Tinybird

Tinybird lets developers turn streaming data into low-latency analytics APIs, taking the ClickHouse column store and wrapping it in the ingestion, versioning, and API-publishing layer that makes it usable by a product team rather than a data platform team. The problem it addresses is real-time analytics inside an application, which is a genuinely different problem from business intelligence. A dashboard a analyst opens twice a day can take four seconds to load. A usage graph rendered inside your product for every customer, on every page view, cannot - and it has to stay fast while aggregating over billions of rows. That workload is what column stores like ClickHouse were built for, and it is also why teams that try to serve it from their transactional Postgres end up with slow queries, heavy indexes, and a database under pressure from a feature that is not core to it. Tinybird's contribution is the operational layer. You stream events in over HTTP or from Kafka, define transformations as SQL pipes, and publish any pipe as a parameterised, authenticated REST endpoint in a single step. There is no service to write, no cache to invent, and no infrastructure to provision - the endpoint is the deliverable. Data sources and pipes are versioned and deployable through a CLI, so analytics behaves like code rather than like a console someone clicked through once. Pricing is freemium, with a free tier sufficient for prototyping and usage-based pricing on ingestion, storage, and processed data as volumes grow. Against running ClickHouse yourself, you trade some control and cost efficiency at very large scale for not having to operate a distributed column store. Against Snowflake or BigQuery, the difference is latency and purpose: those are warehouses built for analytical queries measured in seconds, not for user-facing endpoints measured in milliseconds. Against PostHog or a product analytics tool, Tinybird is infrastructure rather than a finished analytics product - you build the feature, it does not come pre-built. Choose it when analytics is a feature your customers use, not a report your team reads.