Grafana Mimir: Enterprise-Grade Metrics Storage for Prometheus at Scale

Grafana Mimir: Enterprise-Grade Metrics Storage for Prometheus at Scale

Managing metrics at scale is one of the most challenging problems DevOps engineers and SREs face today. As your infrastructure grows, traditional Prometheus setups struggle with retention, scalability, and multi-tenant isolation. Grafana Mimir solves these problems by providing a horizontally scalable, highly available, long-term storage backend for Prometheus and OpenTelemetry metrics.

In this technical guide, we'll explore Grafana Mimir's architecture, deployment strategies, and practical implementation patterns that will help you build a robust metrics infrastructure capable of handling billions of active series.

What is Grafana Mimir?

Grafana Mimir is an open-source, horizontally scalable time series database designed for long-term storage of Prometheus metrics.[3] Announced in 2022, Grafana Mimir combines the best practices from Grafana Labs' experience running Grafana Enterprise Metrics and Grafana Cloud at massive scale.[1] The project enables organizations to scale metrics to 1 billion active series and beyond while maintaining high availability, multi-tenancy, and durable storage.[6]

Released under the AGPLv3 license, Grafana Mimir is built on a microservices architecture that allows you to deploy it as a monolithic service for simpler use cases or as distributed components for enterprise deployments.[4] Unlike traditional Prometheus setups that are limited by single-node storage constraints, Grafana Mimir automatically clusters, scales, and rebalances without manual intervention.

Core Architecture and Components

Grafana Mimir follows a microservices-based design where all components are compiled into a single binary.[3][4] The -target parameter determines which component(s) the binary runs, allowing flexible deployment patterns. This design provides three deployment modes:

• Monolithic Mode: All components run in a single process, ideal for testing and small deployments
• Read-Write Mode: Components are grouped by function (read path vs. write path), balancing simplicity and scalability
• Microservices Mode: Each component runs independently, enabling true horizontal scaling

The write path begins when Prometheus instances scrape metrics and push them to Grafana Mimir using the Prometheus remote write API.[4] These requests contain batched Snappy-compressed Protocol Buffer messages and must include a tenant ID header for multi-tenant isolation. The distributor component handles incoming writes, while the query frontend processes incoming PromQL queries on the read path.

A critical innovation in Grafana Mimir 3.0 is the decoupled architecture that separates read and write paths through an asynchronous Kafka-based ingest layer.[2] This eliminates cross-path dependencies, keeping queries fast and stable even under heavy ingestion loads. The new Mimir Query Engine (MQE) streams query results instead of loading entire datasets into memory, reducing memory usage by up to 92% and improving execution speed.[2]

Key Features for DevOps and SRE Teams

Horizontal Scalability: Grafana Mimir clusters automatically. To increase capacity, simply add new instances to the cluster—Grafana Mimir handles rebalancing internally.[6] The two-stage horizontally scalable compactor and query sharding split single queries across multiple machines, enabling efficient processing of high-cardinality metrics.[7]

Multi-Tenancy: Metrics from different teams or departments can be isolated in storage, allowing each group of users to query only their own data.[7] This is essential for organizations where multiple teams share infrastructure but require data isolation for security and compliance.

High Availability: Grafana Mimir provides built-in redundancy and failover capabilities. The system maintains data durability through distributed storage backends and automatically recovers from component failures.[6]

Cost Efficiency: Early testing reports up to 15% lower resource usage while achieving higher throughput and consistency across large clusters.[2] This translates to significant operational cost savings for organizations running large-scale metrics infrastructure.

Deployment Considerations

Before deploying Grafana Mimir, consider your organization's specific requirements. For teams with strict privacy, security, or compliance requirements, Grafana Labs offers Enterprise Metrics as a self-managed Prometheus service.[6]

Request authentication and authorization are handled by an external reverse proxy, allowing you to integrate Grafana Mimir with your existing identity and access management systems.[4] This separation of concerns simplifies security architecture and enables flexible integration with enterprise authentication providers.

Grafana Mimir includes best-practice dashboards, alerts, and runbooks packaged with the system, making it easy to monitor the health of your metrics infrastructure.[5] The Overview dashboard provides a high-level view of your entire Grafana Mimir cluster, showing health and status metrics at a glance.[8]

Practical Implementation Pattern

Here's a basic example of configuring Prometheus to send metrics to Grafana Mimir:

global:
  scrape_interval: 15s
  evaluation_interval: 15s

remote_write:
  - url: http://mimir-distributor:9009/api/prom/push
    write_relabel_configs:
      - source_labels: [__name__]
        regex: 'go_.*'
        action: keep
    queue_config:
      capacity: 10000
      max_shards: 200
      max_samples_per_send: 10000

scrape_configs:
  - job_name: 'prometheus'
    static_configs:
      - targets: ['localhost:9090']
  
  - job_name: 'kubernetes-nodes'
    kubernetes_sd_configs:
      - role: node
    relabel_configs:
      - action: labelmap
        regex: __meta_kubernetes_node_label_(.+)

This configuration enables Prometheus to write metrics to Grafana Mimir's distributor component while applying write-time relabeling to reduce cardinality. The queue configuration ensures reliable delivery with automatic backoff and retry logic.

Query Performance and Optimization

Grafana Mimir achieves query performance up to 40x faster than Cortex through its sharded query engine and optimized storage format.[1] The streaming query results approach in Mimir 3.0 eliminates memory bottlenecks that plagued earlier implementations.

For high-volume environments, Grafana Mimir supports probabilistic query hints that accelerate analysis by returning approximate results faster without losing visibility.[2] This is particularly valuable for exploratory queries and debugging scenarios where approximate results suffice.

OpenTelemetry and Future-Proofing

Beyond Prometheus metrics, Grafana Mimir supports OpenTelemetry metrics, future-proofing your metrics infrastructure.[5] This enables unified collection and storage of metrics, logs, and traces through Grafana Alloy, Grafana Labs' distribution of the OpenTelemetry Collector.[2] Grafana Mimir aligns with newer OpenTelemetry semantic conventions, ensuring compatibility with the evolving observability ecosystem.

Conclusion

Grafana Mimir addresses the fundamental scalability challenges of Prometheus-based monitoring. By providing automatic clustering, multi-tenancy, and enterprise-grade reliability, it enables organizations to build metrics infrastructure that grows with their needs. Whether you're managing thousands of servers or operating at cloud scale, Grafana Mimir's proven architecture and community support make it an excellent choice for long-term metrics storage.

Start with monolithic mode for evaluation, then transition to distributed deployments as your metrics volume grows. With Grafana Mimir, you're investing in a platform that scales with your infrastructure while maintaining the simplicity and familiarity of Prometheus.