Thanos consulting and hands-on support

Thanos is an open-source set of components that extends Prometheus with long-term metric storage, cross-cluster querying, and high-availability deduplication. SRE, platform engineering, and cloud operations teams use it to retain Prometheus data in object storage and query metrics from multiple clusters through a consistent interface.

Thanos fits into monitoring architectures where individual Prometheus servers collect local metrics while shared components provide durable storage and global access. Its deployment requires decisions about object-storage permissions, retention, compaction, query performance, replica labels, network access, and failure handling.

• Assess Prometheus and Thanos architectures, including the roles of Sidecar, Store Gateway, Querier, Query Frontend, and Compactor components.
• Design long-term metric retention in S3-compatible or cloud object storage with clear bucket policies, encryption, lifecycle rules, and access controls.
• Configure global querying across Kubernetes clusters, regions, or environments while preserving tenant and environment boundaries where required.
• Set replica labels and deduplication rules for highly available Prometheus pairs without producing misleading or duplicated time series.
• Plan compaction, downsampling, caching, and query limits to control storage growth and protect monitoring services under heavy analytical workloads.
• Integrate Thanos operations with alerting, dashboards, runbooks, deployment automation, upgrades, and day-2 incident response.

Teams use Thanos when Prometheus metrics must remain queryable across clusters, regions, and longer retention periods without relying on a single Prometheus instance.

• Long-term metric retention lets teams upload Prometheus time-series blocks to durable object storage instead of keeping all historical data on local disks. This supports retention policies measured in months or years, subject to the configured storage and compaction design.
• Global querying lets a Thanos Querier read data from multiple Prometheus sidecars, Store Gateway instances, and other Thanos components through one query endpoint. Operators can investigate incidents across clusters without switching between separate monitoring systems.
• High-availability deduplication reduces duplicate series when teams run replicated Prometheus servers. Consistent external labels and replica-label configuration allow Thanos to identify replicas and return a single logical series during normal operation.
• Controlled storage costs come from keeping raw data for detailed investigation while using downsampling for older, longer-range queries. The Thanos Compactor can apply retention rules and create lower-resolution blocks, although teams still need to size object storage and query infrastructure against their actual metric volume.
• Separation of query and storage workloads allows teams to scale the Querier, Store Gateway, Sidecar, and Compactor according to their individual resource requirements. A large historical query workload does not have to consume the same resources as Prometheus collection.
• Centralized access control gives platform teams a clear place to protect metric data and object storage. Teams can restrict bucket permissions by component, secure query endpoints through their existing network and identity controls, and define which operators may access sensitive labels.
• More predictable incident investigation comes from retaining metrics after a Prometheus pod, node, or cluster is replaced. Teams can compare current behavior with older deployments and incidents without depending entirely on the local retention window of an individual Prometheus server.
• Operational automation supports repeatable deployment and maintenance through Kubernetes manifests, Helm-based workflows, or other infrastructure-as-code practices. Teams can manage retention settings, external labels, object storage configuration, query routing, alerts, backups, and runbooks as part of their normal platform operations.

Our practical experience with Thanos helps clients design and operate reliable Prometheus architectures with durable metric retention, cross-cluster querying, high-availability deduplication, controlled access, and predictable storage costs. MeteorOps provides senior engineering capacity embedded with your team to assess the current setup, implement the target architecture, and strengthen day-2 operations without a retainer or long-term lock-in.

Some of the things we did include:

• Assessing Prometheus and Thanos components, scrape topology, external labels, query patterns, retention requirements, and object storage usage to identify architectural risks and operational gaps.
• Designing a reference architecture for Thanos Sidecar, Store Gateway, Query, Query Frontend, Compactor, and Receive components across clusters or regions.
• Implementing Thanos configuration and supporting infrastructure as code, including object storage integration, access policies, encryption settings, retention controls, and environment-specific configuration.
• Planning and executing migrations from standalone Prometheus retention to durable object storage while preserving metric continuity, validating query results, and controlling ingestion and storage costs.
• Configuring external labels, query federation, deduplication, caching, downsampling, and query limits so operators can search across clusters without creating ambiguous or excessively expensive queries.
• Adding operational monitoring and alerting for compaction failures, block upload delays, store availability, query latency, object storage errors, and duplicate or missing series.
• Creating runbooks, upgrade procedures, incident checks, backup and recovery guidance, and knowledge-transfer material so your team can maintain Thanos safely after implementation.

Some of the things we can help you do with Thanos include:

• Assess your Prometheus and Thanos architecture: Review Prometheus instances, Thanos Sidecar and Receive usage, object storage configuration, external labels, tenancy boundaries, query paths, retention requirements, and current operational ownership to identify gaps and define a practical roadmap.
• Design a long-term metrics storage architecture: Plan the use of Thanos Sidecar, Store Gateway, Compactor, Querier, Query Frontend, and Receive for your cluster, region, availability, retention, and disaster recovery requirements.
• Implement durable object storage for metrics: Configure Thanos to store Prometheus blocks in compatible object storage, including access credentials, bucket layout, encryption settings, lifecycle policies, and retention controls.
• Enable global and cross-cluster querying: Configure external labels, Thanos Querier endpoints, Store Gateway discovery, query federation patterns, and deduplication settings so operators can query metrics across Prometheus instances and Kubernetes clusters.
• Automate Thanos deployment and configuration: Manage Kubernetes manifests, Helm values, object storage credentials, service discovery, resource requests, and environment-specific settings through your existing infrastructure-as-code or GitOps workflows.
• Integrate Thanos with delivery and change management: Add validation for Thanos configuration, recording and alerting rules, dashboards, and deployment changes to CI/CD pipelines, with controlled promotion and rollback procedures.
• Apply security and governance controls: Define access boundaries for query and store components, configure network policies and TLS where required, restrict object storage permissions, separate tenant data when applicable, and document operational ownership and change controls.
• Improve query performance, reliability, and storage cost: Tune compaction, downsampling, query limits, caching, Store Gateway resources, block retention, and object storage lifecycle rules while monitoring query latency, failed requests, compaction health, and storage growth.
• Plan upgrades, migrations, and day-2 operations: Prepare version upgrades or migrations from existing Prometheus storage patterns, test rollback and recovery procedures, create runbooks for component failures and data gaps, and establish regular reviews for capacity, retention, alerts, and Thanos health.