InfluxData provides InfluxDB, a purpose‑built time‑series database that ingests and queries high‑resolution operational data at millions of points per second with low latency and predictable storage costs. It supports edge‑to‑cloud deployment and offers client libraries, an open‑source Telegraf collector, and standard APIs so engineering teams can integrate real‑time telemetry into AI/ML pipelines and monitoring stacks.
Funding
$30M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
SVFounders
Product
Problem
Modern telemetry, edge devices, and physical AI applications generate continuous high‑resolution data streams that traditional relational databases cannot ingest or query efficiently, leading to latency, high storage costs, and limited real‑time insight.
Solution
InfluxData offers InfluxDB, a purpose‑built time‑series database designed for real‑time operational data at scale. The engine provides millisecond‑level ingest rates for millions of points per second while maintaining predictable storage and compute costs through efficient compression. It supports edge‑to‑cloud continuity, allowing data to be captured on devices, at the edge, or in the cloud without pipeline redesign. Integrated client libraries for multiple languages (Python, Go, Java, JavaScript, .NET) enable developers to write and query data via HTTP APIs or the high‑performance Arrow Flight protocol. The platform includes open‑source components such as Telegraf for data collection and offers seamless integration with existing stacks, making it suitable for AI/ML workloads that require rapid telemetry loops.
Target Audience
Primary customers are engineering teams building telemetry, IoT, and physical‑AI systems—such as aerospace, manufacturing, and energy operators—who need a scalable time‑series database for real‑time analytics and automated decision making.
Features
- High‑throughput ingest engine capable of millions of data points per second with low latency
- Efficient columnar storage and compression optimized for continuous, high‑resolution time‑series data
- Edge‑to‑cloud deployment model supporting on‑prem, edge, and cloud environments with a single database engine
- Language‑specific client libraries (Python, Go, Java, JavaScript, .NET) that provide batch writes, Arrow Flight‑based SQL/InfluxQL queries, and error handling abstractions
- Open‑source Telegraf agent for plug‑and‑play collection of metrics, logs, and events from diverse sources
- Native integration points for AI/ML pipelines, enabling real‑time sensor data to feed predictive models and automated control loops
- Compatibility with existing monitoring and visualization tools via standard APIs and extensible plugin architecture