Self-hosted observability for AI agents, tracing runs with full data ownership.
Product memo
AI developers use Tracea to debug and remember agent runs, keeping all data on their own infrastructure. It traces LLM calls, tool calls, and cost spikes, offering AI-powered root cause analysis directly within the user's environment. This approach appeals to teams prioritizing data privacy and control over their AI workflows, avoiding the typical vendor lock-in of cloud-based SaaS tools.
For who
AI developers and teams
Solves what
Debugging and remembering AI agent runs with full data ownership.
- Trace LLM and tool calls
- Local RCA and alerts
- Self-hosted observability
In their own words
Know exactly why
your agents failed.
Trace every LLM call, tool call, cost spike, error, and decision path, then turn finished sessions into searchable team memory.
Commercial cues
Model
usage based
Free tier
Yes
Trial
No
Pricing Strategy
- • Usage-based pricing aligns costs directly with AI agent session activity.
- • Self-hosting removes vendor lock-in, serving data-sensitive developers.
- • Free tier lowers testing friction.
Operator context
Team
Indie / lean
Founded
May 2026
Platform
Web app
Audience
Developers
Public footprint
Tech stack
Builder Strategy
- Strategy Type
- Open Source Commercial
- Stage
- Bootstrapped Lean
- Effort
- Small Team
About Tracea Expand
Tracea delivers a self-hosted observability product specifically for AI developers and teams building AI agents. It focuses on giving users full data ownership by running entirely on their infrastructure, a key differentiator from many cloud-based alternatives.
The platform traces every LLM call, tool call, cost spike, and error, turning completed sessions into searchable team memory. This open-source commercial approach helps developers debug complex agent behaviors while maintaining control over their sensitive AI data and avoiding vendor lock-in.