Teams shipping AI features run into the same blind spots: latency, token costs, and model failures don't show up until a user runs into them. AI observability tools are how you see them coming.
A lot of LLM observability tools like to flex their "free" muscles, but "free" can mean a lot of things; it can be as generous as 100,000 events a month with no seat limit (yes, really), or as limited as a 24-hour retention window and a single seat.
This guide ranks these tools by how far their free tiers actually take you.
If you specifically want open source, we have a separate guide to the best free open source LLM observability tools.
At a minimum, a useful AI observability tool should:
The best tools go further with:
PostHog is an all-in-one developer platform where AI observability sits alongside product analytics, session replay, feature flags, experiments, error tracking, logs, and more.
Since LLM data is stored as regular events, you can connect it to user behavior, replay sessions where an agent failed, and ship prompt changes behind a feature flag without ever having to switch between tools.
PostHog's AI Evals score outputs with LLM-as-judge or your own code, and run automatically after a prompt or model change to catch the quality regressions that error rates miss.
And because PostHog AI and the MCP server can read your trace data, you can ask questions in plain English such as "what were my most expensive calls yesterday?" directly from your code editor.
Free tier: PostHog's free plan includes 100K LLM events per month, unlimited seats, and 30 day retention.
Strengths:
Teams building AI features inside a real product who want traces, evals, and cost tracking wired into the analytics, replay, and error data they already collect.
Langfuse is a focused LLM engineering platform for tracing, prompt management, datasets, and evals. It was early in this category and still the benchmark for depth in LLM-native workflows.
It offers prompt versioning and A/B testing, LLM-as-judge scoring, human annotation workflows, and dataset-based experiments, all in an OpenTelemetry-native platform.
What separates Langfuse from simple cost trackers is the depth of its eval and dataset workflows. You can build versioned test sets from production traces, run LLM-as-judge or heuristic scorers on live data or in offline experiments, and route human annotation through review queues.
Free tier: The Hobby plan includes 50K billable units per month (traces, spans, events, and scores all count), 30 days of data access, and 2 users.
Strengths:
Teams that want a dedicated, open-source LLM engineering platform with deep evaluation and prompt-management workflows.
Traceloop is a managed backend that ingests spans from OpenLLMetry an Apache-2.0 OpenTelemetry layer for LLM apps. The catch is retention: trace data disappears every 24 hours, making it useful for active debugging rather than long-term monitoring.
For persistent observability, point OpenLLMetry at PostHog, Langfuse, or your own backend instead.
OpenLLMetry's appeal is that it speaks plain OpenTelemetry. Pre-built instrumentations for many popular LLM, vector, and agent libraries are available across multiple programming languages, and spans can flow to any OpenTelemetry-compatible backend or your own collector.
Free tier: The Free Forever plan for Traceloop includes 50,000 spans per month, up to 5 seats, and 24-hour data retention only.
Strengths:
Developers who want vendor-neutral, OpenTelemetry-native instrumentation they can route to any backend, rather than a long-term monitoring home of its own.
Arize built Phoenix, a popular open source AI observability project with no limits on traces, retention, or users when self-hosted.
AX Free is the hosted version of that same project for teams who would rather not run infra, and it adds online (production) evaluations that the local open source build does not include.
Phoenix is the open-source core: OpenTelemetry-based tracing, versioned datasets, experiments, a prompt playground, and built-in evaluators for faithfulness, relevance, hallucination, and toxicity, all runnable locally with no limits.
Free tier: AX Free includes 25K spans per month, 1 GB ingestion per month, and 15-day retention.
Strengths:
Teams already running Phoenix locally who want a hosted, OpenTelemetry-based observability layer with built-in evals and a path into broader ML monitoring.
Lunary is a lean observability layer for LLM apps with prompts, analytics, human review, and agent tracing. Alongside tracing and cost tracking per user, session, and model, it threads conversations so you can follow a full multi-turn exchange, and it collects feedback directly from end users rather than only from internal annotators.
It is simpler than Langfuse or Phoenix, which is the point: a refined layer for teams that want conversation-level threading and prompt collaboration without a heavyweight platform.
Free tier: Lunary Free includes 10,000 events per month, 1 seat, and 30-day log retention.
Strengths:
Solo developers building chatbots or RAG apps who want simple platform for lightweight tracing, prompt management, and conversation threading.
HoneyHive targets production agent observability with OpenTelemetry-native ingestion, evals, and prompt studio features. It mostly deals with enterprises but still offers a self-serve developer tier.
It auto-instruments providers and tools like OpenAI, Anthropic, and Pinecone. HoneyHive then captures every prompt, retrieval, tool call, and model output as OpenTelemetry spans, and lets you run the same evaluators offline on datasets and online against live traffic. Because it is OTel-native, it stays agnostic across models, frameworks, and clouds with no lock-in.
Free tier: The Developer plan includes 10,000 events per month, up to 5 users, 30-day retention, and the full observability and eval suite.
Strengths:
Small teams building production agents who want OpenTelemetry-native tracing and evaluation with a clear path to enterprise compliance.
LangSmith is the platform layer in LangChain's stack: LangChain is the framework, LangGraph the orchestration runtime, and LangSmith the place you trace, evaluate, and now deploy agents.
Its tracing goes deeper into that ecosystem than anyone else's – node-by-node state diffs, full execution graphs, and model-plus-tool breakdowns you can replay against new model versions – and its eval framework spans datasets, LLM-as-judge, human annotation queues, and pairwise comparison, both pre-ship and on live traffic.
Free tier: The Developer plan includes 5,000 traces per month, 1 seat, 1 workspace, and 14-day base trace retention.
Strengths:
Teams building on LangChain or LangGraph who want first-party tracing, evaluation, and agent deployment in one tightly integrated platform.
Braintrust focuses on tracing, evals, and scoring with a strong AI analysis assistant for automated quality work. It is popular with teams that treat evals as product infrastructure.
Its "Loop" assistant agent generates scorers, prompts, and datasets from plain-language descriptions and mines production logs for failure patterns, so you are not hand-writing evaluation logic from scratch. Every agent run can be scored asynchronously in production across dimensions like correctness, safety, and efficiency.
Free tier: The Starter plan includes $10 in monthly credits, 1 GB processed data, 10,000 scores per month, 14-day retention, and unlimited users and projects.
Strengths:
Braintrust is best for...
Teams that treat evaluation as core product infrastructure and want scoring, experiments, and automated eval generation front and center.
Datadog bolts LLM tracing onto its established APM and infrastructure platform.
LLM Observability is one module inside Datadog's broader platform, sharing the same agents, dashboards, and alerting as its APM, infrastructure, and log products.
It auto-detects and traces LLM calls, surfaces token usage and cost, and runs quality and safety evaluations, with everything correlated to the underlying services so you can trace a slow agent response down to the host or database behind it.
Pricing: Datadog's pricing lists a free plan for LLM Observability (up to 40,000 LLM spans per month with 15-day retention), but it is only available with Datadog's 14-day free trial, not as an evergreen free tier.
Strengths:
Datadog LLM Observability is best for...
Enterprises already standardized on Datadog who want LLM traces correlated with the rest of their application and infrastructure telemetry.
No. Some only offer a free trial (usually 14 to 30 days) that then converts to a paid plan or a hard downgrade.
A real free tier is different: you can keep using the product indefinitely within published limits on events, spans, seats, and retention. The most generous belong to PostHog (100,000 events a month with unlimited seats) and Langfuse (50,000 units a month with full feature access), with HoneyHive, Braintrust, Lunary, and self-hosted Phoenix also worth a look.
A few plans sit in between; they're technically free, but tight enough that most teams outgrow them fast, like Traceloop's 24-hour retention or LangSmith's single-seat cap.
For tools you can run yourself at no cost, see our guide to the best free open source LLM observability tools.
LLM observability focuses on what happened in production: traces, costs, latency, error rates, and user behavior.
AI evaluation focuses on whether what happened was good: scoring responses for quality, factual accuracy, safety, or task completion.
You usually want both, and roughly in that order – evaluation only starts making sense once you can actually see the outputs you're scoring. For the longer version, see our explainer on what AI observability is and how it works.
PostHog leads with 100,000 AI observability events per month and unlimited team members on its free plan.
Langfuse follows at 50,000 units per month with full feature access.
Yes. Langfuse, Traceloop, Phoenix, and PostHog all offer open-source cores.
See our guide to open source LLM observability tools for a feature-by-feature comparison.
PostHog, Lunary, and Langfuse are strong $0 options for side projects.
Pick PostHog if you also need product analytics, web analytics, error tracking, and session replay.
For a fuller walkthrough of what to instrument first, how to tie LLM data to product analytics, and when a free tier stops being enough, see our guide to AI observability for your MVP.
None of the active evergreen free tiers in this guide require a card to start, including PostHog, Langfuse and LangSmith.
You only need a card when you upgrade past free limits or start a paid trial like Datadog.
Each tool handles it differently. PostHog switches to usage-based billing automatically. You keep working and pay only for usage above the free allowance. PostHog also lets you set billing limits per product so you do not get surprise overages.
Langfuse stops accepting new data on Hobby once you hit 50K units and requires a plan upgrade.
Lunary restricts your account if you exceed limits for two consecutive days but continues capturing data in the background.
The OpenTelemetry specification and SDKs are free and open source. Traceloop's OpenLLMetry SDK is a free Apache 2.0 instrumentation layer that sends traces to any OTEL-compatible backend. The backend is where costs appear: self-hosted Phoenix or Jaeger are free to run. Cloud backends charge for ingestion and storage. Langfuse and PostHog both accept OTEL-compatible trace data if you want a managed backend with a genuine free tier.
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