Full visibility into every request, every step.

In a point-to-point architecture, debugging a slow or incorrect response means correlating logs from every service that could have been involved — different log formats, different timestamps, different teams. What should take ten minutes takes half a day.

Every request that passes through Apitide produces a single execution timeline: which steps ran, in what order, how long each took, what data was passed in and returned, whether any retries occurred, and what the final composed response looked like. One timeline, one place, complete picture.

Execution timelines are searchable and filterable. Find all executions where the inventory service took over 200ms. Find all executions where a particular customer ID produced a partial failure. Drill into any execution to see the exact request and response at each step — the same level of detail that would normally require access to each service's internal logs.

Alerts in Apitide are defined as conditions on workflow metrics — when p95 latency exceeds a threshold, when error rate spikes above a percentage, when a specific upstream service starts failing. Each alert is evaluated on a configurable time window and triggers after a configurable number of consecutive violations, reducing false positives from transient spikes.

Notifications route to Slack channels, email addresses, or any HTTP endpoint — including PagerDuty, OpsGenie, and other on-call platforms. Each notification includes the metric value, the threshold, the time window, and a direct link to the relevant execution timeline entries.

Alert rules are workflow-scoped. The alert on your checkout flow has a tighter latency SLO than the alert on your admin dashboard. The alert on your payment-adjacent workflows routes to the payments on-call rotation. Alerts are not an afterthought — they are configured in the same place as the workflow itself.

When a workflow is modified and deployed, Apitide compares the new version to the previous one and generates a human-readable summary of what changed: which steps were added or removed, which transformation logic changed, which retry policies were modified, which test cases were added or updated.

Release notes are shareable — send them to the team Slack channel, attach them to a PR, or include them in your changelog. Engineers who didn't make the change can understand what changed and why without reading workflow diffs.

The generated notes also surface test suite changes: which scenarios now pass that previously failed, which new scenarios were added, and whether any existing assertions changed. This gives reviewers a complete picture of the intent and impact of a workflow change.