ZeptoDB vs InfluxDB
Overview
Section titled “Overview”InfluxDB is a popular time-series database for monitoring and IoT. ZeptoDB targets a different envelope — workloads where microsecond latency, ASOF JOIN, high-throughput ingestion, and agent-ready operational memory are non-negotiable.
Feature Comparison
Section titled “Feature Comparison”| ZeptoDB | InfluxDB | |
|---|---|---|
| Query Latency | 272μs (1M rows) | ~15ms |
| Ingestion | 5.52M events/sec | ~50K events/sec |
| Query Language | Standard SQL | InfluxQL / Flux |
| ASOF JOIN | ✓ | ✗ |
| Window Functions | ✓ (full SQL window) | Limited |
| EMA / VWAP | ✓ (built-in) | Flux function |
| Python Zero-Copy | 522ns | Client library (ms) |
| Agent Memory | Native memory + exact/semantic cache layer | Separate stack required |
| Storage | In-memory + Parquet HDB | TSM engine (disk) |
| Cardinality | No limit (symbol-partitioned) | High cardinality issues |
| JIT Compilation | LLVM JIT | ✗ |
| Feed Handlers | FIX, ITCH, Binance, Kafka | Telegraf plugins |
| Clustering | Multi-node auto-sharding | Enterprise only (paid) |
| License | BUSL-1.1, free Community | MIT (OSS) / Proprietary (Cloud) |
When to Choose ZeptoDB
Section titled “When to Choose ZeptoDB”- Microsecond query latency required
- Financial, industrial, or robotics time-series with ASOF JOIN needs
- High-throughput ingestion (millions of events/sec)
- Standard SQL preferred over InfluxQL / Flux
- Python zero-copy for ML pipelines and feature stores
- Agent Memory for operational agents over metrics, incidents, and telemetry
- High-cardinality data (fleets, devices, meters — no cardinality penalty)
When InfluxDB May Be Better
Section titled “When InfluxDB May Be Better”- Infrastructure monitoring with Telegraf ecosystem
- Simple metrics collection where ms latency is acceptable
- Existing InfluxDB/Grafana stack investment
- InfluxDB Cloud managed service preference
Get started with the Quick Start Guide.