Grafana Mimir is an open source project written in Go that provides horizontally scalable, highly available, multi-tenant long-term storage for Prometheus metrics. It addresses the limitations of single Prometheus instances by enabling organizations to store and query massive volumes of time series data across distributed infrastructure. The project is designed to handle up to 1 billion active time series according to internal testing, making it suitable for large-scale observability deployments.
The core architecture emphasizes both operational simplicity and massive scalability. Mimir can run in monolithic mode with just a single binary and no additional dependencies, making initial deployment straightforward. However, it also supports a horizontally scalable distributed architecture that allows organizations to process orders of magnitude more time series than standalone Prometheus. The system uses object storage for long-term data retention, supporting multiple backends including AWS S3, Google Cloud Storage, Azure Blob Storage, OpenStack Swift, and any S3-compatible storage solutions. This approach provides cost-effective, durable storage while maintaining high availability through metric replication that prevents data loss during machine failures.
Mimir's query engine is built for performance, extensively parallelizing query execution to handle high-cardinality queries with speed. The system enables global views of metrics by allowing queries that aggregate series from multiple Prometheus instances, providing unified visibility across distributed systems. Its natively multi-tenant architecture isolates data and queries between independent teams or business units sharing the same cluster, with advanced limits and quality-of-service controls ensuring fair capacity distribution among tenants.
The repository shows active development and maintenance. GitGenius tracking indicates the project had 788 open issues as of the most recent check, with growth of 3 issues from the previous tracking period. The most frequently labeled issues involve bugs (231 occurrences), enhancements (181), and Helm-related topics (115), reflecting the project's focus on reliability, feature development, and Kubernetes deployment scenarios. The core contributor base includes dimitarvdimitrov with 572 tracked events, 56quarters with 489 events, and narqo with 438 events, indicating sustained engagement from key maintainers. Response latency across 966 tracked issues and pull requests shows a median of 0.0 hours with a mean of 2945.8 hours, reflecting the project's handling of both urgent and longer-term items.
The project maintains comprehensive documentation covering deployment, configuration, and production operations. Migration paths are explicitly documented for users transitioning from Thanos, Prometheus, or Cortex. The repository is distributed under the AGPL-3.0-only license and maintains active community engagement through GitHub discussions, a monthly community call, and a dedicated Slack channel. The project's classification across multiple domains including time-series databases, distributed architecture, query processing, and data aggregation reflects its position as a comprehensive metrics storage and querying platform for modern observability infrastructure.
