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.
Code against production reality, not hallucinations.
The first deterministic QA Companion for the dynamic nature of production.
AI coding agents fail because they are forced to guess how your APIs behave in a dynamic environment. Speedscale injects real, evolving production traffic directly into your agent's context window.
No credit card required • 5-minute setup • 30-day free trial
Trusted by teams shipping 126% more AI-assisted code
From platform engineering to core product teams, Speedscale grounds AI agents in the dynamic reality of production traffic.
The dynamic context gap
Static RAG is dead. Live reality is the new standard.
Production isn't a document; it's a live, shifting environment. Static context files like CLAUDE.md are obsolete the moment you commit them. Speedscale provides a continuous, dynamic signal from your production environment directly to your AI's inner loop.
The Static Context Problem
Static rules can't keep up with dynamic reality.
- Traditional RAG and static Markdown files (CLAUDE.md) suffer from 'context drift' as your production environment evolves.
- Google's 2024 DORA report shows a 7.2% decrease in stability for every 25% increase in AI adoption. The cause: shipping code that hasn't seen reality.
- Only 29% of engineers fully trust AI accuracy (Stack Overflow 2024). Hallucinations thrive when agents lack live production signals.
The Speedscale Solution
Feed your AI a live feed of production reality.
- 92% of developers now use AI (GitHub Octoverse 2024). Speedscale grounds them with dynamic, organic production context via MCP.
- Teams using Speedscale complete tasks 55% faster and ship 126% more projects (Microsoft/MIT Sloan) by eliminating debugging loops.
- Validate AI changes against the *current* state of your dynamic system, catching regressions before they hit production.
Stop vibe coding. Start grounded coding.
Compare how different approaches handle the constantly evolving nature of production context.
| Capability | Legacy APM | Static analysis | Speedscale |
|---|---|---|---|
| Context Freshness | Passive & Retrospective. | Static & Outdated. | Dynamic: A live feed of production reality. |
| Grounding for AI Agents | Sampling only. No bodies. | No runtime behavior. | Grounded: Real-world payloads for every request. |
| Verification Accuracy | Post-deploy detection. | Syntax/Types only. | Deterministic: Replays reality against your change. |
| Stability Impact | Reacts to outages. | Misses runtime edge cases. | Proactive: Fixes the 7.2% DORA 'stability gap'. |
A live feed of production reality for your AI agent.
Deep API Inspection: The literal, varied payloads currently flowing through your system, not just an idealized schema.
Grounded Coding: Force your agent to handle real-world edge cases and malformed inputs it would otherwise hallucinate.
Break the stability gap: Close the 7.2% DORA stability gap by validating against reality before every merge.
Shippable Evidence: Attach a passed traffic replay trace to every PR as cryptographic-like proof of quality, closing the 29% trust gap.
The AI QA Companion
Deterministic validation for the age of autonomous agents.
Dynamic API Inspection
Production is constantly shifting. Speedscale gives your AI agent the literal, varied, and dynamic data combinations currently flowing through your system.
Break the 'Token Tax' Loop
Stop 'Hot Potato' reasoning loops. Binary, deterministic feedback from traffic replay halts hallucinations and saves your API budget.
Beyond Static RAG
While RAG merely feeds an agent static documentation, Speedscale feeds it reality. No more 'vibe coding' against outdated Swagger files.
Autonomous Accountability
Make your AI prove its work. Every PR includes evidence of passing against 10,000 real production requests, closing the 29% trust gap.
MCP-Native Live Signal
Claude Code, Cursor, and Copilot connect directly to your production reality. They see what actually happens, not just what you *think* happens.
Verified Throughput
Join the elite teams shipping 126% more projects. Move validation left into the inner loop, directly where your agent lives.
Close the trust gap. Ship with evidence.
Inject dynamic production context into your AI's context window and validate every change against live reality.