Scaling Opik
Opik is built to power mission-critical workloads at scale. Whether you’re running a small proof of concept or a high-volume enterprise deployment, Opik adapts seamlessly to your needs. Its stateless architecture and powerful ClickHouse backed storage make it highly resilient, horizontally scalable, and future-proof for your data growth.
This guide outlines recommended configurations and best practices for running Opik in production.
Proven at Scale
Opik is engineered to handle demanding, production-grade workloads. The following example demonstrates the robustness of a typical deployment:
A deployment of this scale is fully supported using:
Opik Services - These Opik Services run on r7i.2xlarge instances with 2 replicas:
- Opik Backend
- Opik Frontend
The Opik Python Backend service runs on c7i.2xlarge instances with 3 replicas:
ClickHouse - running on m7i.8xlarge instances with 2 replicas.
This configuration provides both performance and reliability while leaving room for seamless expansion.
Built for Growth
Opik is designed with flexibility at its core. As your data grows and query volumes increase, Opik grows with you.
- Horizontal scaling - add more replicas of services to instantly handle more traffic
- Vertical scaling - increase CPU, memory, or storage to handle denser workloads
- Seamless elasticity - scale out during peak usage and scale back during quieter periods
For larger workloads, ClickHouse can be scaled to support enterprise-level deployments. A common configuration includes:
- 62 CPU cores
- 256 GB RAM
- 25 TB disk space
ClickHouse’s read path can also scale horizontally by increasing replicas, ensuring Opik continues to deliver high performance as usage grows.
Resilient Services Cluster
Opik services are stateless and fault-tolerant, ensuring high availability across environments. Recommended resources:
Instance Guidance
Backend Service (Scales to Demand)
Frontend Service (Always Responsive)
ClickHouse: High-Performance Storage
At the heart of Opik’s scalability is ClickHouse, a proven, high-performance analytical database designed for large-scale workloads. Opik leverages ClickHouse for storing traces and spans, ensuring fast queries, robust ingestion, and uncompromising reliability.
Instance Types
Memory-optimized instances are recommended, with a minimum 4:1 memory-to-CPU ratio:
Replication Strategy
- Development: 1 replica
- Production: 2 replicas
Always scale vertically before adding more replicas for efficiency.
CPU & Memory Guidance
Target 10–20% CPU utilization, with safe spikes up to 40–50%.
Maintain at least a 4:1 memory-to-CPU ratio (extend to 8:1 for very large environments).
Disk Recommendations
To ensure reliable performance under heavy load:
Opik’s ClickHouse layer is resilient even under sustained, large-scale ingestion, ensuring queries stay fast.
Managing System Tables
System tables (e.g., system.opentelemetry_span_log) can grow quickly. To keep storage lean:
- Configure TTL settings in ClickHouse, or
- Perform periodic manual pruning
Why Opik Scales with Confidence
- Enterprise-ready — built to support multi-terabyte data volumes
- Elastic & flexible — easily adjust resources to match workload demands
- Robust & reliable — designed for high availability and long-term stability
- Future-proof — proven to support growing usage without redesign
With Opik, you can start small and scale confidently, knowing your observability platform won’t hold you back.
