Arize AI records 249,028 monthly visits with +3.2% month-over-month growth and Paid, Contact for Pricing pricing. Laminar records 0 monthly visits with 0% growth and Freemium pricing.
Agent observability, evaluation, and continuous improvement for production AI
Open-source agent observability that catches failures before your users do
| Dimension / Metric | | |
|---|---|---|
| Official Domain | arize.com | lmnr.ai |
| Pricing Model | Paid, Contact for Pricing | Freemium |
| Monthly Visits | 249.03K (249,028) | 0 (0) |
| MoM Traffic Delta | +7.67K (+3.2%) | 0 (0%) |
| Primary Market & Share | 🇺🇸 United States (51.6%) | 🌐 Global (—) |
| Direct Visits / mo | 107.84K | 0 |
| User Rating | 4.6 | 4.7 |
| Supported Platforms | Website | Website |
Agent observability, evaluation, and continuous improvement for production AI
Capture spans, traces, and sessions across agents and LLM calls using OpenInference conventions built on OpenTelemetry, so instrumentation stays portable.
Run evaluations at span, trace, and session level to grade retrieval quality, tool use, and final answers across large production datasets.
An in-product agent that inspects traces, diagnoses likely root causes, and proposes fixes that engineers can review before shipping.
Self-host the leading open-source tracing and eval tool when you need to keep trace data inside your own infrastructure.
Open-source agent observability that catches failures before your users do
Automatically records LLM calls, tool calls, sub-agents, tokens and cost, then shows them as a readable transcript with a cost heatmap.
A custom analysis model reviews every run to surface failure modes you never defined, then clusters similar failures into patterns for fast triage.
Convert error clusters into eval datasets and re-run them after each release to catch regressions before they reach users.
Get notified about new failure clusters in Slack and ask the Laminar agent questions about your traces from the same channel.
Unified observability, security and incident response for cloud-scale engineering teams
Unified observability, security and incident response for cloud-scale engineering teams
Track, evaluate, and ship AI models and agents in one connected environment.
Track, evaluate, and ship AI models and agents in one connected environment.
Free / Open-sourceBuild, simulate, evaluate and monitor self-improving AI agents
Free / Open-sourceBuild, simulate, evaluate and monitor self-improving AI agents
Free / Open-sourceEvaluate, monitor, and govern your AI applications in production
Free / Open-sourceEvaluate, monitor, and govern your AI applications in production
The collaboration layer for AI engineering teams
The collaboration layer for AI engineering teams
Standardize LLM evaluation, observability, and red teaming across your whole org
Standardize LLM evaluation, observability, and red teaming across your whole org
Free / Open-sourceAgent observability, evals and governance for AI platform teams
Free / Open-sourceAgent observability, evals and governance for AI platform teams
SpecialistStronger focus on compliance enforcement and AI governance, but less specialized for deep developer tracing workflows.
SpecialistStronger focus on compliance enforcement and AI governance, but less specialized for deep developer tracing workflows.
Free / Open-sourceOpen-source observability that finds your AI agent's failures — and fixes them
Free / Open-sourceOpen-source observability that finds your AI agent's failures — and fixes them
Free / Open-sourceOpen-source workspace for building, evaluating and shipping AI agents
Free / Open-sourceOpen-source workspace for building, evaluating and shipping AI agents
Provides enterprise governance and model explainability features, but has a steeper setup curve for pure LLM tracing.
Provides enterprise governance and model explainability features, but has a steeper setup curve for pure LLM tracing.
Find and fix problems in your AI agents before users do
Find and fix problems in your AI agents before users do
