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In the evolving landscape of cloud-native applications, the demand for high-scale, durable, and low-latency monitoring solutions is more critical than ever. Developers and platform teams managing Kubernetes clusters, distributed systems, or microservices-based architectures need a solution that is cost-effective, highly available, and developer-friendly.
Enter Grafana Mimir, an open-source time series database that seamlessly integrates with Prometheus, unlocking horizontal scalability, multi-tenancy, long-term retention, and unmatched performance. In this blog, we'll dive deep into how Grafana Mimir works with Prometheus, the architecture that powers it, the benefits it offers over traditional methods, and why it has become a favorite among engineering teams.
Grafana Mimir is a massively scalable, multi-tenant, Prometheus-compatible long-term storage system for metrics. It was created by Grafana Labs as a continuation and rebranding of Cortex, with a stronger focus on ease of deployment, performance, and seamless integration into the Grafana observability ecosystem.
It allows users to send metrics from one or many Prometheus instances to a single Mimir deployment, enabling unified querying, durable historical retention, high availability, and efficient resource usage. Grafana Mimir is designed to store and query billions of time series, making it ideal for companies running production infrastructure at scale.
Standalone Prometheus is excellent for small to medium workloads, but when the number of active time series increases into the tens or hundreds of millions, it begins to hit architectural limits. Developers often experience memory bloat, query delays, and the need for federation or sharding. Grafana Mimir solves this problem by horizontally scaling the ingestion and querying layer, enabling you to handle more metrics without complex Prometheus federation setups.
Grafana Mimir supports over one billion active time series, and it achieves this through distributor, ingester, querier, and compactor components that work together to distribute the load efficiently. This means you can scale each component independently based on your system’s load, ensuring optimal use of system resources.
Prometheus is designed primarily for short-term storage. While it offers retention for days or weeks, organizations need access to months or years of historical data for trend analysis, compliance, and forecasting. Grafana Mimir supports long-term storage using object storage backends like Amazon S3, Google Cloud Storage, Azure Blob Storage, and MinIO.
This design makes Grafana Mimir a cost-effective solution for long-term metric storage. It compresses metric blocks and stores them in the cloud, offloading the burden from local disks. Developers can query historic data efficiently using the same PromQL language they’re familiar with, without worrying about local disk capacity.
In a production environment, losing metrics or failing alert pipelines is unacceptable. Mimir introduces built-in high availability features. With replicated ingestion pipelines, write-ahead logs (WALs), and deduplication, Grafana Mimir ensures your metrics are safe even during node failures or restarts.
For developers, this means you don’t need to worry about data gaps during rollouts or crashes. Mimir's architecture uses a quorum-based model for replication and supports zone-awareness, making it resilient to availability zone outages when deployed in cloud environments.
A key challenge in modern SaaS or large enterprise environments is isolating workloads between teams, services, or environments. Grafana Mimir solves this by introducing first-class multi-tenancy. Each tenant can be assigned unique metric namespaces, resource quotas, and rate limits.
Developers working across multiple environments, like staging, testing, and production, can send metrics with different tenant tokens, ensuring clean separation and avoiding accidental data leaks or conflicts. Additionally, team-specific dashboards and alerting rules become easier to manage with isolated tenants.
One of the primary reasons developers adopt Grafana Mimir is its efficient query execution model. Queries are split into sub-queries and executed in parallel across sharded queriers, then aggregated by a frontend component. This results in fast and responsive PromQL queries, even when querying across billions of samples or extended time ranges.
Mimir also features query result caching, label match optimization, and on-the-fly deduplication, all of which contribute to lower latency and resource usage during query execution.
Prometheus continues to act as your metrics scraper. It collects data from exporters (Node Exporter, cAdvisor, application endpoints, etc.) and pushes these metrics using remote_write to Grafana Mimir. You can also use Grafana Agent, a lightweight alternative to Prometheus, for scraping and shipping metrics directly to Mimir.
remote_write:
- url: http://<mimir>:9009/api/v1/push
This setup enables Prometheus to offload storage concerns to Mimir while focusing on collection and local querying.
Once metrics are sent to Mimir, the distributor component receives the incoming data and forwards it to multiple ingesters. These ingesters temporarily store the data in memory and persist them to disk using write-ahead logs. Ingesters are responsible for deduplicating samples, storing them efficiently, and ensuring they can be recovered in case of failure.
The ingesters then flush blocks of metrics to object storage, compressing and organizing them for future querying.
Periodically, Mimir’s compactor process kicks in to optimize the blocks stored in the object storage backend. It merges small blocks into larger ones, removes deleted series, and indexes the data for faster queries.
This lifecycle makes Grafana Mimir particularly suitable for cost-efficient long-term metric storage, and allows developers to run historical analyses without performance degradation.
When a developer runs a query via Grafana or the HTTP API, it hits the query frontend, which parses the PromQL and distributes it to multiple querier nodes. Each querier is responsible for fetching data from both ingesters (for recent data) and object storage (for historical data).
Mimir then aggregates results and applies functions like sum(), avg(), or rate() as defined in the PromQL query. This model provides low-latency querying even at scale, an essential requirement for monitoring large infrastructure or microservice ecosystems.
Mimir supports Prometheus-style recording rules and alerting rules, allowing developers to precompute expensive queries or trigger alerts based on thresholds.
These rules can be stored as YAML files and managed via GitOps or custom CI/CD flows. Mimir’s compatibility ensures seamless integration with Alertmanager or Grafana dashboards, so you don’t need to reinvent your observability pipelines.
Pipedrive, a leading CRM platform, ran into major issues using vanilla Prometheus. They experienced OOM errors, data loss, and slow queries with just over 15 million active time series. After migrating to Grafana Mimir, they scaled effortlessly, ran cardinality reports, and prevented single misbehaving queries from impacting the entire observability pipeline.
This highlights how Mimir is not just about scale, it’s about stability, visibility, and predictability for engineering teams.
Before Mimir, scaling Prometheus meant federating data across instances or manually sharding metrics. This introduced latency, duplication, increased maintenance complexity, and risk of partial data during outages.
Grafana Mimir removes this overhead by offering automatic ingestion routing, redundant storage, data deduplication, and centralized querying across all time series, while being Prometheus-compatible.
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