InfluxDB Cloud

Achieves 90% reduction in storage costs compared to InfluxDB Open Source

Uses Telegraf, a plugin-driven server agent with over 300 plugins written in Go, to collect time series data from databases, applications, systems, and IoT sensors with minimal memory footprint

Collect, analyze, and store metric, event, and tracing data to open new use cases and simplify data pipelines

InfluxDB Cloud Dedicated offers a single tenant cluster with capacity-based pricing and support included

Access InfluxDB Cloud via powerful client libraries for Arduino, C#, Go, Java, JavaScript, Kotlin, Node.js, PHP, Python, R, Ruby, Scala, and Swift

Programmatic access to InfluxDB Cloud through a robust set of APIs common across InfluxDB Open Source and Cloud allowing code to run locally or against the database as a service

Connect to Google Data Studio and other BI or data warehouses with open source ODBC and JDBC plugins based on Flight SQL

Interoperable with data ecosystems using Apache Parquet, an open data interchange format allowing use of data science tools to operate directly on Parquet files for machine learning or analytical tasks

Provides 100x faster queries for high cardinality data compared to InfluxDB Open Source

Provides 45x better write throughput compared to InfluxDB Open Source

Delivers 45x faster queries for recent data compared to InfluxDB Open Source

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

Free to start with InfluxDB Cloud Serverless, only paying for what you use

Explore and analyze data using SQL and community-driven Apache Arrow Flight SQL-compatible libraries

Easy learning curve with native SQL support for queries with an intuitive visual script editor that provides multiple ways to build and write SQL

InfluxDB Cloud Serverless provides automatic, elastic scaling that adjusts resources based on demand

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

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