Built with AI. Debugged with Tracekit.
Whether you vibe-coded your MVP with Claude or manage a fleet of microservices, Tracekit gives you the observability layer that AI can't generate from a prompt alone.
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The observability platform for the AI coding era
AI can write your app in minutes. But when it breaks in production, you still need to understand what happened. Tracekit bridges the gap between AI-generated code and production reliability.
One prompt. Full observability.
Tell your AI assistant “Set up Tracekit tracing in my project” and it handles everything -- SDK install, configuration, and instrumentation. Works with Claude, ChatGPT, Cursor, Windsurf, and any AI coding tool.
$ npx skills add tracekit-dev/tracekit-for-ai
Skill installed successfully
# Then tell your AI assistant:
"Set up Tracekit tracing in my Node.js project"
# AI configures everything:
Installing @tracekit/node-apm...
Configuring tracing for Express...
Adding error boundaries...
Done. Traces will appear in your dashboard.
Shipped a SaaS in a weekend with AI
Built the entire backend with Claude and Cursor. Tracekit was the safety net -- AI set up tracing in 30 seconds with a single prompt. When the AI-generated payment webhook broke at 2am, live debugging showed the exact variable state without touching the code.
AI agent with 12 tool calls per request
Our AI agent makes chained LLM calls, database lookups, and API requests. Without tracing, debugging a failed chain was impossible. Now every agent run is a trace -- we see which tool call failed, what the LLM returned, and how long each step took.
Vibe-coded MVP, production-grade observability
I don't write observability code. I told Claude 'add Tracekit to my Express app' and it was done. When users reported slow pages, I searched traces with natural language instead of writing database queries. Found a missing index in 2 minutes.
From zero to monitored in 60 seconds
Copied the AI setup prompt from the onboarding page, pasted it into Cursor, and my entire Django app was instrumented before my coffee was ready. Session replay caught a CSS bug I never would have found from server logs alone.
How engineering teams use Tracekit
From solo founders to growing teams.
Faster incident resolution
“We went from 45-minute MTTR to under 10 minutes. Live debugging means we don't waste time adding log statements and redeploying.”
Full-stack visibility
“Having traces, errors, and session replay in one tool eliminated our context-switching between Sentry, Datadog, and FullStory.”
Team-friendly pricing
“Flat $29/month means we can give every engineer access without worrying about per-seat costs eating our budget.”
Why AI-built apps need Tracekit
AI generates code fast. But AI-generated code has unique observability challenges.
You didn't write the code
When AI-generated code breaks, you can't debug from memory. You need traces that show exactly what happened, line by line.
LLM calls are black boxes
AI agent chains make dozens of API calls per request. Without tracing, a failed chain is impossible to debug.
You ship faster than ever
AI lets you ship in hours, not weeks. But speed without observability is just shipping bugs faster. Tracekit keeps you safe.
What developers are saying
Unfiltered feedback from the Tracekit community.
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No credit card required. Free tier includes 200K traces/month with unlimited spans and metrics.