best llm observability platforms for production

You've got an LLM-powered feature working in a notebook. Great. Now put it in front of real users and the questions change fast: Which prompt caused that 10-second latency spike? Why did token usage double overnight? Is that hallucination a one-off or a pattern?

Prototyping tools won't answer those. Production LLM observability means tracing every request end-to-end, tracking token spend per user or per model, running evals on real traffic, and catching failures before they cascade.1

Here are three platforms that handle that job — each with a different philosophy about where observability should live.

portkey — best for production gateways and failover

Portkey sits between your app and every LLM provider as a proxy gateway. Every request, response, latency, token count, and error gets logged automatically — no SDK changes, no manual instrumentation.1

What makes it stand out in production is the failover and fallback logic. If OpenAI is slow, Portkey can route to Anthropic. If a model returns a bad response, you can retry with a different temperature. All of that is configurable through the dashboard without redeploying code.

The observability side gives you per-request tracing, cost breakdowns by model and user, and prompt-level analytics. It's SaaS, so there's nothing to self-host.

Best for: Teams that want observability as a side effect of a production gateway, with automatic failover baked in.

litellm — best open-source gateway for spend tracking

LiteLLM takes a similar proxy approach but goes all-in on open source and cost visibility. It normalizes calls to 100+ LLM providers behind a single OpenAI-compatible API, then tracks every token and dollar spent across all of them.2

The spend tracking is unusually granular: you can break down costs by model, by user, by API key, or by custom tags. Combined with budget limits and rate limiting, it's a solid choice for teams that need to control costs across multiple projects or departments.

Because it's open source and self-hostable, you own the data and the infrastructure. The trade-off is you're responsible for uptime and scaling the proxy yourself.

Best for: Cost-conscious teams that want open-source control and don't mind self-hosting.

datadog apm — best for unified full-stack observability

If your team already lives in Datadog, adding LLM tracing to your existing APM setup means you can correlate a slow LLM call with a database query, a high CPU on the backend, or a network blip — all in one view.1

Datadog's LLM Observability product traces prompt and completion pairs, tracks token usage, and surfaces latency breakdowns. It integrates with LangChain, LlamaIndex, and the OpenAI SDK directly, so you don't need a separate proxy layer.

The real advantage is context: when an LLM call fails, you can see whether it was the model, the infrastructure, or something upstream. That's hard to get from a standalone observability tool.

Best for: Teams already on Datadog that want LLM traces alongside their existing application monitoring.

how they compare

which one should you pick?

• If you need failover and a production gateway today, Portkey gives you observability as a free side effect of routing traffic.
• If cost control is your top concern and you want open source, LiteLLM's spend tracking is the most flexible option out there.
• If you're already in Datadog, don't add another dashboard — LLM Observability inside APM gives you the full picture.

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