ZeptoDB vs ClickHouse
Overview
Section titled “Overview”ClickHouse excels at analytical queries over large datasets. ZeptoDB is purpose-built for real-time time-series where microsecond latency, temporal operations, and agent-ready context are critical — whether the source is an exchange, a robot, a factory line, a fleet of vehicles, or a grid of meters.
Feature Comparison
Section titled “Feature Comparison”| ZeptoDB | ClickHouse | |
|---|---|---|
| Primary Use | Real-time time-series | OLAP analytics |
| Query Latency | 272μs (1M rows) | ~5ms |
| Ingestion | 5.52M events/sec | ~100K events/sec |
| Storage Model | In-memory + Parquet HDB | Disk-based columnar (MergeTree) |
| ASOF JOIN | ✓ (native) | ✗ |
| Window JOIN | ✓ | ✗ |
| EMA / VWAP | ✓ (built-in) | UDF required |
| xbar (time bucketing) | ✓ | toStartOfInterval() |
| Python Zero-Copy | 522ns | — (requires serialization) |
| Agent Memory | Native memory + exact/semantic cache layer | Separate stack required |
| JIT Compilation | LLVM JIT | Partial |
| SIMD | Highway (AVX2/512, NEON) | SSE4.2/AVX2 |
| Real-Time Ingestion | Lock-free ring buffer | Async inserts + merge |
| Feed Handlers | FIX, ITCH, Binance, Kafka | Kafka (via connector) |
| Clustering | Multi-node auto-sharding | ReplicatedMergeTree + ZooKeeper |
| License | BUSL-1.1, free Community | Apache 2.0 |
When to Choose ZeptoDB
Section titled “When to Choose ZeptoDB”- Sub-millisecond query latency is a requirement
- You need ASOF JOIN, Window JOIN, or built-in temporal analytics
- Real-time streaming ingestion at millions of events/sec
- Python zero-copy for ML, feature stores, or quant research
- Agent Memory for context retrieval, prompt cache, and AgentOps telemetry beside live events
- Physical AI, sensor fusion, autonomous systems, industrial control, or market data — any workload where time alignment matters
When ClickHouse May Be Better
Section titled “When ClickHouse May Be Better”- Large-scale batch analytics (billions of rows, seconds-acceptable latency)
- Complex OLAP queries with many JOINs on dimension tables
- Existing ClickHouse ecosystem and tooling investment
- Data warehouse use cases where disk-based storage is preferred
Performance Comparison
Section titled “Performance Comparison”| Workload | ZeptoDB | ClickHouse |
|---|---|---|
| Point query (1M rows) | 272μs | ~5ms |
| Aggregation (1M rows) | 185μs | ~3ms |
| ASOF JOIN | 410μs | N/A |
| Ingestion (single stream) | 5.52M/sec | ~100K/sec |
| Python result access | 522ns | ~1ms (serialization) |
Get started with the Quick Start Guide.