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# Efficient multi-provider agent environments with AI gateways: best practices

**[Datadog](https://daily.dev/sources/datadog)** · 11 min read · 0 upvotes · 0 comments

## Summary

AI gateways provide a single API endpoint for routing LLM calls across multiple providers, centralizing reliability controls, model selection, and cost governance for multi-agent environments. Key practices include configuring centralized retry logic, circuit breakers, and fallback models to handle provider outages gracefully; using gateway routing rules to swap models per agent task without code changes; enforcing per-team and per-key budget limits via virtual keys and TPM/RPM caps; and instrumenting per-task telemetry to validate routing decisions and catch regressions. Tools like LiteLLM and OpenRouter are highlighted, with Datadog Agent Observability recommended for trace-level visibility across the full agent loop.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://www.datadoghq.com/blog/ai-gateways-best-practices>

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---

Tags: [#ai-agents](https://daily.dev/tags/ai-agents), [#ai-gateway](https://daily.dev/tags/ai-gateway), [#llm-observability](https://daily.dev/tags/llm-observability)

[View this post on daily.dev](https://daily.dev/posts/efficient-multi-provider-agent-environments-with-ai-gateways-best-practices-wh7p56xuv)

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