Cheapest AI observability tools for developers, compared

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.

What features do you need in an AI observability tool?

At a minimum, a useful AI observability tool should:

• Capture LLM traces with input, output, latency, and token counts
• Track cost per model and per user
• Visualize aggregate metrics (p50/p99 latency, error rates, total spend)
• Support the LLM providers you actually use (OpenAI, Anthropic, and others)
• Offer free access without requiring a credit card

The best tools go further with:

• Prompt management: Version prompts without redeploying code
• Evaluations: Score outputs with LLM-as-judge, human annotations, or automated metrics
• User and session context: Tie model behavior to product analytics, session replay, or feature flags
• Dataset curation: Build golden datasets from production traces
• Self-hosting: Keep all data in your own infrastructure for privacy or compliance

AI observability tools with the best free tiers

1. PostHog

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:

• Every LLM event is a standard PostHog event, so it's all available to query with SQL, add to dashboards, and set up alerts on
• Spend tracking down to cost per conversation, p95/p99 latency views, and LLM errors auto-captured in error tracking
• No per-seat pricing, usage-based billing with spend limits you set, and free credits for early-stage companies via PostHog for Startups

Install PostHog with one command

Paste this into your terminal and make AI do all the work.

npx @posthog/wizard

Learn more

2. Langfuse

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:

• Tracing, prompts, datasets, evals, and annotation queues in one mature product
• Python and JS SDKs, OpenTelemetry, and most major agent frameworks supported
• Self-host core Langfuse features in your own infra for free

3. Traceloop (OpenLLMetry)

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:

• OpenTelemetry-first, so it fits teams that already standardize on OTel
• 50K free spans a month rivals Langfuse on raw volume
• Not locked into Traceloop's UI if you self-route telemetry

4. Arize (Phoenix)

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:

• Strong OTel and framework integrations from the Phoenix OSS project
• Online evals on the free tier, with more eval depth than many hobby plans
• Upgrade path to enterprise ML observability if you later need ML and CV tooling beyond LLMs

5. Lunary

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:

• Simple, one-line integration to get started
• Built-in prompt management
• Conversation threading and end-user feedback capture for chat and RAG apps

6. HoneyHive

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:

• OTel-native, with 50+ library integrations including LangChain and the OpenAI Agents SDK
• 5 users on the free tier, better for tiny teams than single-seat hobby plans
• CI/CD integration to run automated quality checks in your deployment pipeline

7. LangSmith

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:

• SmithDB trace queries for sub-second lookups across millions of traces at scale
• Deep LangChain integration across tracing, evals, prompt hub, and deployment tooling
• Natural fit if you deploy agents on LangGraph and LangSmith infrastructure

8. Braintrust

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:

• Unlimited users on the free plan, rare in this list
• Scores-and-evals-first UX, strong for teams measuring output quality
• SOC 2 Type II and multi-factor authentication (MFA) on the free tier

9. Datadog LLM Observability

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:

• LLM spans correlate with host metrics, traces, and logs in one platform
• Generous 40,000 LLM span allowance during the 14-day trial
• Mature alerting, dashboards, and on-call tooling teams already run

Which AI observability tool should you choose?

• Want one tool that survives the jump from side project to real product? PostHog – 100K events/month free, no per-seat fees, and the only option here that can show you the session replay of the actual user behind a broken trace.
• Want the deepest pure-play LLM platform? Langfuse.
• Living inside LangChain or LangGraph? LangSmith.
• Shipping production agents on a tiny team? HoneyHive.
• Want vendor-neutral instrumentation you can route anywhere? Traceloop.
• Want zero licensing cost and no caps? Phoenix.
• Treating evals as product infrastructure? Braintrust.
• Want the simplest single-purpose free tier? Lunary.

Recommendations by team type

For solo developers and side projects

• PostHog for 100K free LLM events a month, no per-seat cost, and multiple tools behind the same login
• Lunary for the lightest setup: one-line integration, conversation threading, and 1,000 prompt queries a month for a single chatbot or RAG project
• Phoenix self-hosted for zero licensing cost and no event cap when you're happy owning the infrastructure

For early-stage startups

• PostHog for one platform from prototype to PMF: AI traces sit next to experiments, error tracking, flags, and analytics, plus free credits for qualifying companies via PostHog for Startups
• HoneyHive for up to 5 seats on the free tier and collaborative evaluation workflows once more than one person is grading outputs
• Langfuse when the team lives in Python notebooks and agent repos and wants the deepest open-source prompt and eval tooling

For teams building multi-step agents

• LangSmith for the deepest tracing if you're on LangChain or LangGraph: node-by-node state diffs and full execution graphs
• HoneyHive for OTel-native agent traces and evals you can run online against live traffic
• Braintrust when you want every agent run scored on correctness, safety, and efficiency

For evals-first teams

• Braintrust when evaluation is core product infrastructure, with scoring and experiments front and center (unlimited users on the free plan)
• PostHog for LLM-as-judge and code-based evals that run automatically after a prompt or model change
• Langfuse for LLM-as-judge, human annotation queues, and dataset experiments in an open-source platform

For enterprises with compliance needs

• Datadog when you already run their platform and want LLM traces beside infrastructure and APM telemetry (expect per-host platform pricing)
• PostHog for CDP, data warehouse, and AI observability under one vendor, with self-host and EU hosting for data residency

Install PostHog with one command

Paste this into your terminal and make AI do all the work.

npx @posthog/wizard

Learn more

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