How do I centralize all my app’s AI API calls in one gateway?

To centralize all AI API calls in one gateway, you want a single AI proxy / orchestration layer between your app and every model provider. Your app talks only to your gateway, and the gateway handles routing, auth, logging, retries, model selection, and provider-specific quirks.

1) Put a gateway in front of every AI provider

Instead of calling OpenAI, Anthropic, Gemini, etc. directly from your app:

• Frontend / backend app → calls your AI gateway
• AI gateway → calls the actual provider APIs

This gives you one place to manage:

• API keys and secrets
• request/response normalization
• provider failover
• rate limiting
• audit logs
• caching
• moderation / policy checks
• usage and cost tracking

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2) Use a common internal request format

Define one internal schema for all AI calls, for example:

{
  "task": "chat",
  "model": "auto",
  "messages": [
    {"role": "system", "content": "You are helpful."},
    {"role": "user", "content": "Write a short email."}
  ],
  "temperature": 0.7,
  "max_tokens": 300,
  "metadata": {
    "tenant_id": "abc123",
    "request_id": "req_789"
  }
}

Your gateway converts this into the specific format each provider expects.

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3) Build the gateway responsibilities

A good AI gateway usually handles these layers:

Core request handling

• Accept requests from your app
• Validate input
• Select a provider/model
• Forward the request
• Normalize the response

Cross-cutting concerns

• Authentication and authorization
• Tenant/project separation
• Rate limiting and quotas
• Observability: logs, metrics, traces
• Error handling and retries
• Circuit breakers / fallback providers

Governance and safety

• Prompt filtering
• PII redaction
• Policy enforcement
• Content moderation
• Prompt/version management

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4) Decide routing logic

Your gateway should know how to choose a provider. Common strategies:

• Static routing: “Chat goes to OpenAI, embeddings go to provider X”
• Model-based routing: choose by model field
• Cost-aware routing: cheapest model that satisfies task
• Latency-aware routing: fastest healthy provider
• Fallback routing: if primary fails, retry secondary
• Tenant-based routing: different customers use different providers

Example routing rule:

• task=embedding → OpenAI embeddings
• task=chat and tier=free → cheaper model
• task=chat and tier=pro → premium model

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5) Normalize responses

Different providers return different shapes. Your gateway should translate them into one consistent response structure:

{
  "id": "resp_123",
  "model": "gpt-4.1",
  "output": "Here is your email draft...",
  "usage": {
    "input_tokens": 120,
    "output_tokens": 80,
    "total_tokens": 200
  },
  "provider": "openai",
  "latency_ms": 842
}

If you support streaming, normalize streaming events too.

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6) Add logging and tracing from day one

Centralization is most valuable when you can inspect usage and debug issues.

Log:

• request ID
• tenant/user ID
• provider/model
• token usage
• latency
• success/failure
• retry count

Be careful not to log secrets or sensitive content unless explicitly required and protected.

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7) Secure secrets in one place

Never put provider API keys in client apps.

Use:

• environment variables
• secret manager (AWS Secrets Manager, GCP Secret Manager, Vault, etc.)
• per-provider credential store
• key rotation

Your app should only have credentials for your gateway.

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8) Support fallback and retry policies

Typical policy:

• Retry transient failures with exponential backoff
• If provider A fails, try provider B
• If request exceeds latency budget, cancel or downgrade model
• Avoid retrying non-retriable errors like invalid auth or bad input

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9) Consider an existing gateway product vs building your own

Build your own if:

• You need full control
• You have custom policies or routing
• You want deep internal observability

Use a managed/open-source gateway if:

• You want to move quickly
• You need multi-provider support out of the box

Examples in the market include AI proxy/gateway tools and LLM orchestration layers. The best choice depends on your stack and governance needs.

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10) A practical architecture

A simple production setup:

App

• calls /ai/chat, /ai/embed, /ai/moderate

AI Gateway

• auth
• validation
• policy checks
• router
• provider adapters
• logging/metrics
• retries/fallbacks

Provider adapters

• OpenAI adapter
• Anthropic adapter
• Gemini adapter
• Internal model adapter

Data stores

• usage DB
• prompt/version store
• audit log store
• metrics/tracing backend

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11) Minimal implementation pattern

If you’re building it yourself, create these modules:

• api/ — public endpoints
• auth/ — tenant and user auth
• router/ — model/provider selection
• adapters/ — provider-specific integrations
• normalizer/ — common response format
• policies/ — rate limit, safety, allow/deny rules
• telemetry/ — logs, metrics, traces
• billing/ — token and cost accounting

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12) Example flow

1. Your app sends a chat request to the gateway
2. Gateway authenticates tenant
3. Gateway checks policy and quotas
4. Router picks provider/model
5. Adapter transforms request to provider format
6. Provider returns response
7. Gateway normalizes response
8. Gateway logs usage and returns result

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13) Start small

If you want to implement this quickly:

• Begin with one endpoint: POST /ai/chat
• Support one model provider
• Add normalized request/response types
• Add logging and tenant auth
• Then add embeddings, moderation, and fallback routing

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If you want, I can also give you:

1. a reference architecture diagram,
2. a Node.js / Python gateway skeleton, or
3. a production checklist for building this securely.