Time Series Startups funded by Y Combinator (YC) 2026

A Quant Neolab building toward RSI by leveraging markets as self-improving research environments. They work with frontier AI labs and academic groups building research harnesses, evaluation benchmarks, and post-training environments.

QuestDB is the data platform for mission-critical workloads where data cannot be lost and batch processing is too slow. It runs real-time trading analytics, grid monitoring, fleet telemetry and mission operations at millions of events a second, with years of history kept queryable. Data stays in open formats like Parquet, and you or any of your agents can query it in plain SQL. QuestDB started where the problem is hardest, on the electronic trading floor, and built outward into energy, aerospace and robotics. Today 7 of the largest 20 investment banks use it, as do energy companies and makers of aircraft, rockets and satellites. Its engineers work with each customer's team on the whole solution, from the live feed to the archive.

InfluxData, the creators of InfluxDB, delivers a modern Open Source Platform built from the ground up for analyzing metrics and events (time series data) for DevOps and IoT applications. Whether the data comes from humans, sensors, or machines, InfluxData empowers developers to build next-generation monitoring, analytics, and IoT applications faster, easier, and to scale delivering real business value quickly. Based in San Francisco, InfluxData customers include Autodesk, Cisco, eBay, and Coupa. Visit https://www.influxdata.com.

PipelineDB is an open-source PostgreSQL extension that enables realtime reporting use cases by continuously aggregating large streams of time series data down into summary data. It shines the most at larger scales, where storing lots of raw time-series data and aggregating it over and over again becomes inefficient. The amount of value that PipelineDB adds is directly proportional to the amount of continuous aggregation that an analytics use case can benefit from. PipelineDB should be used for analytics use cases that only require summary data, like realtime reporting dashboards. PipelineDB is not a good fit for exploratory analytics on siloed datasets.