Tracekit gives AI the context to debug production
Connect traces, LLM calls, alerts, sessions, and dynamic logs. Ask from Tracekit or your MCP-enabled assistant, then get answers grounded in the telemetry behind the incident.
LLM spend attributed
$262.98
LLM calls analyzed
1,849
Tokens traced
64.5M
Top cost driver
analytics-service
Cost over time
Grouped by model for the last 7 days
Analytics-service is the cost driver.
Tracekit found that 163 long-context gpt-4o calls from analytics-service account for $232.74 of weekly LLM spend. One recent report request used 630K tokens and took 11.2 seconds.
Service
analytics-service
$232.74
Model
gpt-4o
$244.88
Call
630K tokens
11.2s
Recommended next step: cache the report prompt, summarize upstream, or move lower-risk summaries to gpt-4o-mini.
AI investigation
The answer is only useful when the evidence is attached.
Dashboards still matter, but developers are starting investigations inside AI assistants. Tracekit gives those assistants the missing layer: production context they can reason over without guessing.
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.
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.
LLM cost investigation
Last 7 days, grouped by model and service
Model mix
Evidence returned
- Service path
- Trace and span IDs
- Provider latency
- Token breakdown
MCP for AI tools
Bring production telemetry into the assistant developers already use.
The MCP server is not a side quest. It is how Tracekit makes observability useful inside the AI-assisted debugging workflow, with live traces and service context available on demand.
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.
What developers are saying about Tracekit
Unfiltered feedback from engineers using Tracekit to understand production issues, LLM calls, and AI-generated code.
Platform
The observability signals an AI debugger needs
Tracekit does the serious observability work first. AI becomes useful because the data is connected, current, and inspectable.
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.
Open standards first
OpenTelemetry-native ingestion keeps teams from locking production truth inside one vendor format.
Built for small teams
Start with useful defaults, free traces, and AI credits without needing a dedicated observability owner.
Designed for operators
Keyboard-friendly workflows, inspectable evidence, and direct links into the trace or replay behind an answer.
Give your AI assistant real production context
Start free with 200K traces/month, 100 AI credits, alerting, LLM monitoring, and enough context to debug the next incident with evidence.
Free forever plan. Upgrade when your services and usage grow.