Full payload capture
Record complete request and response bodies, headers, auth, and timing from Kubernetes, ECS, desktop, or agent traffic. Not sampled. Not truncated. Everything your API testing needs.
You can't fix what you can't reproduce.
Validate AI-generated code with real production traffic before merge.
AI agents introduce bugs faster than your team can triage them. Speedscale captures the exact production request that broke, replays it in a sandbox, and hands your AI agent the real data to fix it.
No credit card required • 5-minute setup • 30-day free trial
Trusted by teams shipping AI-assisted releases
FLYR, Sephora, IHG, and platform teams worldwide use Speedscale to capture real production payloads and replay them against AI-authored changes.
The reproduction gap
Every engineer has been here. AI coding agents made it worse.
A failure appears in production. You can't reproduce it in staging. Your APM shows a trace but not the payload. The AI agent you ask to fix it has never seen what your system actually looks like under real load. Speedscale was built for exactly this, across your entire Kubernetes and API testing pipeline.
Without reproduction
A bug you can't reproduce is a bug you can't fix.
- APM tools capture error rates and traces, but not the request body, auth-related metadata, or upstream responses that explain why the failure actually happened.
- AI coding agents generate changes faster than any team can review them. The result: more PRs, more defects, and more time spent debugging code you didn't write.
- Staging can't replicate production state. Synthetic data misses edge cases. The request that broke things lived and died in production.
With Speedscale
Capture it once. Reproduce it forever.
- Record request and response payloads from production, including the headers, bodies, status codes, and timing you configure to capture, stored as reproducible snapshots.
- Replay the exact scenario that exposed a bug in a disposable sandbox. Give Claude Code, Cursor, or Codex the precise request it needs to understand and fix the regression.
- Engineers and AI agents can replay captured production failures on demand. No more 'works in prod, can't reproduce in staging.'
Reproduce the bug. Fix it. Ship with proof.
See exactly what each approach gives you when a production failure needs to be understood, reproduced, and fixed.
| Capability | Legacy APM | Static analysis | Speedscale |
|---|---|---|---|
| Captures the full request payload | Sampled traces. No request bodies. | No runtime data at all. | Complete payloads: headers, bodies, and auth-related metadata for captured calls. |
| Deterministic reproduction of failures | Fires an alert. No replay capability. | No runtime behavior. | Replay any production snapshot on demand. |
| Gives AI agents real data to fix bugs | Dashboard only. No coding context. | Diffs only. No production signal. | MCP-native context for Claude Code, Cursor, and Codex. |
| Catches behavioral regressions before merge | Detects after deploy. Customers see it first. | Syntax and types only. | Replays real traffic against every AI change before merge. |
The exact request that broke production. In your agent's hands in seconds.
Headers, body, auth tokens, query params. The full payload, not a sampled trace that lost the body somewhere in transit.
Replay it in a disposable sandbox against your change. No live dependencies, no flakiness, no guessing.
Your AI coding agent gets the actual request and response that triggered the failure, so it can fix the real problem instead of guessing at it.
Every pull request gets a before/after payload diff so reviewers can verify the fix is complete.
Why Speedscale?
Full payload visibility and deterministic replay for the age of AI-generated code.
Deterministic reproduction
Replay the exact production scenario in a disposable sandbox. Same payloads, same headers, same upstream responses. Every time, without flakiness.
Real context for AI agents
Claude Code, Cursor, and Copilot can't fix what they can't see. Give them the actual request and response that triggered the failure, not a stack trace.
PII-safe production replay
Sensitive fields are masked automatically. Payload structure stays intact, so you get accurate reproduction without compliance risk.
MCP-ready reproduction context
Serve production traffic snapshots through MCP so AI coding agents can pull the exact request that failed and replay it without touching production.
PR-ready fix evidence
Every pull request gets a before/after payload diff. Reviewers see exactly what changed and whether the fix actually addresses the root cause.
Reproduce it. Fix it. Ship it.
Capture production traffic, replay it in your Kubernetes CI pipeline, and give your AI coding agent the context it needs through MCP.
Observability for AI delivery.
From capture to validated fixes.
Validate AI-generated code with real production traffic before merge.
Speedscale turns observability data into action: deep capture, portable traffic context, AI-assisted debugging, deterministic reproduction, and fix validation before merge.
No credit card required • 5-minute setup • 30-day free trial
Trusted by teams shipping AI code without the quality tax
FLYR, Sephora, IHG, and platform teams worldwide use Speedscale to validate AI-generated changes against real production behavior before merging.
Observability that ships
Use observability to reproduce incidents and validate fixes, not just watch charts.
This challenger focuses on five core observability capabilities: deep capture and inspection, data portability, AI integration, deterministic reproduction, and fix validation.
Traditional observability
Dashboards explain symptoms, not always the root cause.
- Logs and traces are useful for alerting, but they often miss full request/response context needed to reproduce failures.
- Production issues still require manual back-and-forth to isolate the exact payload that broke behavior.
- Fix validation is frequently done by guesswork across staging and production drift.
Speedscale observability workflow
Use observability data to fix and validate behavior, not just monitor it.
- Deep capture and inspection: collect full payloads, headers, auth-related metadata, and timing for captured critical calls.
- Data portability: move captured traffic across CI, sandboxes, and teams without rebuilding fragile test fixtures.
- Direct AI integration: feed production-context traffic to Claude Code, Cursor, and Codex through MCP workflows.
- Recreate production issues: replay exact failing conversations in deterministic environments.
- Validate fixes before merge: compare before/after behavior and prove regressions are resolved.
Observability platforms tell you where it hurts. Speedscale helps you fix it faster.
Compare monitoring-only workflows with replay-driven validation workflows.
| Capability | Legacy APM | Static analysis | Speedscale |
|---|---|---|---|
| Catches behavioral regressions from AI code | After deploy. Customers see the failure first. | Syntax only. Misses all runtime failures. | Before merge. Replays real production traffic in CI. |
| Shortens the defect feedback loop | Hours to days: alert, triage, reproduce, fix. | Seconds, but misses most AI-introduced bugs. | Fast feedback. Full payload replay catches what static tools miss. |
| Scales with AI-generated PR volume | Dashboards don't review code. | Overwhelmed by AI change set size and complexity. | Automated replay can run on each change in your configured branches. |
| Gives AI agents context to self-correct | No integration with coding workflows. | No production signal. | MCP-native context for Claude Code, Cursor, and Codex. |
Replay production traffic against every AI change. Automatically.
Record traffic once from Kubernetes, ECS, desktop, or agent surfaces. Replay it against every branch, every change, automatically.
Find the exact request an AI-generated change broke before it reaches staging or your customers.
Your AI coding agent gets the actual production request that exercises the change. Not a static schema. Not a synthetic stub.
Every pull request gets before/after behavioral diffs so reviewers ship with data, not hope.
Why Speedscale?
Validate AI-generated code against real production traffic. Ship faster and catch failures faster.
Deep capture and inspection
Capture complete request/response context from production workloads and inspect failures at payload-level detail.
Portable traffic data
Move captured traffic between environments, CI pipelines, and teams so observability data can be reused instead of recreated.
Direct AI integration
Expose observability-backed traffic context to Claude Code, Cursor, and Codex so agents can debug with real data.
Recreate production issues
Replay the exact failing production conversation in a controlled sandbox to reproduce bugs deterministically.
Validate fixes with evidence
Run before/after comparisons on replayed traffic and confirm that each fix resolves the real regression before merge.
PR-ready behavioral evidence
AI-authored pull requests can include before/after payload comparisons, latency diffs, and severity scores. Reviewers ship with evidence, not optimism.
Ship AI code at speed. Catch failures at speed too.
Production traffic replay and behavioral diffs, built into your Kubernetes pipeline.