Production debugging with evidence

Find what broke before users get stuck

Tracekit connects traces, errors, sessions, LLM calls, alerts, and dynamic logs. Ask what changed, where it failed, and what to fix next.

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Tracekit Code Monitoring showing a live production variable diff without pausing the application.

Tracekit Code Monitoring showing live production variables and an inspector diff without pausing the application.

Incident investigation

Ask what broke. Get the evidence.

Dashboards still matter, but production incidents need a direct path to the cause. Tracekit gives your team and AI tools the context behind the error, trace, session, and service.

Collect the context

Traces, LLM calls, sessions, alerts, releases, and dynamic logs stay connected to the same service and request path.

Ask from the workflow

Use Tracekit Copilot or your MCP-enabled AI tool to ask what changed, which service regressed, or why spend moved.

Return evidence

Answers cite services, spans, token usage, latency, recent calls, and the exact telemetry behind the recommendation.

Act with confidence

Jump into the trace, replay, alert rule, dynamic log capture, or owner while the investigation is still fresh.

Live Copilot investigation

Tracekit Copilot answering why order requests are failing with the root cause and affected services.

LLM observability

AI spend is easier to fix when it is tied to traces.

Tracekit records model, provider, token count, latency, service, and trace context for LLM calls. That turns a billing spike into a concrete engineering question: which workflow created it, and what changed?

Every LLM call lands as a span inside the originating trace.
Cost, tokens, and latency roll up by service, model, and provider.
Spikes link straight to the calls and prompts that caused them.

Tracekit LLM observability showing model cost, call volume, latency, token usage, and cost trends.

MCP for AI tools

Ask your AI tool what broke in production.

The Tracekit MCP server lets Claude, Cursor, and other assistants inspect real traces, errors, services, and LLM calls instead of guessing from a screenshot.

Connect once

Expose Tracekit telemetry through an MCP endpoint secured by your account.

Ask naturally

Ask about service health, slow traces, LLM usage, alert history, or dynamic log captures.

Inspect proof

Open the trace, call detail, runbook, or replay that supports the assistant answer.

MCP demo

Production context

Why developers love Tracekit

What developers are saying about Tracekit

Unfiltered feedback from engineers using Tracekit to understand production issues, LLM calls, and AI-generated code.

Platform

The signals you need to find the cause

Tracekit keeps the important details connected, so alerts, traces, sessions, LLM calls, and dynamic logs point to the same problem.

LLM observability

Attribute OpenAI and Anthropic cost, latency, token volume, and model usage to the services making the calls.

MCP server

Give Claude, Cursor, and compatible assistants access to live production context without copying dashboard screenshots.

Distributed tracing

Follow requests across services with OpenTelemetry-native spans, waterfalls, latency, errors, and service health.

Dynamic logs

Capture variable state from running production code without redeploying or asking engineers to add another log line.

Session replay

See the frontend behavior that produced a backend trace, error, or failed checkout path.

Alerts and incidents

Connect anomaly detection, alert history, ownership, and incident replay to the same evidence trail.

Instrument once

Tracekit speaks the stack developers already run.

Use Tracekit SDKs or send OpenTelemetry data directly. The goal is not another proprietary island. It is clean telemetry that AI tools can query when production gets confusing.