TraceFlow is distributed tracing built for backend teams — follow a request across every service, span, and query, and know exactly where your latency lives.
Six tools that share one trace model, so context never gets lost between them.
Follow a single request across every service boundary, queue, and database call — no more guessing where time went.
Live throughput, error rate, and latency for every route, refreshed in real time.
P50 to p99 percentiles, grouped by service, region, or deploy — spot regressions before your users do.
Wrap any function, job, or query with one line and see it appear in the trace tree instantly.
Every exception linked back to the exact trace, span, and payload that caused it.
Share a trace like a link. Annotate spans, tag teammates, resolve incidents together.
Every stage below is a real span TraceFlow captures automatically.
Drop the SDK into any Node, Python, or Go service. Auto-instruments your framework, database driver, and queue client — custom spans take one line.
▍Every panel below is looking at the same request.
“We replaced four dashboards with one trace view. Time-to-root-cause on incidents dropped from an hour to under ten minutes.”
“The SDK auto-instruments our whole Go stack. Custom spans for the weird internal jobs took an afternoon, not a sprint.”
“TraceFlow is the first observability tool our on-call rotation actually opens before Slack.”
For side projects finding their first users.
For teams running production workloads.
For orgs with compliance and scale needs.
TraceFlow is built trace-first: every panel, alert, and chart is a view into the same span data, so you never lose context switching between tools.
Yes. TraceFlow can ingest OTLP directly, or you can use our SDK for automatic framework and driver instrumentation.
5ms on average per instrumented request, measured across our production customer base.
Enterprise plans support a dedicated ingest pipeline in your own VPC.