Parseable
An observability platform for predictive insights across MELT telemetry.
2.4k stars 171 forks last commit first released AGPL-3.0
Actively maintained
Last commit 26 Aug 2026.
Parseable is a full-stack observability platform built to ingest, analyze and extract insights from all types of telemetry (MELT) data. It can run locally, in the cloud, or as a managed service, providing a unified way to explore signals across the stack.
Key Features
- Unified signals across MELT data for a single source of truth
- Predictive analytics and anomaly forecasting to anticipate issues
- Natural language and SQL querying across telemetry
- Hybrid execution engine with columnar storage and indexing for fast queries
- Granular access control and federated IAM
- Open standards and vendor-neutral design (OTel, Parquet compatibility)
- Cloud-ready with BYOC options
Use Cases
- Full-stack observability of applications, databases, infrastructure and networks
- AI workloads observability for telemetry from AI models and LLMs
- Product observability to analyze user behavior, feature adoption, and performance
Conclusion Parseable provides predictive observability with a unified data model, enabling faster insights and proactive incident response across the full telemetry stack.
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