Prometheus Long-Term Storage in 2025: Thanos vs VictoriaMetrics vs Grafana Mimir Performance Comparison for VPS Deployments

When running Prometheus at scale, the built-in local storage quickly becomes a bottleneck. For organizations processing millions of metrics across distributed systems, choosing the right long-term storage solution can make or break your observability strategy. In 2025, three solutions dominate the Prometheus long-term storage landscape: Thanos, VictoriaMetrics, and Grafana Mimir.

This comprehensive comparison evaluates these platforms across critical dimensions including ingestion performance, query latency, storage costs, multi-tenancy capabilities, and operational complexity when deployed on VPS infrastructure.

Architecture Overview and Design Philosophy

Thanos: Sidecar Architecture

Thanos follows a **sidecar pattern** where components run alongside existing Prometheus instances. The architecture consists of:

• Sidecar: Uploads blocks to object storage and serves real-time queries
• Store Gateway: Serves historical data from object storage
• Querier: Provides unified query interface across time ranges
• Compactor: Performs downsampling and retention management

This design minimally impacts existing Prometheus deployments, making it ideal for gradual migrations. However, the distributed nature increases operational complexity.

VictoriaMetrics: Unified Platform

VictoriaMetrics offers both **single-node** and **cluster** deployments with a focus on simplicity and performance. The cluster architecture includes:

• vminsert: Handles data ingestion and routing
• vmselect: Processes queries across storage nodes
• vmstorage: Stores and serves time series data

VictoriaMetrics emphasizes operational simplicity with aggressive compressions achieving 10x better storage efficiency than vanilla Prometheus.

Grafana Mimir: Microservices Architecture

Grafana Mimir, derived from Cortex, implements a **microservices architecture** optimized for multi-tenancy:

• Distributor: Routes incoming metrics with tenant isolation
• Ingester: Buffers and stores recent data
• Querier: Executes queries with automatic query sharding
• Store Gateway: Serves historical blocks from object storage

Mimir excels in **enterprise environments** requiring strict tenant isolation and horizontal scaling.

Performance Benchmarks and Resource Requirements

Ingestion Performance

Based on community benchmarks and production deployments, here’s how each solution performs at scale:

VictoriaMetrics leads in raw ingestion performance:

• Single-node: Up to 1M samples/sec on 8-core VPS
• Cluster: 10M+ samples/sec with horizontal scaling
• Memory usage: 50% lower than Prometheus

Thanos maintains Prometheus-native performance:

• Limited by underlying Prometheus instances
• Sidecar adds ~10% CPU overhead
• Excellent for preserving existing monitoring workflows

Grafana Mimir optimizes for multi-tenant workloads:

• 500K-2M samples/sec per ingester pod
• Strong tenant isolation with minimal performance impact
• Higher memory requirements due to microservices overhead

Query Performance and Latency

Query performance varies significantly based on data patterns and time ranges:

Recent Data Queries (< 2 hours):

• VictoriaMetrics: 20-50ms median latency
• Thanos: 50-100ms (sidecar queries)
• Mimir: 30-80ms with proper caching

Historical Data Queries (> 24 hours):

• VictoriaMetrics: 100-500ms with efficient compression
• Thanos: 200ms-2s depending on object storage latency
• Mimir: 150ms-1s with store gateway caching

Storage Costs and Retention Management

Storage efficiency directly impacts operational costs, especially for VPS deployments in Amsterdam and New York where storage pricing varies.

Compression Efficiency

• VictoriaMetrics: Up to 10x compression vs Prometheus (proprietary algorithms)
• Thanos: 2-4x compression with configurable downsampling (5m, 1h retention policies)
• Mimir: 2-3x compression with block-based storage

Object Storage Integration

All three solutions support S3-compatible storage, crucial for cost-effective long-term retention:

• Thanos: Native object storage design with automatic block uploads
• VictoriaMetrics: vmbackup/vmrestore tools for S3 integration
• Mimir: Built-in S3 support with configurable retention policies

Multi-Tenancy and Data Isolation

Multi-tenancy requirements vary significantly between organizations, from simple namespace separation to strict regulatory compliance.

Grafana Mimir provides the strongest multi-tenancy features:

• Native tenant isolation at ingestion and query levels
• Per-tenant rate limiting and resource quotas
• Separate retention policies per tenant

VictoriaMetrics offers basic multi-tenancy:

• Tenant ID-based data separation
• Simple tenant routing in cluster mode
• Limited per-tenant resource controls

Thanos handles multi-tenancy through external labels:

• Relies on Prometheus external labels for tenant separation
• Query-time filtering based on labels
• Requires careful configuration to prevent data leakage

Operational Complexity and Maintenance

The operational burden varies dramatically between solutions, impacting staffing requirements and incident response times.

Deployment Complexity

• VictoriaMetrics: Single binary deployment, minimal configuration
• Thanos: Multiple components requiring careful coordination
• Mimir: Complex microservices deployment, typically requires Kubernetes

Monitoring and Observability

All solutions provide comprehensive metrics, but monitoring complexity differs:

• VictoriaMetrics: Built-in dashboards, simple metric exposition
• Thanos: Component-specific dashboards, complex query tracing
• Mimir: Extensive metrics, tenant-aware dashboards

For teams managing observability stacks on Ubuntu VPS, VictoriaMetrics often provides the best operational experience.

VPS Deployment Considerations

When deploying these solutions on VPS infrastructure, several factors become critical:

Resource Requirements

Minimum VPS Specifications:

• VictoriaMetrics: 2 vCPUs, 4GB RAM, 100GB NVMe (handles 100K samples/sec)
• Thanos: 4 vCPUs, 8GB RAM, 200GB NVMe (multiple components)
• Mimir: 8 vCPUs, 16GB RAM, 300GB NVMe (microservices overhead)

Network Latency Impact

Object storage latency significantly impacts query performance. For multi-region deployments across Amsterdam and New York VPS, consider:

• Regional object storage placement
• Query result caching strategies
• Cross-region replication requirements

Decision Framework: Choosing the Right Solution

Choose VictoriaMetrics if:

• You prioritize operational simplicity and cost efficiency
• Single-tenant or basic multi-tenant requirements
• Limited operational expertise for complex distributed systems
• High ingestion rates with aggressive cost optimization

Choose Thanos if:

• You have existing Prometheus infrastructure to preserve
• Gradual migration requirements without service disruption
• Strong preference for CNCF ecosystem compatibility
• Existing expertise with Prometheus operations

Choose Grafana Mimir if:

• Strict multi-tenancy and compliance requirements
• Large-scale enterprise deployments with dedicated teams
• Advanced features like query sharding and result caching
• Integration with Grafana Enterprise ecosystem

Conclusion

The choice between Thanos, VictoriaMetrics, and Grafana Mimir depends heavily on your specific requirements around operational complexity, multi-tenancy, and performance characteristics. VictoriaMetrics offers the best balance of performance and simplicity for most organizations, while Thanos provides the smoothest migration path from existing Prometheus deployments. Grafana Mimir excels in complex enterprise environments requiring advanced multi-tenancy features.

For organizations deploying on VPS infrastructure in Amsterdam or New York, we recommend starting with VictoriaMetrics for its operational simplicity and excellent price-performance ratio. The reduced operational overhead allows teams to focus on application development rather than infrastructure management.

Consider your team’s expertise, compliance requirements, and growth projections when making this architectural decision. All three solutions have proven themselves in production environments, and the “best” choice ultimately aligns with your organization’s operational constraints and technical requirements.