How do their growth rates compare?

Fact0 has a month-over-month growth rate of 0%; Miro has +1.7%.

How does pricing compare?

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. Miro: Freemium, Contact for Pricing. Freemium with three paid paths. Free covers small teams with 3 editable boards, templates, and core formats. Starter runs about $8 per member/month billed yearly with unlimited private boards and 25 AI credits per member. Business is about $20 per member/month billed yearly with workspaces, prototyping, and AI workflows. Enterprise is quoted on request with SSO, governance, and dedicated support.

What are alternatives to Fact0 and Miro?

Other tools in related categories include Notion, Hermes-Agent, Clawdbot (OpenClaw), MindStudio, Whimsical.

Fact0 vs Miro — Traffic (0 vs 30.75M), Pricing & Verdict

Open AI Tools

Fact0 vs Miro — Traffic (0 vs 30.75M), Pricing & Verdict | Open AI Tools

Side-by-Side Traffic & Specification Matrix

Dimension / Metric	Fact0	Miro
Official Domain	fact0.io	miro.com
Pricing Model	Open Source (MIT); self-hosted, no subscription fees	Freemium, Contact for Pricing
Monthly Visits	0 (0)	30.75M (30,749,206)
MoM Traffic Delta	0 (0%)	+505.1K (+1.7%)
Primary Market & Share	🌐 Global (—)	🇺🇸 United States (19.4%)
Direct Visits / mo	0	20.18M
User Rating	4.5	4.5
Supported Platforms	Website	Website

Fact0

Self-hosted audit history and execution replay for AI agents

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.

Strengths & Pros

+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