AI Debugging Is Still Black Magic
AI systems fail in production and developers are left guessing. Traditional debugging tools don't work with AI decision-making.
AI models fail in production with no clear debugging path
Impossible to trace AI decisions through complex pipelines
No way to reproduce AI bugs or test edge cases
Stakeholders ask 'why did the AI do that?' and you can't answer
AI performance degrades over time without visibility
Multiple AI services with no unified monitoring
AI Observability for Developers
Add logging, monitoring, and debugging capabilities to AI systems with just a few lines of code.
Built for Developers
Industry-specific features and integrations
Simple Integration
Add AI observability with just a few lines of code. Works with any framework or AI service.
Real-Time Debugging
Live debugging of AI decisions with full context, inputs, outputs, and decision reasoning.
Version Control
Track AI model versions, experiments, and deployments with full history and rollback capabilities.
AI-Specific Debugging
Debug bias, hallucinations, and edge cases with specialized AI debugging tools and visualizations.
Performance Optimization
Identify bottlenecks, optimize model performance, and reduce AI infrastructure costs.
Developer Tools
CLI tools, IDE extensions, and API integrations that fit seamlessly into developer workflows.
Built by Developers, for Developers
QuietStack was created by developers who've experienced the frustration of debugging AI systems. We built the tools we wished we had when building AI applications.
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