Products / InfluxDB Cloud

InfluxDB Cloud

Collect, store, and analyze all time series data in a fully managed database. Query data using SQL for real-time insights.

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Scale your time series workloads in the cloud

Focus on what matters. Leave your infrastructure to us.

45x

Better write
throughput

90%

Reduction in
storage costs

100x

Faster queries for high
cardinality data

45x

Faster queries for
recent data

Compared to InfluxDB Open Source, see the Benchmarks Report

Tools and resources to help innovate faster

With a powerful set of data collection tools, client libraries, and APIs you can get data from everywhere.

Telegraf

InfluxDB Cloud uses Telegraf to collect time series data from, and to send it to databases, applications, systems, and IoT sensors. Telegraf is a plugin-driven server agent with over 300 plugins. It is written in Go, compiles into a single binary with no external dependencies, and requires a very minimal memory footprint.

Client Libraries

Access InfluxDB Cloud via a set of powerful client libraries. Currently, there are client libraries for Arduino, C#, Go, Java, JavaScript, Kotlin, Node.js, PHP, Python, R, Ruby, Scala, and Swift. These client libraries are easily accessible in a new tab in the UI.

API

Programmatic access to InfluxDB Cloud is available through a robust set of APIs. These APIs are common across InfluxDB Open Source and InfluxDB Cloud allowing you to write code once and run it locally against an open source version of the database or against the elastic database as a service.

InfluxDB Cloud features

Single datastore for all time series data

Collect, analyze, and store metric, event, and tracing data to open new use cases
Simplify data pipelines and remove unnecessary tooling

Native SQL support

Easy learning curve with native SQL support for queries
An intuitive, visual script editor that provides multiple ways to build and write SQL
Explore and analyze data using SQL and community-driven Apache Arrow Flight SQL-compatible libraries

Low latency queries

Work with leading-edge data. Keep live and recently queried data in “hot” storage tier, built using Apache Arrow, an in-memory columnar format optimized for speed

Unlimited cardinality

Ingest high volume and high cardinality data without impacting performance
Continuously ingest, transform, and analyze hundreds of millions of time series data points per second

Open and interoperable with data ecosystems

Many other open source ecosystems utilize Apache Parquet, an open data interchange format
Use of data science tools to operate directly on Parquet files to power machine learning or other higher-order analytical tasks
Connect to Google Data Studio, and other BI or data warehouses with open source ODBC and JDBC plugins based on Flight SQL

Superior data compression

High compression storage using Apache Parquet file format
Persist data to cloud object store, saving more data in less space, while also reducing costs