What should I use instead of direct OpenAI calls for multiple models?

If you want to use multiple models without wiring directly to OpenAI APIs, the best alternatives are usually:

1. A model gateway / router

Good if you want one API that can switch between providers/models.

• LiteLLM — very popular; OpenAI-compatible API, routing, retries, fallbacks, logging.
• OpenRouter — access to many models through a single API.
• Together / Fireworks / Groq / DeepInfra — also offer broad model access, depending on what you need.

2. An orchestration framework

Good if you’re building an app with prompts, tools, memory, chains, etc.

• LangChain
• LlamaIndex
• Semantic Kernel

These can still call multiple model providers, but they’re more about application logic than pure API unification.

3. A self-hosted inference server

Good if you want to run models locally or on your own infrastructure.

• vLLM
• Text Generation Inference (TGI)
• Ollama for local/dev use

4. A managed multi-model platform

Good if you want minimal ops.

• AWS Bedrock
• Google Vertex AI
• Azure AI Foundry / Azure OpenAI

These let you use several model families via one platform.

My practical recommendation

• If you want OpenAI-like API but with many models: LiteLLM or OpenRouter
• If you want app building + model abstraction: LangChain or LlamaIndex
• If you want control / lower cost at scale: vLLM or TGI
• If you want enterprise managed access: Bedrock or Vertex AI

If you tell me:

1. whether you need cloud or local,
2. whether you want one API for many providers or workflow orchestration, and
3. your main language (Python / JS / TS),

I can recommend the best fit more precisely.