Buyer’s guide · 2026

Best LLM Observability Tools in 2026

LLM apps have their own signals, tokens, cost, latency, quality, and their own failure modes. This is an honest shortlist of LLM observability tools in 2026, ordered by depth of LLM-specific insight and what happens after you find a problem.

The shortlist

Most LLM observability tools help you see prompts, tokens, cost and quality. We ordered by how complete that view is and, for production operations, whether anything acts on the problems it surfaces.

1

Ops Singularity

Best for: Operating LLM apps in production

Beyond observing, its AI/ML and Agent Ops pillar operates LLM apps and agents: it watches cost, latency and quality via OpenTelemetry and takes governed action when a model or agent degrades. Air-gapped ready for private-model estates.

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2

Langfuse

Best for: Open-source LLM tracing

Open-source tracing, prompt management and evaluation for LLM apps. Best for teams that want a self-hostable, developer-centric LLM trace store.

3

Arize Phoenix

Best for: Evaluation and drift

Open-source LLM and ML observability with strong evaluation and drift analysis. Best for teams focused on model quality and evals.

4

Helicone

Best for: Proxy-based logging

Proxy-based logging of LLM calls with cost and usage analytics. Best for quick drop-in cost and request visibility.

5

Traceloop / OpenLLMetry

Best for: OTel-native instrumentation

OpenTelemetry-based instrumentation for LLM apps, emitting standard gen_ai spans. Best for keeping LLM traces in your existing OTel pipeline.

6

Datadog LLM Observability

Best for: Suite-integrated

LLM monitoring inside the broader Datadog suite. Best when you already run Datadog and want LLM views alongside everything else.

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Observing versus operating LLM apps

Deciding factor: do you need to see LLM behaviour, or run it in production. Evaluation and tracing tools excel at the former. If you need cost and quality watched and acted on, with governance and the option of private, air-gapped models, that is an operations problem Ops Singularity is built for.

Frequently asked questions

What should LLM observability capture?

Prompt and completion tokens and cost, model latency and time-to-first-token, error and rate-limit rates, and, for RAG, retrieval quality. See our guide on observing LLM apps with OpenTelemetry.

Is LLM observability different from normal APM?

Yes. It adds token, cost and quality signals and agent-step tracing that traditional APM does not model, which is why gen_ai semantic conventions exist.

See autonomous resolution on your own stack.

Bring a real incident. We will show you Sentinel investigate, act and verify end to end, with every action reversible and audited.

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