Buyer’s guide · 2026
Best LLM Observability Tools in 2026
Written by
Amber Jain
Published
June 2026
Reading time
8 min read
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.
Opstral
Best for: Operating LLM apps in productionBeyond observing, its AI 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.
Explore the platform →Langfuse
Best for: Open-source LLM tracingOpen-source tracing, prompt management and evaluation for LLM apps. Best for teams that want a self-hostable, developer-centric LLM trace store.
Arize Phoenix
Best for: Evaluation and driftOpen-source LLM and ML observability with strong evaluation and drift analysis. Best for teams focused on model quality and evals.
Helicone
Best for: Proxy-based loggingProxy-based logging of LLM calls with cost and usage analytics. Best for quick drop-in cost and request visibility.
Traceloop / OpenLLMetry
Best for: OTel-native instrumentationOpenTelemetry-based instrumentation for LLM apps, emitting standard gen_ai spans. Best for keeping LLM traces in your existing OTel pipeline.
Datadog LLM Observability
Best for: Suite-integratedLLM monitoring inside the broader Datadog suite. Best when you already run Datadog and want LLM views alongside everything else.
Read the comparison →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.