LogStitch.

LogStitch

LogStitch is a native macOS application that transforms how developers interact with AWS Lambda logs. Instead of scrolling through chaotic, timestamp-sorted CloudWatch streams, LogStitch automatically groups every log line by request ID—stitching fragmented executions into readable, coherent stories that reveal what actually happened across your serverless infrastructure.

Product Highlights

• Stitch: Groups logs by request ID with automatic JSON parsing, cold-start detection, and platform event surfacing—turning scattered lines into complete invocation narratives.
• Correlate: Follows requests across functions, accounts, and regions with a visual swim-lane timeline that exposes propagation latency, error origins, and downstream blast radius in a single view.
• Analyze: Delivers built-in p99 trend analysis, cold-start scatter plots, memory right-sizing recommendations, and working monthly cost projections without external dashboards.
• Detect: Auto-clusters recurring errors into patterns with lifecycle tracking and statistical anomaly detection across duration, errors, cold starts, and cost—surfacing issues before customers notice.

Use Cases

• Debugging Distributed Traces: Engineers troubleshooting requests that span multiple Lambda functions can trace execution paths across accounts and regions, identifying exactly where latency spikes or failures originate without manual correlation.
• Cost Optimization at Scale: Platform teams monitoring hundreds of Lambda functions use built-in analytics to identify memory over-provisioning, cold-start inefficiencies, and projected spend—enabling data-driven right-sizing decisions.
• AI-Assisted Incident Response: Teams integrating Claude or other MCP-compatible tools can query their complete log history through LogStitch's local server, receiving contextual analysis without exposing AWS credentials to third-party services.

Target Audience

LogStitch serves backend engineers, DevOps practitioners, and platform teams managing AWS Lambda at scale who need fast, local-first log analysis without the friction of CloudWatch's raw streams or the security risks of cloud-based log aggregation tools.