# Arize AI vs Fact0

Arize AI records 249,028 monthly visits with +3.2% month-over-month growth and Paid, Contact for Pricing pricing. Fact0 records 0 monthly visits with 0% growth and Open Source (MIT); self-hosted, no subscription fees pricing.

| Metric | [Arize AI](https://openaitools.app/tool/arize-com) | [Fact0](https://openaitools.app/tool/fact0) |
| :--- | :--- | :--- |
| Website URL | https://arize.com | https://fact0.io |
| Categories | Large Language Models (LLMs), AI Agent, AI Monitor, AI Developer Tools | AI Agent |
| Pricing Model | Paid, Contact for Pricing | Open Source (MIT); self-hosted, no subscription fees |
| Monthly Visits | 249,028 | 0 |
| MoM Growth Rate | +3.2% | 0% |
| Primary Market | United States | Global |
| Direct Visits | 107,838 | 0 |
| Review Score | 4.6 | 4.5 |
| Saved Count | 1 | 0 |
| Supported Platforms | Website | Website |

## Arize AI

Arize is an AI engineering platform built for teams that run generative AI in production. It is split into two products: Arize AX, a managed environment for agent observability, evaluation, and experimentation, and Phoenix, an open-source tracing and evaluation library that teams can self-host when they need full data control. Both rely on OpenInference, the open-source semantic convention for generative AI tracing that the Arize team founded and that is built on OpenTelemetry.

The workflow follows three questions. Observe answers what an agent actually did by capturing every span, trace, and session; Evaluate answers whether quality is improving or regressing using span-level, trace-level, and session-level evals; Learn answers whether a proposed change will help by letting teams test prompts and harnesses before rollout. A datastore for GenAI traces (ADB) keeps data in open formats that connect to warehouses such as BigQuery, Databricks, and Snowflake, and an in-app AI engineering agent named Alyx surfaces issues and drafts review-ready fixes.

The platform integrates with dozens of models, frameworks, and tools, including OpenAI, Anthropic, Google, Amazon Bedrock, LangChain, LangGraph, LlamaIndex, CrewAI, and DSPy. Arize publishes compliance certifications including SOC 2 Type II, ISO 27001, PCI DSS, HIPAA, and GDPR, and supports cloud, hybrid, and self-hosted deployment.

**Pricing & Plans:** Arize uses a paid model with a free entry point: teams can start free or book a demo, while enterprise-scale usage, deployment options, and support are quoted through a contact-sales process. The open-source Phoenix project is free to self-host.

- **End-to-end tracing:** Capture spans, traces, and sessions across agents and LLM calls using OpenInference conventions built on OpenTelemetry, so instrumentation stays portable.
- **Scalable evaluation framework:** Run evaluations at span, trace, and session level to grade retrieval quality, tool use, and final answers across large production datasets.
- **Alyx AI engineering agent:** An in-product agent that inspects traces, diagnoses likely root causes, and proposes fixes that engineers can review before shipping.
- **Phoenix open-source toolkit:** Self-host the leading open-source tracing and eval tool when you need to keep trace data inside your own infrastructure.
- **Warehouse-native trace storage:** ADB stores traces in open formats that connect natively to BigQuery, Databricks, or Snowflake for joint analytics with business data.

**Strengths:** Covers the full loop of observe, evaluate, and improve rather than stopping at dashboards; Backed by OpenInference and OpenTelemetry standards, limiting vendor lock-in on trace formats; Strong enterprise posture with SOC 2, ISO 27001, HIPAA, and GDPR coverage plus self-hosting options; Integrates with a very wide set of models, frameworks, and agent SDKs

**Limitations:** Self-serve pricing is not published, so cost requires a sales conversation; The breadth of features can be overwhelming for small teams shipping their first LLM feature; Full value depends on disciplined instrumentation across the whole application

[Profile (Markdown)](https://openaitools.app/tool/arize-com.md)

## Fact0

Fact0 addresses a practical gap in agent engineering: once an AI agent runs autonomously, it is hard to say precisely what it did. Fact0 installs locally via Docker Compose (a Go API, a Next.js dashboard and PostgreSQL) and creates one owner and one workspace without any cloud account. You then instrument your agent through the Python SDK or connect the Claude Code plugin, and every supported span — prompts, tool calls, tool results, assistant text, handled failures — is written to your own database.

Inspection is a first-class feature. A graph view shows the path through a run, a timing waterfall shows ordering, and each recorded step exposes the captured value beside it so you can expand, copy or download the raw content. Replay reconstructs saved execution events at your own pace; importantly, replay replays the record and does not re-run the agent, so you are always looking at what genuinely happened.

Fact0 is deliberately honest about its limits. It is experimental software under best-effort maintenance, with one owner and one workspace in the current release. Audit verification proves that the checked records are consistent with their hashes and preceding links, and export verification checks signed file bytes against a separately trusted public key — but neither proves complete capture, truthful source data or protection from an administrator replacing the whole history. Missing or unsupported content is marked partial or unavailable rather than silently omitted.

**Pricing & Plans:** Free and open source under the MIT license. You self-host with Docker Compose, so there are no Fact0 subscription fees or per-seat pricing; you only bear your own infrastructure costs. There is no hosted billing or team-invitation offering, and no SLA.

- **Local-first capture:** Run the API, dashboard and PostgreSQL in your own environment via Docker Compose, then point your agent at the local instance with an API key. Data and signing keys stay with your installation.
- **Two entry points, one history:** Record Python agents through the SDK (execution spans, tool results, supported model content, audit events) and Claude Code sessions through the collector plugin, then inspect both in the same workspace.
- **Execution graphs and replay:** Follow a run through a graph, a timing waterfall and recorded spans, then step through saved events at your own pace. Replay reconstructs the record without re-running the agent.
- **Hash-chained audit records:** Check the consistency of stored events against their hashes and preceding links, with capture gaps reported visibly rather than hidden.
- **Signed evidence exports:** Download authenticated PDF reports and evidence ZIPs, and verify the signed PDF and verification JSON against a separately trusted instance public key.

**Strengths:** Open source under the MIT license and fully self-hosted, so sensitive prompts and source code never leave your infrastructure; Unusually transparent documentation that states capture limitations and what audit verification actually proves; Docker Compose setup with a one-owner, one-workspace model makes getting started quick and scriptable; Covers both Python agents and Claude Code in a single inspection workspace

**Limitations:** Explicitly experimental with best-effort maintenance, so it is not suited to production-critical reliance; Not a hosted service: no cloud account, no team invitations, no SLA, and none of the operational support a managed product provides; Capture completeness depends entirely on your instrumentation and supported hooks; absent content is only marked partial or unavailable

[Profile (Markdown)](https://openaitools.app/tool/fact0.md)

## Frequently Asked Questions

### How do Arize AI and Fact0 compare in monthly traffic?

Arize AI has 249,028 monthly visits, while Fact0 has 0. Traffic estimates describe audience size, not product quality.

### How do their growth rates compare?

Arize AI has a month-over-month growth rate of +3.2%; Fact0 has 0%.

### How does pricing compare?

Arize AI: Paid, Contact for Pricing. Arize uses a paid model with a free entry point: teams can start free or book a demo, while enterprise-scale usage, deployment options, and support are quoted through a contact-sales process. The open-source Phoenix project is free to self-host. Fact0: Open Source (MIT); self-hosted, no subscription fees. Free and open source under the MIT license. You self-host with Docker Compose, so there are no Fact0 subscription fees or per-seat pricing; you only bear your own infrastructure costs. There is no hosted billing or team-invitation offering, and no SLA.

## Alternatives

| # | Tool Name | Monthly Visits | MoM Growth Rate | Pricing Model | Summary | Profile (Markdown) |
| :--- | :--- | :--- | :--- | :--- | :--- | :--- |
| 1 | [Notion](https://openaitools.app/tool/t) | 173,441,670 | +0.1% | Freemium | The AI workspace where teams and agents get work done together | [.md](https://openaitools.app/tool/t.md) |
| 2 | [Hermes-Agent](https://openaitools.app/tool/hermes-agent-1) | 3,638,741 | -5.4% | Freemium | A free, open-source personal AI agent that learns your projects and works across every messaging app. | [.md](https://openaitools.app/tool/hermes-agent-1.md) |
| 3 | [Clawdbot (OpenClaw)](https://openaitools.app/tool/clawdbot) | 1,903,068 | -25.4% | Paid, Free | Open-source personal AI assistant that runs on your own machine and works from chat apps | [.md](https://openaitools.app/tool/clawdbot.md) |
| 4 | [MindStudio](https://openaitools.app/tool/mindstudio) | 1,793,337 | +14.2% | Contact for Pricing | No-code builder for custom AI agents and workflows | [.md](https://openaitools.app/tool/mindstudio.md) |
| 5 | [Eigent AI](https://openaitools.app/tool/eigent-ai) | 575,703 | +1.4% | Freemium, Free Trial, Contact for Pricing | Open-source desktop AI workforce that runs browser, file, and app tasks for you | [.md](https://openaitools.app/tool/eigent-ai.md) |
| 6 | [Weights & Biases (CoreWeave Forge)](https://openaitools.app/tool/prompts) | 315,624 | -80.2% | Freemium, Contact for Pricing, Free | Excels at experiment tracking and model registry workflows, but is less purpose-built for real-time LLM production observability. | [.md](https://openaitools.app/tool/prompts.md) |
| 7 | [Sim AI](https://openaitools.app/tool/sim-ai) | 140,953 | +20.6% | Freemium, Paid (per user/month), and Enterprise with custom pricing; self-hosting free under Apache 2.0 | Open-source workspace to build, deploy and govern AI agents | [.md](https://openaitools.app/tool/sim-ai.md) |
| 8 | [Agno](https://openaitools.app/tool/agno) | 140,302 | -3.3% | Contact for Pricing, Free | Self-driving agent platform you run in your own cloud | [.md](https://openaitools.app/tool/agno.md) |
| 9 | [Future AGI](https://openaitools.app/tool/future-agi) | 121,741 | -7.8% | Freemium, Usage-based | Includes integrated guardrails and simulation tools, but is less mature for large-scale enterprise production telemetry monitoring. | [.md](https://openaitools.app/tool/future-agi.md) |
| 10 | [Maxim AI](https://openaitools.app/tool/maxim-ai) | 121,351 | +10% | Contact for Pricing, Free Open Source | Features strong testing and prompt evaluation tools, though its monitoring interface is less oriented towards massive telemetry streams. | [.md](https://openaitools.app/tool/maxim-ai.md) |

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Source: https://openaitools.app/compare/arize-com-vs-fact0

More Markdown: [Categories](https://openaitools.app/categories.md) · [Traffic rankings](https://openaitools.app/rankings.md) · [llms.txt](https://openaitools.app/llms.txt)