comet-ml--opik/apps/opik-documentation/documentation/fern/docs/self-host/scaling.mdx

---
headline: Scaling Opik | Opik Documentation
og:description: Learn best practices and configurations for running Opik in production,
  ensuring resilience and scalability for mission-critical workloads.
og:site_name: Opik Documentation
og:title: Scaling Opik for High-Volume Deployments
subtitle: Comprehensive guide for scaling Opik in production environments
title: Scaling Opik
canonical-url: https://www.comet.com/docs/opik/self-host/scaling
---

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.

## Reference Deployment: Live Production Environment

The following is based on an active, high-volume Opik deployment serving real users in production. Use it as a reference when planning your own infrastructure.

### Workload Profile

| Metric | Value |
|--------|-------|
| Active users (daily) | ~600 |
| Traces ingested per day | 4–6 million |
| Weekly data ingestion | ~100 GB |
| Total traces stored | 40 million (400 GB) |
| Total spans stored | 250 million (3.1 TB) |
| Total data on disk | 5 TB |
| Select queries per second | ~80 |
| Insert queries per second | ~20 |
| Rows inserted per minute | Up to 75K |

### Infrastructure

**Opik Backend** — 10 pods
- 3 CPU cores, 5 GB memory per pod

**Opik Python Backend** — 12 pods
- 1.5 CPU cores, 2 GB memory per pod

**Opik Frontend** — 3 pods
- Lightweight reverse proxy, minimal CPU and memory requirements

**ClickHouse** — 2 replicas, 1 shard
- 30 CPU cores, 230 GB memory per replica
- 15 TB gp3 SSD storage per replica (3000 IOPS, 250 MiB/s throughput)

### Key Configuration

| Setting | Value |
|---------|-------|
| Async ClickHouse inserts | Enabled |
| Rate limiting | 10,000 events per 60 seconds per client |
| Max query execution time | 60 seconds |
| Max memory per query | 10 GB |
| Ingestion method | Batch endpoints (up to 1,000 items/request) |

This deployment runs comfortably at 10–20% average CPU utilization on ClickHouse, with headroom for traffic spikes up to 40–50%.

## 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:

| Environment | CPU (vCPU) | RAM (GB) |
|-------------|------------|----------|
| Development | 4 | 8 |
| Production | 13 | 32 |

### Instance Guidance

| Deployment | Instance | vCPUs | Memory (GiB) |
|------------|----------|-------|--------------|
| Dev (small) | c7i.large | 2 | 4 |
| Dev | c7i.xlarge | 4 | 8 |
| Prod (small) | c7i.2xlarge | 8 | 16 |
| Prod | c7i.4xlarge | 16 | 32 |

### Backend Service (Scales to Demand)

| Metric | Dev | Prod Small | Prod Large |
|--------|-----|------------|------------|
| Replicas | 2 | 5 | 7 |
| CPU cores | 1 | 2 | 2 |
| Memory (GiB) | 2 | 9 | 12 |

### Frontend Service (Always Responsive)

| Metric | Dev | Prod Small | Prod Large |
|--------|-----|------------|------------|
| Replicas | 2 | 3 | 5 |
| CPU (millicores) | 5 | 50 | 50 |
| Memory (MiB) | 16 | 32 | 64 |

## 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:

| Deployment | Instance |
|------------|----------|
| Small | m7i.2xlarge |
| Medium | m7i.4xlarge |
| Large | m7i.8xlarge |

### 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).

| Deployment | CPU cores | Memory (GiB) |
|------------|-----------|--------------|
| Minimum | 2 | 8 |
| Development | 4 | 16 |
| Production (small) | 6 | 24 |
| Production | 32 | 128 |

### Disk Recommendations

To ensure reliable performance under heavy load:

| Volume | Value |
|--------|-------|
| Family | SSD |
| Type | gp3 |
| Size | 8–16 TiB (workload dependent) |
| IOPS | 3000 |
| Throughput | 250 MiB/s |

Opik's ClickHouse layer is resilient even under sustained, large-scale ingestion, ensuring queries stay fast.

## Ingestion Best Practices

How you send data to Opik matters more than how much hardware you run. Optimizing your ingestion pattern is the single most impactful thing you can do for performance.

### Use Batch Endpoints

Opik provides batch ingestion endpoints that accept up to **1,000 items per request**:

- `POST /v1/private/traces/batch`
- `POST /v1/private/spans/batch`

Instead of sending 1,000 individual HTTP requests (each triggering a separate database insert), a single batch request handles them all at once. This dramatically reduces connection overhead and ClickHouse insert pressure.

<Tip>
The **Opik Python SDK batches automatically** — no code changes needed. This guidance applies primarily to direct API integrations or custom SDK implementations.
</Tip>

### Enable Async Inserts

For production deployments, enable async ClickHouse inserts. This allows Opik to buffer writes and flush them in larger, more efficient batches rather than committing each insert individually.

### Set Query Limits

Protect your ClickHouse cluster from runaway queries:

| Setting | Recommended Value |
|---------|-------------------|
| Max execution time | 60 seconds |
| Max memory per query | 10 GB |
| Max concurrent queries per user | 2–4 |

These limits prevent a single expensive query from impacting ingestion performance or other users.

### Monitor What Matters

Key metrics to watch for a healthy deployment:

- **ClickHouse CPU utilization** — target 10–20% average, safe spikes up to 40–50%
- **Insert rate** — rows inserted per minute, should be steady without drops
- **Query duration** — p95 and p99 query times; investigate if consistently above 10 seconds
- **Pod restarts** — frequent restarts may indicate health check timeouts under load

## 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.

## References

- [ClickHouse sizing & hardware recommendations](https://clickhouse.com/docs/guides/sizing-and-hardware-recommendations)