Free / Open-sourceOpen-source workflow automation built for AI agents and technical teams
#1Arize AI records 249,028 monthly visits with +3.2% month-over-month growth and Paid, Contact for Pricing pricing. Phinite records 1,272 monthly visits with -80.3% growth and Freemium pricing.

Operating system for designing, deploying and governing multi-agent AI
| Dimension / Metric | ![]() | ![]() |
|---|---|---|
| Official Domain | arize.com | phinite.ai |
| Pricing Model | Paid, Contact for Pricing | Freemium |
| Monthly Visits | 249.03K (249,028) | 1.27K (1,272) |
| MoM Traffic Delta |
| +7.67K (+3.2%) |
| -5.17K (-80.3%) |
| Primary Market & Share | 🇺🇸 United States (51.6%) | 🇺🇸 United States (100%) |
|---|
| Direct Visits / mo | 107.84K | 509 |
|---|
| User Rating | 4.6 | 4.2 |
|---|
| 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.

Operating system for designing, deploying and governing multi-agent AI
Design multi-agent workflows on a visual canvas with conditional branching, parallel paths and human-in-the-loop checkpoints, producing a graph the team can read, review and version.
Build the tools, skills and integrations agents call as reusable, versioned components that are tested before they reach a production workflow.
Ship the same graph to dev, UAT and production with every release versioned, so a bad change rolls back in one step instead of becoming an incident.
Traces follow each step across agents and tools, with latency and spend attributed per agent so teams can see what ran, why and what it cost.
Free / Open-sourceOpen-source workflow automation built for AI agents and technical teams
#1An all-in-one AI super assistant for professionals and enterprises
No-code builder for custom AI agents and workflows

Free / Open-sourceBuild, deploy, and govern production-ready agentic AI workflows

Free / Open-sourceOpen-source desktop AI workforce that runs browser, file, and app tasks for you

Excels at experiment tracking and model registry workflows, but is less purpose-built for real-time LLM production observability.


A secure environment where teams build, deploy and govern AI agents, apps and automations

Build, automate and publish AI agents without starting from scratch

Free / Open-sourceIncludes integrated guardrails and simulation tools, but is less mature for large-scale enterprise production telemetry monitoring.

Free / Open-sourceEvaluate, monitor, and govern your AI applications in production

Focuses heavily on prompt management and versioning control, whereas Arize prioritizes end-to-end tracing and ML observability.

Offers comprehensive LLM evaluation and unit testing via DeepEval, but lacks Arize's deep production monitoring infrastructure.
Free / Open-sourceOpen-source workflow automation built for AI agents and technical teams
An all-in-one AI super assistant for professionals and enterprises
No-code builder for custom AI agents and workflows
Free / Open-sourceBuild, deploy, and govern production-ready agentic AI workflows
Excels at experiment tracking and model registry workflows, but is less purpose-built for real-time LLM production observability.
A secure environment where teams build, deploy and govern AI agents, apps and automations
Free / Open-sourceIncludes integrated guardrails and simulation tools, but is less mature for large-scale enterprise production telemetry monitoring.
Free / Open-sourceEvaluate, monitor, and govern your AI applications in production
Focuses heavily on prompt management and versioning control, whereas Arize prioritizes end-to-end tracing and ML observability.
Offers comprehensive LLM evaluation and unit testing via DeepEval, but lacks Arize's deep production monitoring infrastructure.