best observability tools for llm applications

why llm observability is different

Traditional APM tools were built for request/response latency, error rates, and uptime. They tell you if your API is slow or down. That's fine for a CRUD app. But LLM applications introduce a whole new class of failure modes: hallucinations, prompt injection, cost blowouts, and subtle regressions in response quality that no 500 error will catch.2

LLM observability platforms add three layers that standard APM misses:

1. Trace-level prompt/response logging — see exactly what went in and what came out.
2. Evaluation and scoring — run automated checks on output quality, safety, and accuracy.
3. Cost and token tracking — because every LLM call has a variable price tag.

The tools below represent the current best options, depending on whether you need an open-source data plane, a gateway-first architecture, or deep integration with an existing monitoring stack.

the picks

3. helicone — best for gateway-first observability

Helicone sits between your application and the LLM provider as a proxy, giving you caching, rate limiting, and cost tracking out of the box.1 Its observability layer captures every request/response pair with minimal latency overhead, and it supports multi-provider routing so you're not locked into a single backend.

Best for: teams that want observability bundled with a gateway — caching, fallback, and cost controls in one place.

Trade-off: less eval depth than dedicated evaluation platforms; better suited for operations than prompt engineering iteration.

4. portkey — best for production reliability

Portkey is a production-grade AI gateway with automatic failover, prompt versioning, and a built-in prompt CMS.1 It's designed for teams running LLMs at scale who need to manage multiple providers, handle provider outages gracefully, and track every prompt variation in a structured way.

Best for: production deployments where reliability and prompt management are top priorities.

Trade-off: the gateway layer adds complexity for smaller teams; the eval features are solid but not as deep as dedicated eval-first platforms.

5. datadog llm observability — best for datadog-native teams

If your org already lives in Datadog, the LLM Observability module extends the same dashboarding, alerting, and trace analysis to LLM calls.3 It surfaces token usage, latency breakdowns, and error patterns alongside your existing infrastructure metrics, which is powerful for teams that want a single pane of glass.

Best for: enterprises already invested in Datadog who want to add LLM monitoring without adopting a new platform.

Trade-off: limited eval capabilities compared to purpose-built LLM observability tools; you'll likely need a separate evaluation pipeline for quality scoring.

comparison at a glance

why logging isn't enough for genai

The core insight behind LLM observability is the feedback loop. In traditional software, you log an error, fix the code, and deploy. With LLMs, the "code" is a prompt and a model — both probabilistic. You can't just fix a bug; you need to evaluate whether the new prompt produces better outputs than the old one.1

That's why the best LLM observability platforms don't just log — they integrate with evaluation frameworks, CI/CD pipelines, and experiment trackers. The goal is to close the loop: observe → evaluate → improve → deploy → observe again.2

how to choose

• If you need a gateway with observability baked in, start with Helicone — it's the simplest way to get caching, routing, and monitoring in one deploy.
• If production reliability is your top concern, Portkey gives you failover, prompt versioning, and a structured prompt CMS that keeps your team organized.
• If you're already a Datadog shop, the Datadog LLM Observability module is the path of least resistance — just know you'll need to supplement it with an eval pipeline.

Disclosure: AskBuy may earn a commission if you purchase through the links above. We only recommend tools we've researched and believe offer genuine value.