Deep capture and inspection
Capture Traffic Context from production workloads and inspect failures at payload-level detail.
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.
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 Traffic Context to Claude Code, Cursor, and Codex so agents can debug with real production 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.
Free CLI. Runs on your laptop.
Record your traffic. Replay it against your change.
Your service already produces the best test data you will ever have. Capture it, run without your dependencies, and see exactly what your change broke.
brew install speedscale/tap/proxymock
Also available via curl, and for Java, .NET, Node.js, Go, and Python.
01 Record real traffic
proxymock record --app-port 8080capturing on :8080 -> 4919 requests
POST /api/checkout 1204
GET /api/cart/{id} 892
postgres://orders 2823
written to ./proxymock/recorded 02 Run without the dependencies
proxymock mock --in-dir ./proxymock/recordedmock server listening on :4143
postgres://orders matched
https://api.stripe.com matched
https://auth.internal matched
no live dependencies required 03 Replay it against your change
proxymock replay --in-dir ./proxymock/recorded \
--test-against http://localhost:80804919 requests replayed
4917 identical
2 changed
POST /api/checkout
- "tax": 4.20
+ "tax": 4.2000000001That last diff is a real regression. Nobody wrote an assertion to catch it.
The same recordings run in CI.
Promote a local recording into a CI job and every pull request replays real traffic before merge.
Response diffs are computed against what production actually returned, so there are no assertions to write or maintain.
Sensitive fields are masked at capture time, so recordings are safe to commit and share.
Latency is reported per endpoint at P50 through P99, so performance regressions surface in the same run.
What you get
Real traffic, captured once, reused everywhere.
No Kubernetes required
proxymock runs on your laptop against a local process. No cluster, no operator, no sidecar. The same recordings run in CI when you are ready.
More than HTTP
Postgres, MySQL, Redis, and gRPC are captured and mocked at the protocol level, so the dependencies your service actually talks to are covered.
Diffs, not assertions
Replay compares every response against what production actually returned. You do not write the assertions, and you do not maintain them.
A sandbox your agent cannot fake
Coding agents get a deterministic environment and a pass/fail signal from real traffic, instead of grading their own work against tests they wrote.
Your traffic is already there. Start using it.
Install proxymock and get to a first record, mock, or replay in about ten minutes.