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 headers, 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 complete request and response payloads from production: every header, body, status code, and timing, stored as reproducible, shareable 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.
- Any engineer or AI agent can replay a production failure 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, auth, every call. |
| 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.
Stop saying 'can't reproduce in staging.'
Capture the payloads. Replay the failure. Ship the fix with proof.
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.
AI agents ship code 10× faster.
Bugs travel at the same speed.
Validate AI-generated code with real production traffic before merge.
You feel faster, but you're spending hours reviewing code you didn't write and debugging failures you can't reproduce. Speedscale replays real production traffic against every AI change so you ship with proof, not hope.
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.
The velocity trap
Speed without feedback is just faster failure.
AI promised to make you faster. Instead, you're spending more time reviewing code you didn't write, chasing bugs that only appear in production, and rewriting changes that passed every check but still broke. The teams actually moving fast are the ones who test every AI change against real traffic before it merges. Speedscale plugs into your Kubernetes and API testing pipeline to make that automatic.
Unguided velocity
Faster code generation. Faster bug generation.
- AI agents generate code fast, but the defect rate climbs with adoption. More AI-authored PRs means more time in review, more rework, and more incidents that trace back to changes nobody fully understood.
- Static analysis and unit tests were calibrated for human-paced development. They can't keep up with AI-generated change sets or the runtime regressions hiding inside them.
- Code review at AI velocity is theater. Nobody can audit 1,000 lines of agent-generated code against real production behavior in a pull request window.
Guided velocity with Speedscale
Ship fast. Catch failures faster.
- Replay captured production traffic against every AI-authored change, automatically in CI. Regressions surface in seconds, not in production incidents.
- Give Claude Code, Cursor, Codex, and Copilot the exact production requests they need to validate their own changes before asking for a human review.
- The best teams don't ship slower. They just know what's broken before it merges. Speedscale gives you that signal from real production traffic.
The fastest teams aren't the ones that ship the most. They catch failures first.
See where the velocity tax actually comes from, and where Speedscale cuts it.
| 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. | Seconds. 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 covers every change on every branch. |
| 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.
Velocity is an asset. Unchecked defects are the liability.
Drop Speedscale into your CI pipeline and MCP workflow. Replay real traffic against every AI change before it reaches your customers.
Why Speedscale?
Validate AI-generated code against real production traffic. Ship faster and catch failures faster.
Production replay at AI velocity
Replay tens of thousands of real requests against every AI-authored change, automatically in CI. Regressions get caught before they compound into incidents.
Full payload capture, any surface
Record complete request and response payloads from Kubernetes, ECS, desktop, or agent traffic. Share deterministic snapshots across branches without rebuilding environments.
MCP-native AI agent context
Your AI coding agent can pull the exact production requests it needs through MCP to validate its own changes, without touching live systems.
PII-safe production data
Sensitive fields are masked automatically while payload structure stays intact. Full fidelity replay without compliance or governance risk.
Regression visibility
See exactly where an AI-generated change breaks: latency, payloads, auth, downstream contracts. All visible before it reaches your SLA or your customers.
PR-ready behavioral evidence
Every AI-authored pull request gets before/after payload comparisons, latency diffs, and severity scores. Reviewers ship with data, not optimism.
Ship AI code at speed. Catch failures at speed too.
Production traffic replay and behavioral diffs, built into your Kubernetes pipeline.