Speedscale
UnclaimedRuntime validation for AI-generated code using real production traffic.
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TL;DR - Speedscale
- Validates AI-generated code against real production traffic.
- Captures and replays live traffic in deterministic, PII-safe sandboxes.
- Identifies behavioral failures that static analysis misses before merging AI code.
Pricing: Free plan available
Best for: Growing teams
Pros & Cons
Pros
- Catches behavioral regressions in AI-generated code that static analysis cannot.
- Provides deterministic, repeatable testing environments with production context.
- Reduces the need for extensive manual code review of AI-generated code.
- Offers PII-safe sandboxes, allowing use of production data without security risks.
- Integrates seamlessly into existing CI/CD pipelines and developer workflows.
Cons
- Requires capturing and managing production traffic data.
- Initial setup might involve configuring traffic capture mechanisms.
- Reliance on accurate traffic capture for comprehensive validation.
Key Features
AI Code Verification against real production trafficProxymock for local development and CI mockingKubernetes Traffic Replay for production changesService Virtualization for mocking dependenciesLoad Testing with production-shaped patternsAPI Observability for performance and dependency analysisSensitive Data Redaction (PII-safe sandboxes)PR-ready validation reports with diffs and remediation guidance
Pricing Plans
Free TrialProxymock
$0
- Unlimited local mocking
- Basic traffic recording
- Community support
- Desktop CLI tool
- Local dev environment setup
- Local API Mocking
- Traffic Recording
- Traffic Replay Local Only
- Data Storage Local Only
Team
Contact Us
- Everything in Free
- Advanced traffic replay features
- CI/CD pipeline integration
- Priority email support
- Team collaboration and dashboard
- Local API Mocking
- Traffic Recording
- Traffic Replay Local + Cloud
- Chaos Mocks
- CI/CD Integration
- Business Day Support
- Sensitive Data Redaction
- Data Transformation
- Data Storage Cloud Multi Tenant
Enterprise
Custom
- Everything in Team
- Dedicated support and onboarding
- Custom integrations
- Advanced security features
- SLA guarantees
- Local API Mocking
- Traffic Recording
- Traffic Replay Local + Cloud
- Chaos Mocks
- CI/CD Integration
- Dedicated Slack Support
- Sensitive Data Redaction
- Data Transformation
- Single Sign On (SSO)
- Customer Managed Encryption
- SLA
- Data Storage Cloud Dedicated Tenant
About Speedscale
By Toolradar Team·
Speedscale addresses the 'validation gap' in AI-assisted software development by providing runtime validation for code generated by AI agents like Claude Code, Cursor, Copilot, and Codex. It captures real production traffic patterns and replays them against AI changes in deterministic sandboxes, identifying behavioral failures that static analysis tools cannot detect. This ensures that AI-authored code behaves as expected in a production environment before it is merged.
The platform is designed for development teams and platform engineers who are integrating AI coding tools into their workflows. It helps them confidently ship AI-assisted releases by providing proof of code behavior based on repeatable requests and responses. Speedscale reduces the need for manual review of large AI-generated codebases and eliminates the burden of constantly rebuilding or babysitting staging clusters.
Key benefits include catching regressions early, ensuring contract adherence, and providing audit-ready evidence for every pull request. It allows engineers to surface exact requests that an AI-generated change broke, compare performance metrics like latency and retries, and share traffic snapshots with AI agents for reproduction of defects with production context, all without exposing sensitive production data.
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Speedscale FAQ
How does Speedscale ensure that sensitive production data is safe when replaying traffic in sandboxes?
Speedscale includes a PII-safe sandbox feature that automatically masks sensitive fields while preserving the data structure. This allows governance teams to approve replaying production data without compromising privacy or security.
Can Speedscale help AI coding agents like Copilot or Claude Code reproduce and fix bugs more effectively?
Yes, Speedscale provides MCP-ready testing context, giving AI agents the exact requests and responses needed to triage regressions without guesswork. This allows them to reproduce defects with production context, leading to more accurate and efficient fixes.
How does Speedscale handle dynamic data in traffic replays, such as timestamps or unique IDs, to ensure consistent validation?
Speedscale allows users to apply transforms to modify dynamic data within the captured traffic. This ensures that replays work consistently even when underlying data changes, maintaining the determinism required for reliable validation.
What specific evidence does Speedscale provide in its validation reports to prove AI-authored code behaves correctly?
Speedscale generates machine-readable diff reports that compare before-and-after latency, payloads, and retries as deterministic runs. These PR-ready reports highlight failing calls immediately, confirm downstream contract adherence, and provide severity and remediation guidance.
Beyond AI code validation, what other types of testing can Speedscale facilitate using captured production traffic?
In addition to AI code validation, Speedscale offers comprehensive API testing, service virtualization to mock flaky dependencies, load testing with production-shaped patterns, and API observability to analyze performance and dependencies from real traffic.
Source: speedscale.com