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If you call more than one large language model from your code, you face a massive cost gap.
Consider one task: generating a 100,000-token report.
- A cheap model costs about $0.03.
- An expensive frontier model costs about $3.01.
That is a 106x price difference for the same result. You do not want to rewrite your application eleven times to save money.
An AI gateway solves this. It acts as a proxy between your code and model providers. You change a single URL in your code, and you gain access to many models through one endpoint.
Benefits of using a gateway:
- Automatic failover when a provider goes down.
- Caching to save money.
- Per-team rate limits and budgets.
- Usage and cost tracking.
- Security guardrails.
Choose your setup based on your needs:
Hosted (Minimal Ops):
- OpenRouter: Great marketplace with 400+ models.
- Vercel AI Gateway: Adds routing and caching at list price.
- Cloudflare AI Gateway: Adds routing and caching at list price.
Self-hosted (Your Infrastructure):
- LiteLLM: The standard for Python users.
- Bifrost or TensorZero: Built for high throughput.
- Kong, Higress, or Apache APISIX: Best if you already use Kubernetes.
Three things to watch for:
Hidden costs: Reasoning models charge for invisible "thinking" tokens. Always budget for total output, not just the visible answer.
Cache fragility: Caching is cheap but breaks easily. A single changed byte in your prompt can ruin your cache hit rate and spike your costs.
Security: A gateway holds your keys and sees every prompt. Treat it as a security perimeter. Keep it updated and never expose admin panels to the public internet.
Stop looking for the best gateway. Instead, design your routing policy. Use cheap models by default and only escalate to expensive models when a task fails.
Pick the tool that makes your policy easy to run.
Optional learning community: https://github.com/cuihuan/awesome-ai-gateway