already use the OpenAI SDK. The Python integration changes three values: the API key, the base URL, and the Gemini model name. The documented base URL is https://generativelanguage.googleapis.com/v1beta/openai/.

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from openai import OpenAI

client = OpenAI(
    api_key='your-gemini-api-key',
    base_url='https://generativelanguage.googleapis.com/v1beta/openai/'
)

response = client.chat.completions.create(
    model='your-gemini-model-id',
    messages=[{'role': 'user', 'content': 'Explain how an AI gateway works.'}]
)
print(response.choices[0].message.content)

In a gateway deployment, the application should send the request to your gateway address with a gateway-issued key. The gateway’s Gemini adapter supplies the Google credential and endpoint. Keep the model ID in the alias configuration rather than allowing arbitrary client-selected provider IDs.

Know the compatibility boundary

Google warns that the compatibility layer has limitations for advanced Gemini features and that schema translation adds complexity. Use the native Google GenAI SDK when you need Gemini-specific tools, grounding, File API behavior, or other provider-native capabilities. Google states that the Google GenAI SDK reached general availability in May 2025 and is the recommended SDK for supported languages.

Do not claim that an OpenAI-shaped request gives complete Gemini feature parity. Test each combination of model, modality, tool, streaming mode, and structured-output requirement before assigning it to a portable alias.

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Pin the Gemini API version

Google defines v1 as stable and v1beta as actively developed; SDKs default to v1beta unless configured otherwise. Pin the API version used by production routes and monitor model deprecation notices. Google’s current Interactions API documentation says that API became generally available in June 2026 and is recommended for new Gemini projects, while generateContent remains supported. Verify feature parity before moving existing traffic to Interactions or changing the adapter behind an established alias.

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Observability without collecting sensitive prompts

Instrument the gateway at the point where it receives the client request and again when the provider response completes. Record:

  • Correlation ID, tenant ID, environment, gateway version, provider, model, alias, and region
  • Request count, status code, timeout, retry count, fallback count, and circuit-breaker state
  • Total latency and time-to-first-token for streaming responses
  • Input and output token counts, where the provider reports them
  • Estimated cost based on the configured provider and model rate data
  • Policy decisions such as quota denial, unauthorized alias, or disallowed modality

Disable prompt and response body storage by default. If debugging requires content capture, redact secrets and personal data, restrict access, set a short retention period, and record the approval and deletion process. Metrics should remain useful even when message bodies are unavailable.

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Security and governance checklist

  • Every gateway request is authenticated; anonymous pass-through is disabled.
  • Authorization checks tenant, project, environment, alias, modality, and token limits before provider execution.
  • Provider keys exist only in gateway runtime secrets and are never returned to clients.
  • Input and output token ceilings prevent unexpectedly large requests.
  • Outbound network rules and certificate validation restrict the gateway to approved provider endpoints.
  • Logs redact API keys, credentials, prompts containing secrets, and personal data.
  • Retention and deletion rules cover logs, metrics, traces, and any approved debug captures.
  • Audit records include provider, model, region, policy decision, and gateway version.
  • Staging and production credentials, projects, and quotas are separate.
  • Fallback candidates are reviewed for data residency, retention, safety, and tool behavior.

Test every route before production

Build a provider matrix rather than testing only one successful chat. For each alias and provider combination, run contract tests against pinned API versions.

Test area What to verify Expected evidence
Authentication Valid, expired, revoked, and cross-tenant gateway credentials Authorized calls succeed; invalid identities receive a gateway error without an upstream request
Alias resolution Known alias, unknown alias, disabled model, and provider-specific model ID supplied by a client Only approved aliases resolve; arbitrary provider IDs cannot bypass policy
Text generation Short and long prompts, maximum token limits, empty or malformed messages Correct normalization, bounded errors, and accurate usage accounting
Streaming First-token timing, disconnects, event ordering, and provider stream errors Client receives a valid stream or a clear terminal error; partial output is classified correctly
Tools and structured output Single and multiple tool calls, invalid arguments, schema refusals, and fallback eligibility Mappings preserve required fields and incompatible fallbacks are blocked
Vision and other modalities Supported and unsupported content parts for every alias Capability checks occur before upstream spend
Resilience 429, transient 5xx, timeout, authentication failure, and content-policy response Only retryable conditions retry; fallback and circuit-breaker rules are observable
Governance Quota exhaustion, concurrency cap, log redaction, correlation IDs, and spend alerts Limits apply before the provider call and telemetry contains no prohibited content

Repeat the suite whenever a gateway version, provider adapter, model mapping, or pinned provider API version changes. Keep a small set of representative prompts and tool schemas for regression testing, but do not store production personal data in the test corpus.

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Common failure modes and fixes

A provider key appears in application logs

Revoke the exposed key, rotate it in the gateway secret store, and inspect build artifacts, traces, crash reports, and client telemetry for additional copies. Add log filters and secret-scanning checks, then confirm that applications use only gateway credentials.

A fallback returns a response with the wrong capability

Inspect the alias capability record and fallback eligibility rule. Add explicit checks for modality, tools, structured output, context size, region, safety policy, and retention before a fallback is selected. If the requirement cannot be met, fail clearly instead of silently downgrading.

Retries multiply cost or latency

Check whether non-transient errors are being retried and whether each attempt has a bounded timeout. Restrict retries to 429 and transient 5xx responses, use exponential backoff with a hard attempt limit, and emit retry-count metrics. A circuit breaker should remove a persistently unhealthy provider from rotation.

Gemini requests work in simple chat but fail with tools or files

This usually indicates a compatibility-layer boundary rather than an authentication problem. Route the feature through the native Google GenAI SDK or a provider-specific adapter, and keep the portable alias limited to the tested common subset.

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Usage totals do not match provider billing

Compare the gateway’s token fields with the provider’s usage definition, account for retries and fallbacks, and label estimated cost as an internal estimate. Ensure every attempt, not only the final response, carries the same correlation and tenant identifiers.

A model change breaks clients despite an unchanged alias

Review the capability and contract tests for the alias. Stable naming does not guarantee identical semantics; a provider swap can change tool behavior, refusals, context limits, or streaming events. Roll back the mapping, add a compatibility note, or create a new alias when the contract has materially changed.

Operate and evolve the gateway

Change management

Treat provider model IDs, API versions, adapter versions, routing weights, quotas, and fallback order as reviewed configuration. Pin versions in production, stage changes with the contract matrix, and record the effective time and owner for every mapping change.

Capacity and cost control

Use request, token, concurrency, latency, and error metrics to size the gateway. Alert on spend anomalies, rising 429 rates, provider-specific failures, and unusual fallback volume. Budget policies should distinguish an expected temporary surge from a runaway client or routing loop.

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Provider deprecations

Track provider notices and test replacement models before changing an alias. Run old and new routes in staging, compare required capabilities and safety behavior, and migrate in a controlled window. Keep a rollback mapping until production traffic confirms the replacement.

A practical rollout plan

  1. Start with one alias and one workload. Choose a basic text-chat route and establish authentication, quotas, logging, and usage accounting.
  2. Add the second provider. Keep deterministic primary routing and prove that the normalized request contract is actually equivalent for the workload.
  3. Introduce Gemini deliberately. Use the OpenAI-compatible endpoint for tested basic calls, then add native routes for tools, grounding, files, or other advanced features.
  4. Expand the catalog. Add vision, embeddings, or quality tiers only after capability records and contract tests exist.
  5. Enable resilience policies. Add bounded retries, then carefully reviewed fallbacks and circuit breakers based on observed metrics.
  6. Audit and rehearse. Practice key rotation, quota exhaustion, provider outage, rollback, log-redaction verification, and tenant off-boarding.

The result is a controlled multi-provider surface: clients depend on stable aliases and gateway credentials, while operators retain authority over keys, routing, budgets, versions, and data handling. Keep the common API intentionally small, make provider-native behavior explicit, and let tests—not assumptions about compatibility—decide which models can serve each route.

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AI gatewaysAnthropic APIAPI securityGemini APIKongLiteLLMLLM routingOpenAI API

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