Buyer’s guide · 2026
Best LLM Observability Tools in 2026
Written by
Amber Jain
Published
Reading time
3 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.
Why LLM observability is a genuinely new discipline
Traditional APM assumes deterministic code paths. Same input, same output, same latency (roughly). LLM applications violate all three assumptions on the first request. The same prompt can produce different completions across requests; token usage varies wildly with input; latency fluctuates because of provider capacity, model choice and the presence or absence of tools invoked mid-request. These are not bugs; they are the shape of the technology. LLM observability had to be invented, not adapted, to capture prompt and completion tokens and cost, agent traces across tool calls, quality signals against expected outputs, and the RAG retrieval steps that increasingly precede generation. The OpenTelemetry gen_ai semantic conventions are the standardisation of this new discipline. The strongest tools embrace them; the weakest tack a proprietary schema onto everything.
Five criteria for an LLM observability tool in 2026
- OpenTelemetry gen_ai conventions support. The standard is settling; tools that ignore it are producing data that will not travel.
- Per-request cost and token visibility. Aggregate averages hide the outliers that generate the customer complaints.
- Agent-step tracing. For multi-step LLM agents, the trace of intermediate reasoning and tool calls is where the debugging happens.
- Quality evaluation, not just latency. Latency is easy to measure; quality against ground truth or business signal is the actual product concern.
- Path to production action. Detecting a quality regression is one thing; falling back, rolling back, or routing around the degraded model or agent under policy is what the observability data enables when a resolution layer sits above it.
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.
Do we need a separate LLM observability tool if we already have APM?
Usually yes, at least initially. LLM-specific signals (tokens, cost per prompt, agent-step traces, quality evaluation) are not what traditional APM was built to model. The path most teams take is to add an LLM observability layer while keeping APM for the surrounding application, with OpenTelemetry as the shared telemetry pipeline.
How does LLM observability fit with private-model or air-gapped deployments?
This is where deployment posture starts to matter. Teams running private or air-gapped LLMs (for regulatory or data-sensitivity reasons) cannot use SaaS-only observability platforms that require prompts and completions to leave the perimeter. The observability layer has to run inside the boundary too. Opstral's AI Ops pillar is built for this constraint, alongside its air-gapped resolution capability.