I'm building an app with multiple model providers — what infra should I use?

If you’re building an app that talks to multiple model providers (OpenAI, Anthropic, Gemini, etc.), the best infra is usually a provider-agnostic LLM layer with a few supporting pieces around it.

Recommended stack

1. LLM gateway / abstraction layer

Use a middleware layer that standardizes:

• auth
• request/response schema
• retries/failover
• streaming
• tool/function calling
• logging and tracing
• rate limits and quotas

Good options:

• LiteLLM (very common for multi-provider routing)
• OpenRouter if you want one API for many models
• Your own thin adapter if you want maximum control

Why: It prevents your app from being tightly coupled to one provider’s SDK or response shape.

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2. Backend service for orchestration

Put all model calls behind your backend, not directly in the client.

Typical responsibilities:

• choose provider/model based on task, cost, latency, or availability
• manage auth keys securely
• enforce usage policies
• do prompt assembly
• run retries/timeouts/fallbacks
• post-process results

Use:

• FastAPI / Flask / Node.js / Go
• containerized with Docker
• deployed on Kubernetes, ECS/Fargate, Cloud Run, Fly.io, or Render

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3. Queue + workers for async jobs

For longer tasks like:

• document processing
• batch summarization
• embeddings generation
• eval runs
• tool-heavy agent workflows

Use:

• Redis + Celery / RQ / BullMQ
• or cloud queues like SQS, Pub/Sub, RabbitMQ

Why: Keeps your API responsive and lets you retry safely.

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4. Observability / tracing

This matters a lot once you’re using multiple providers.

Track:

• prompt/version
• model used
• latency
• token usage
• cost
• error rates
• output quality
• fallback frequency

Tools:

• Langfuse
• Helicone
• Arize Phoenix
• OpenTelemetry + your observability stack

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5. Routing / fallback logic

Implement a policy engine that can route by:

• task type
• cost ceiling
• latency target
• context window
• provider uptime
• user tier

Example:

• cheap model for classification
• stronger model for reasoning
• fallback to another provider if primary fails
• use smaller models for autocomplete / extraction

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6. Data layer

You’ll usually want:

• Postgres for app data, prompt configs, audit logs
• Redis for caching, rate limits, ephemeral state
• Object storage like S3/GCS for files, transcripts, artifacts
• optional vector DB if you’re doing RAG:
  • pgvector
  • Pinecone
  • Weaviate
  • Qdrant

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7. Secrets and key management

Never store provider API keys in the frontend.

Use:

• environment variables for small deployments
• AWS Secrets Manager, GCP Secret Manager, Vault, or 1Password Secrets Automation

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A good default architecture

Client
→ Your API backend
→ LLM gateway/adapter (LiteLLM or custom)
→ Model providers

With:

• Postgres for state
• Redis for cache/rate limits
• Queue/workers for async tasks
• Langfuse/Helicone for tracing

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My practical recommendation

If you want fastest time-to-market:

• Backend: FastAPI or Node.js
• LLM abstraction: LiteLLM
• DB: Postgres
• Cache/queue: Redis
• Tracing: Langfuse or Helicone
• Deployment: Cloud Run / ECS / Fly.io

If you want maximum control and portability:

• build your own provider adapter layer
• keep a clean internal interface like:
  • generateText()
  • streamChat()
  • embed()
  • moderate()
• add routing and observability yourself

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What I’d avoid

• Calling providers directly from the frontend
• Hardcoding provider-specific request formats everywhere
• No tracing/cost tracking
• No fallback strategy
• Mixing orchestration logic into UI code

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If you want, I can suggest a specific infra stack based on:

1. your cloud (AWS/GCP/Azure/Vercel/etc.),
2. expected traffic,
3. whether you need RAG / agents / evals,
4. and your language/framework.