If you want to use OpenAI, Anthropic, and Gemini through one normalized API without rewriting your app, the best options are usually one of these:

1. Use an LLM gateway / unified API layer

These services expose a single API and translate to each provider behind the scenes.

Popular options

• LiteLLM: open-source, very common for OpenAI-compatible routing/normalization
• OpenRouter: one API for many models/providers
• Portkey: API gateway with observability, policy controls, fallbacks
• Helicone: more observability-focused, can also help route/standardize
• Cloudflare AI Gateway: proxy/gateway style normalization and routing
• Bedrock / Vertex / Azure AI Studio: not truly “OpenAI + Anthropic + Gemini” unified, but good if you’re okay centering on one cloud ecosystem

Best if you want

• minimal app changes
• retries/fallbacks
• model switching
• logging/metrics
• centralized keys and policies

2. Use an OpenAI-compatible abstraction

If your app already talks to the OpenAI SDK, the easiest path is to pick a provider/gateway that supports the OpenAI API shape.

Best fit

• LiteLLM
• OpenRouter
• some gateway products that provide an OpenAI-compatible endpoint

This often lets you keep most of your code unchanged and just swap:

• base URL
• API key
• model name

3. Use a framework-level abstraction

If you’re already using an app framework, these can normalize providers at the application layer.

Examples

• LangChain
• LlamaIndex
• Vercel AI SDK
• Semantic Kernel

These are useful when you want higher-level orchestration, but they usually involve more app refactoring than a gateway.

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My practical recommendation

If your goal is “don’t rewrite my app”

Start with:

LiteLLM

Best when you want:

• OpenAI-compatible API
• Anthropic/Gemini/OpenAI support
• self-hosting option
• routing, fallback, load balancing
• portability

OpenRouter

Best when you want:

• fastest integration
• lots of models under one API
• simple switching across providers

Portkey

Best when you want:

• enterprise-style controls
• observability
• policy management
• fallback and governance

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How they differ

Option	OpenAI-compatible	Self-hostable	Observability	Fallback/routing	Best for
LiteLLM	Yes	Yes	Good	Yes	Dev teams wanting control
OpenRouter	Mostly yes	No	Moderate	Yes	Fastest multi-model access
Portkey	Yes	Usually managed	Strong	Yes	Production governance
Helicone	Often yes	Proxy style	Strong	Some	Logging/monitoring
Cloudflare AI Gateway	Proxy-based	Managed	Good	Some	Edge/proxy users

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Important caveats

Even with normalization, you won’t get perfect parity because providers differ in:

• message formats
• tool/function calling behavior
• JSON mode / structured output support
• streaming semantics
• token counting
• safety filters
• image/audio/multimodal capabilities
• model-specific parameters

So “normalize” usually means:

• common request/response shape
• common retry/fallback behavior
• common auth/logging
• common model selection

not 100% identical behavior.

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Simple decision rule

• Need the least code change? → LiteLLM or OpenRouter
• Need enterprise controls and observability? → Portkey
• Need logging/analytics only? → Helicone
• Need one cloud’s ecosystem? → Azure / Vertex / Bedrock
• Need long-term portability and control? → LiteLLM self-hosted

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If you want, I can also give you:

1. a “best option by use case” shortlist, or
2. a drop-in migration example showing how to swap OpenAI SDK code to LiteLLM/OpenRouter with minimal changes.