I need to serve both open-source and hosted models through one interface

You can do this by putting a single API gateway / abstraction layer in front of both model types, then routing requests to either:

• open-source/self-hosted models (e.g., Llama, Mistral, Qwen)
• hosted/proprietary models (e.g., OpenAI, Anthropic, Gemini, Bedrock)

Common pattern

1) Use one unified API schema

Expose one internal interface like:

• POST /chat
• POST /embeddings
• POST /rerank
• POST /images

Then translate that request to whichever backend you want.

2) Add a model router

Route by one or more of:

• explicit model name from the client
• cost
• latency
• context length
• availability/fallback
• task type

Example:

• model: "llama-3.1-70b" → self-hosted vLLM endpoint
• model: "gpt-4.1" → OpenAI
• model: "claude-sonnet-4" → Anthropic

3) Normalize responses

Convert all vendor responses into your own standard format, e.g.:

• text
• usage
• finish_reason
• tool_calls
• citations if applicable

4) Handle differences in capabilities

Some models support:

• function calling
• multimodal inputs
• streaming
• JSON mode
• tool use

Your interface should either:

• expose the lowest common denominator, or
• support feature flags per model and reject unsupported requests cleanly

---

Fastest way to implement

Option A: Use an existing proxy/router

Good if you want speed and vendor neutrality.

Examples:

• LiteLLM — very common for routing many LLM providers behind one OpenAI-compatible API
• OpenRouter — if you want a hosted aggregation layer
• Portkey — gateway, observability, routing
• LangChain / LlamaIndex — orchestration, not ideal as the main gateway

LiteLLM is especially useful if you want:

• one OpenAI-style API
• many providers
• retries/fallbacks
• cost tracking
• model aliases

---

Option B: Build your own thin gateway

If you want more control, make a small service that:

1. accepts OpenAI-style requests
2. maps model IDs to providers
3. converts auth headers/secrets
4. forwards the call
5. normalizes the response
6. logs usage and errors

This is a good fit if:

• you need custom policy
• you want strict data routing
• you need per-tenant model allowlists
• you have compliance constraints

---

Suggested architecture

Client
  ↓
Your Unified API
  ↓
Model Router / Policy Engine
  ├── Self-hosted inference (vLLM / TGI / Ollama / Triton)
  ├── OpenAI
  ├── Anthropic
  └── Gemini / Bedrock / etc.

---

Practical recommendation

If you want the least engineering effort:

• Use LiteLLM as the gateway
• run open-source models behind vLLM or TGI
• point hosted models through their native APIs
• use a single OpenAI-compatible endpoint for your apps

If you want maximum control:

• build your own gateway and keep the API contract stable

---

Key design decisions to make

1. Do you want OpenAI-compatible API semantics?

   • If yes, integration is much easier.

2. Do you want routing at request time or per model alias?

   • Request time = flexible
   • Alias-based = simpler for clients

3. Do you need fallback?

   • Example: try open-source first, then hosted if overloaded

4. Do you need tenant isolation / data residency?

   • This often affects provider choice and routing logic

---

Example model mapping

models:
  gpt-4.1:
    provider: openai
    model: gpt-4.1
  claude-sonnet:
    provider: anthropic
    model: claude-sonnet-4
  llama-3.1-70b:
    provider: vllm
    endpoint: http://llm-internal:8000/v1

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If you want, I can give you:

1. a reference architecture
2. a LiteLLM setup
3. a custom FastAPI gateway example
4. or a model routing policy design for your use case.