Here's what our stack looked like a year ago. One key for Anthropic. One for OpenAI. One for Google. One for DeepSeek. One for Mistral. Five SDKs. Five sets of docs open in five tabs. Five ways to handle a rate limit error.
Then a provider changed its price overnight. Then another had an outage during a demo. Then a teammate spent an afternoon chasing a bug that turned out to be a version mismatch in one SDK, not our code.
That's when we switched. We moved to one API key for every LLM instead. One endpoint. One client. Every provider sits behind it. The change wasn't small — we got back the whole afternoon we used to lose every month.
This setup looks fine on day one. It gets costly fast, and not just in dollars.
Here's what piles up:
None of this is really about the AI. It's plumbing. And plumbing problems show up at the worst time.
A router sits between your app and every model provider. You send it one request, with one key. It forwards that request to whatever provider and model you picked.
Skip the separate credentials for Anthropic, OpenAI, Google, DeepSeek, Meta's Llama models, Mistral, and xAI. Hold one key instead, stored as a single environment variable. That key checks in against one endpoint — an endpoint that already speaks every provider's language behind the scenes.
Your code doesn't need to know Anthropic's API shape differs from OpenAI's. The router already fixed that for you.
This is the part that changed how we build. You don't swap SDKs to try a new model. You change one string in the request:
# same client, same key, different model
response = client.chat.completions.create(
model="anthropic/claude-...", # swap to:
# model="openai/gpt-...",
# model="google/gemini-...",
# model="deepseek/deepseek-...",
messages=[{"role": "user", "content": "Summarize this ticket."}]
)
Testing a cheaper model used to take a full day of wiring. Now it's one line and a re-run.
Do this once. Every future "let's try Model X" chat stops being a whole project.
This isn't a fix for everything, and it's worth saying so plainly. If you need one provider's exact feature the router hasn't added yet, go direct. Chasing the lowest possible latency? Go direct too.
For most products, the flexibility wins anyway. You can still drop to a direct link for the one workload that truly needs it, while everything else runs through the router.
Does a router add lag versus calling a provider directly? A small hop gets added, usually just a few milliseconds. Most products won't notice it next to the model's own response time. That's a fair trade for skipping five integrations.
Can I still use provider-only features through a router? Yes, mostly. Providers share common features like streaming, function calls, and vision input, and routers support these across the board. Something brand new might take a router longer to add — that's the one real trade-off worth knowing up front.
Is one router key less safe than five separate keys? Not really. You still hold a secret. You still rotate it. You still scope it to what your app needs. The security model doesn't get weaker — it just gets simpler to manage, which usually means fewer mistakes.
What happens if the router goes down? Here's the honest trade-off: you've moved from five failure points to one, plus the router itself. In practice, a solid router adds far less downtime than five separate providers would on their own. Still, check a router's uptime record before you bet your product on it.
Five API keys, five SDKs, and five failure modes isn't a sign of a complex product. It's a sign your plumbing hasn't kept up with how many models are worth using today. One API key for every LLM turns that mess into one plain, solid piece of infrastructure — exactly what infrastructure should be.
Want this flexibility without building the router yourself? Try our AI agent free and watch every provider work behind one key, right out of the box.
Sources: OpenRouter Quickstart Docs, OpenRouter Models