9 Best LLM Observability Tools of 2026

Langfuse ranks first because it pairs a full tracing, prompt and evaluation stack with the lowest paid entry price in this list among tools that include unlimited users. Its documentation describes it as an open-source AI engineering platform that is self-hostable, and the docs include setup guides for Docker Compose, Kubernetes with Helm and Terraform on the three big clouds. On Langfuse Cloud, the Hobby plan is free with 50k units a month and 30 days of data access for 2 users. Core costs $29 a month, includes 100k units and 90 days of data, and has no seat cap, which matters for teams where product managers and reviewers need access too. Pro at $199 adds 3 years of data access, unlimited annotation queues and SOC2 and ISO27001 reports. The trade-off is that enterprise identity features sit in a $300 a month Teams add-on, and heavy tracing volumes are billed at $8 per 100k units beyond the allowance.

LangSmith comes second on the strength of its feature set. The observability page lists native tracing for agent frameworks and OpenTelemetry, SDKs for Python, TypeScript, Go and Java, message threading for multi-turn chats, online LLM-as-judge and code evals, trajectory monitoring and alerts through webhooks or PagerDuty. Insights clusters traces to show common behaviors and failure modes without manual tagging. The free Developer plan covers 1 seat and 5k base traces a month. Plus costs $39 per seat a month with 10k base traces, and extra traces are billed in LangChain Standard Units at 0.005 LSU each. The same account also covers agent deployment, sandboxes and an LLM gateway with cost controls and PII redaction. Value scores lower than Langfuse because seat pricing adds up for larger teams, and self-hosting needs an Enterprise contract.

Arize earns fourth place with two ways in. Phoenix is open source and can run locally or be self-hosted, which suits teams that want to start tracing without a vendor account. Arize AX is the hosted platform, and its pricing page lists OpenTelemetry compliant tracing, token, latency and cost tracking, online evals on traces and spans, trace evals for agent trajectories and session evals for multi-turn conversations, plus multi-modal tracing for image, voice and PDF. AX Free gives unlimited users, evals and experiments with 25k spans, 1 GB and 15 days of retention. AX Pro costs $50 a month for 50k spans, 10 GB and 30 days. Data can sit in the US, EU or Canada. Ease of use scores a little lower because teams have to choose between two products and move between them as needs grow, and self-hosted AX, SSO and audit logs all wait for Enterprise.

Braintrust ranks third because its evaluation workflow is the most complete here. The docs organize the product around instrumenting, observing, annotating, evaluating and deploying, with evaluators, playgrounds, experiments, remote evals and online scoring of production traffic. Every plan includes unlimited users, projects, datasets and experiments, so a whole team can join for free on Starter, which includes 1 GB of processed data, 10k scores and 14-day retention. Pro costs $249 a month and adds $100 of model credits, 5 GB of processed data, 50k scores, 30-day retention, custom charts, environments and RBAC. Overage on Pro is $3 per GB and $1.50 per 1,000 scores. The price of Pro is what holds its value score back: a small team that only needs tracing pays less with Langfuse or Opik. Startups should check the 6 to 12 month free Pro offer first.

Comet Opik is the best-value pick on this list. The pricing page says the open-source edition shares the same codebase as the hosted versions and includes agent tracing, test suites and the agent playground. Free Cloud covers up to 10 team members, 25k spans a month and 60-day retention, which is longer than the free tiers from Langfuse, Arize or Braintrust. Pro Cloud costs $19 a month for up to 50 team members and 100k spans, with customizable span limits and retention. The feature list includes execution graphs, sessions, cost tracking, test suites with assertions, 30+ built-in metrics, annotation queues and integrations with 40+ frameworks, providers and gateways. It scores lower on features than the top three because production alerting and analytics are less extensive in its published plan comparison, and the Ollie coding assistant requires buying tokens after the trial.

Datadog now sells its LLM tracing product as Agent Observability, and it makes the most sense for teams already paying for Datadog. The product page says it runs the same Datadog tracer from development through production and correlates agent behavior with backend performance and end-user impact, which standalone tools cannot do on their own. Billing counts only LLM spans, so tool, workflow and retrieval spans are free. The free tier allows up to 40K LLM spans a month with 15-day retention and unlimited evaluations. Pro covers 100K LLM spans for $160 a month on an annual commitment, $200 month-to-month or $240 on-demand, with extra spans from $3.50 per 10K. Sensitive Data Scanner is included to redact personal, financial and health data. Value scores lowest here because longer retention costs $6.50 to $9 per 10K spans, and teams new to Datadog face a larger platform to learn.

Helicone takes a request-first approach. Its docs describe an open-source LLM observability product with an AI gateway that handles provider routing, caching, custom rate limits and fallbacks, so the same setup that records requests can also cut repeat calls. Observability covers sessions, user metrics, cost tracking, custom properties, alerts, reports and HQL, Helicone's query language. Hobby is free with 10,000 requests, 1 GB of storage and 7-day retention for 1 seat. Pro costs $79 a month plus usage, adds unlimited seats, alerts, reports and HQL, and keeps data for 1 month. Team at $799 a month adds 5 organizations, SOC-2 and HIPAA compliance and a dedicated Slack channel. It scores well on ease of use because logging sits in the request path, but its evaluation tools are thinner than those of Braintrust or LangSmith, and the jump from Pro to Team is steep.

Pydantic Logfire suits teams whose AI features sit inside a larger Python service. The pricing page describes one record-based meter for AI observability, evals, application performance and infrastructure, so LLM traces and normal application spans land in the same place. Every plan includes 10M records a month, and the free Personal plan is hard-capped at $0 with 3 projects, 30-day retention and 1 seat plus 2 read-only guests. Team costs $49 a month with 5 seats, unlimited agent, service and host monitoring, AI output evaluations and human review, then $2 per million records up to your spending cap. Growth at $249 adds unlimited seats and projects and up to 90-day retention. It ranks below the specialist tools because its evaluation features start only on paid plans and are less detailed in its plan list than in Braintrust or LangSmith, but the spending cap makes costs easy to predict.

W&B Weave is the natural pick for machine learning teams that already use Weights & Biases for training runs, since tracing, evaluation and model experiments share one workspace. The pricing page, now branded CoreWeave Forge, lists tracing that captures inputs, outputs and metadata for each inference, side-by-side comparison of recipes for accuracy, latency and token usage, LLM-as-a-judge metrics, production monitoring and PII redaction. The free plan includes AI application tracing, evaluations and scorers with 1 GB of Weave ingestion a month. Pro starts at $60 a month billed monthly with a 30-day trial and adds team access controls, CI/CD automations and Slack and email alerts, with 1.5 GB of ingestion. It ranks last because ingestion allowances are small for production tracing, extra data is billed per MB, and Pro is limited to companies with fewer than 50 employees.