InfluxDB 3 Enterprise

High-performance time series database. Start fast, scale faster.

Get Early Access to the Alpha

InfluxDB 3 Enterprise is a high-speed, fully-featured database that’s easy to start and scale

Run it where you need it—on bare metal, VMs, containers, or Kubernetes. Easily scale by adding new instances.

Why InfluxDB 3 Enterprise

Bringing the power of InfluxDB 3 to a simple single-node setup

High-Speed Ingest

Ingest billions of series with fewer CPUs and less RAM

Real-Time Querying

Query across any time range–from real-time to historical data analysis

Unlimited Cardinality

Ingest, transform, and query unlimited time series with unmatched speed and flexibility

Low-Cost Object Storage

Best-in-class compression and Parquet files store more data with less space

Seamless Integration

Integrate with the tools you love via Python-based plugin systems

Native SQL

Analyze data using simple SQL or InfluxQL, a SQL-like language for time series data

Built for developers, from startup to scale-up

Enterprise-Grade Scalability

High availability, read replicas, and long-range data compaction deliver seamless performance and scalability

Fine-Grain Security

ABAC, RBAC, and advanced tokenization all coming soon

Diskless Architecture

Maximum flexibility by operating directly on object storage for dynamic setups

Built-in Admin UI

Take control with unified database management—monitor, configure, and optimize everything from a single, intuitive interface

Slash storage costs by up to 90% with InfluxDB 3

InfluxDB 3 separates compute from storage, allowing efficient management of both active and historical data. With compressed Parquet files and storage in Amazon S3, it reduces storage footprints up to 4.5x, delivering massive cost savings without impacting performance.

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Open data standards drive performance and interoperability

InfluxDB 3 is built in Rust and the FDAP stack—Flight, DataFusion, Arrow, and Parquet—leveraging Apache-backed technologies to efficiently ingest, store, and analyze time series data at any scale.

F

Flight for efficient columnar data transfer

D

DataFusion for high-performance querying

A

Arrow for optimized in-memory columnar analytics

P

Parquet for high-compression storage