What is the difference between LLM observability and LLM monitoring?

Monitoring tracks metrics over time and alerts on thresholds: latency, error rate, cost, quality scores. Observability adds the ability to investigate a specific execution after the fact, reconstructing the trace to understand why one request behaved differently. Most products here do both, with the trace view carrying the observability half.

Do I need a separate tool for agents?

It depends on the depth of agent work. General-purpose tracing renders an agent run as a long flat span list, which is workable for simple chains and painful for a forty-step run with tool calls and handoffs. Agent-first tools treat the session as the primary object and reconstruct the decision tree, which matters once agents become the main workload.

Can observability data serve as compliance evidence?

Sampling means the specific request under review may not exist in the store, and application-emitted records are self-attestation instead of independent evidence. Audit records need to be unsampled, written by a system separate from the one being audited, and retained on a compliance schedule.

Which of these tools can block a request?

None of them block on policy grounds. Helicone sits on the request path as a proxy and can rate limit, which is traffic control instead of an authorization decision. Blocking based on identity, role, and prompt data classification requires a policy engine positioned before the provider.

How many of these should a team run?

Usually one from this list plus a policy layer. Running two observability tools duplicates capture cost and splits the investigation surface. Choose based on your primary object: framework-heavy application work, agent sessions, evaluation, or infrastructure correlation.

LLM Observability Tools: 10 Platforms Compared

These 10 LLM observability tools fall into four capture models: SDK tracing, framework hooks, proxy logging, and OpenTelemetry collection. The practical choice comes down to the object your team needs to inspect, a request, an agent session, or an evaluation run, plus where the trace data is written.

I want to make that selection concrete, then cover the audit-evidence limit that product telemetry leaves behind. The gap appears when a reviewer asks for one named request instead of a latency chart.

TL;DR

Choose an LLM observability tool by capture point and investigation object. SDK tools suit instrumented application traces, session-first tools suit agents, and proxy capture helps with request coverage. Audit evidence needs an unsampled, independently written decision record for the request under review.

How to read this list

Three architectural questions separate these tools more than any feature grid. Capture can happen through an SDK, a framework hook, a proxy, or an OpenTelemetry collector. The primary object may be a request span, an agent session, or an evaluation run. Coverage stops at instrumented code for some products and includes provider-bound traffic for proxy capture.

A tool's answers to those three predict almost everything about where it fits. The OpenTelemetry GenAI semantic conventions are also useful for checking what an instrumented trace can consistently carry across providers.

1. LangSmith

Built by the LangChain team, strongest when your application is a LangGraph state machine. Traces render the full execution tree, any trace saves into a dataset, and evaluation runs against those datasets with judge or code scorers. Prompt versioning and a comparison playground sit alongside.

Capture is SDK-based and framework-agnostic in practice, though the deepest value comes from framework awareness. Buyer: teams building and iterating on LLM applications.

2. Langfuse

Open source, self-hostable, and the default choice for teams that want tracing without a vendor holding prompt payloads. Traces, sessions, prompt management, and evaluation all present, with a generous managed tier for teams that would rather not run it.

The self-hosting story is the differentiator. Prompt content inherits the sensitivity of whatever the user typed, so keeping the trace store inside your own boundary avoids creating a second processing location in a vendor cloud.

3. Arize Phoenix

Open source, built on OpenInference over OpenTelemetry, and installable with a pip command. Its centre is RAG and retrieval debugging plus LLM-as-judge evaluation, with human annotation on top.

Phoenix runs locally for development or self-hosted for teams. Arize sells a commercial platform above it for production monitoring at scale. Buyer: ML and application engineers tuning retrieval pipelines.

4. Datadog LLM Observability

This one is the distribution play. If your organisation already runs Datadog, model call tracing lands next to APM traces, host metrics, and logs, and the pivot between them collapses a three-tool debugging session into one.

Sensitive Data Scanner applies to payloads, and quality evaluators run over sampled traces. Buyer: platform and SRE teams with an existing Datadog footprint.

5. AgentOps

Purpose-built for agents, with the session as the primary object instead of the request. Session replay with point-in-time precision, multi-agent interaction visualisation, per-session cost tracking, and native integrations for CrewAI, AutoGen, LangChain, and the OpenAI Agents SDK.

For debugging a forty-step agent run, nothing general-purpose comes close. Buyer: teams shipping autonomous agents. Detail in DeepInspect vs AgentOps.

6. Braintrust

Braintrust puts evaluation ahead of everything else. Datasets, LLM-as-judge and code scorers, human review, quality gates in CI, plus automation that surfaces patterns in production logs and iterates on prompts against a goal you set.

Observability here feeds evaluation and serves product teams treating output quality as the primary risk. Detail in DeepInspect vs Braintrust.

7. Helicone

Proxy-based capture, which makes it architecturally distinct from most of this list. Point your base URL at Helicone and logging happens without touching application code, along with caching, rate limiting, and cost tracking.

That capture model gives coverage the SDK tools cannot match, since any caller using the endpoint is recorded whether or not anyone instrumented it. Buyer: teams wanting logging and cost control with minimal integration work. Detail in DeepInspect vs Helicone.

8. Weights and Biases Weave

An extension of the W&B experiment tracking most ML teams already run. Traces, evaluations, and comparison views inherit the versioning and experiment lineage discipline W&B built for model training.

Buyer: ML organisations with W&B already embedded, wanting LLM work in the same system of record as their training runs.

9. PostHog LLM Analytics

Product analytics with LLM tracing attached. The distinguishing move is correlating model calls with product events, so you can ask whether users who hit a slow or low-quality AI response churned.

Buyer: product teams treating the AI feature as a product surface, with infrastructure telemetry handled elsewhere.

10. Dynatrace LLM Observability

The APM-heritage entry alongside Datadog, with Dynatrace's automatic dependency mapping applied to AI services. Model calls appear inside the topology of the application calling them.

Buyer: enterprises standardised on Dynatrace for application performance.

The two limits every tool here shares

Both limits follow from architecture, not any vendor's roadmap.

Sampling

Observability data is priced and retained on the assumption that a representative subset answers the question. That assumption holds for latency percentiles and error rates, where a 5% sample tells you what you need.

That assumption fails for evidence. An auditor asking what happened on a specific request on March 14 gets no answer from a store that sampled it away. Compliance records have to be unsampled, which is a different cost profile and a different retention schedule than telemetry. Retention makes this worse in practice: observability windows get tuned down whenever the bill grows, while audit obligations for high-risk AI run to years.

The application writes its own record

Every SDK-based tool on this list emits data from inside the application being observed. For debugging that is correct and completely unremarkable. For evidence it is the structural problem.

An application that makes a decision and also writes the log attesting to that decision has produced self-attestation. Three failure modes follow: selective logging where success paths report and edge cases quietly do not, suppression where the same system that failed can modify the record, and loss on crash where the model responded but the process died before the span flushed. In every other regulated industry, the system under audit is not the system generating the audit record.

Observability and enforcement are different jobs

None of these tools stop anything. That is worth saying plainly, because "guardrails" appears in the marketing of several and means scoring instead of gating.

An evaluator flagging a prompt injection attempt on a sampled trace produces a finding after the response reached the user. Mandiant's M-Trends 2026, built on over 500,000 hours of incident response, put median attacker handoff at 22 seconds, down from more than eight hours in 2022 (Help Net Security). Against that tempo, a control that reports is a forensic tool.

The complement to observability is a decision point on the request path: something positioned to refuse a call before it reaches the provider, keyed on who is asking and what the prompt contains. Most mature stacks I would design run both, with the observability tool covering quality and performance and a policy layer covering authorization and evidence. Background in AI gateway observability.

DeepInspect

This is the gap DeepInspect closes. DeepInspect is a stateless proxy at the AI request boundary that evaluates identity, data classification, model authorization, and organizational policy for every request, then returns a pass or block decision before traffic reaches the model.

Because capture happens on the wire instead of in application code, coverage extends to callers nobody instrumented: the analyst working from a notebook, the vendor SaaS tool calling a model on your data, the service another team shipped last quarter. Enforcement runs inline with overhead under 50 ms in internal testing, against inference times of 500 ms to several seconds. Every decision writes an unsampled structured record carrying the identity, role, data classification, policy version, and outcome, committed before the response returns on storage the calling application cannot write to. Book a demo today.