Tinybird sells the weeks you get back

Five seconds is a curious amount of time. In a meeting, it barely registers. On an analytics screen, it is enough to make a customer wonder whether the button worked. Dub, the link-management company, encountered that distinction when its Redis-based analytics grew slow and inflexible. A link clicked somewhere in the world had become a small administrative ordeal somewhere else.

• The job: turn incoming events into SQL-powered APIs that applications can use.
• The bargain: ClickHouse performance, with ingestion and serving infrastructure included.
• The useful lesson: judge the database alongside the engineering time around it.

Dub considered ClickHouse, the analytical database. The attraction was obvious; maintaining a cluster was less charming. Tinybird offered the surrounding plumbing, too. Dub’s published account reports a functional MVP in two weeks and queries roughly 100 times faster. The purchasing decision concerned both speed and who would spend their afternoons keeping it available.

“We were excited about the performance of ClickHouse, but not the overhead.”

Steven Tey, founder and CEO, Dub

The customer is on the other side of the query

Tinybird occupies a particular corner of the data business: analytics that somebody uses inside a software product. A developer wants a customer to inspect traffic, monitor usage, or understand an event while it still matters. The database must answer quickly, but the answer also needs a dependable route into the application.

That route is Tinybird’s product. Incoming data enters through an HTTP Events API or connectors. Developers write SQL to filter, combine, and aggregate it. Those queries become published HTTP endpoints. An application requests the result and displays it. Managed ClickHouse does the analytical work underneath; Tinybird supplies infrastructure and tools around it.

It is a useful distinction from a business-intelligence dashboard bought for employees, or a database bought as an engine alone. Tinybird sells developers a way to build the feature themselves. The interface belongs to the customer’s product. Tinybird can remain comfortably out of sight.

The expense hiding behind the invoice

Humblytics, a small analytics business, supplies a more domestic version of the problem. Its published customer story describes slow queries, frozen dashboards, and recurring maintenance sprints on TimescaleDB. Continuous aggregations pushed CPU usage to 100%. Improving the analytics had interrupted the analytics.

A working Tinybird starter kit helped persuade the founders. The migration took about a month: test on their own site, backfill, write to both systems, then shift customers gradually. They also changed update-based session tracking to an insert-based approach. This required changing the data model, not merely the connection string.

Their reported bill fell from $400-500 a month to less than $50. Their maintenance sprints disappeared. That is the practical attraction: a small team can spend its next week on something a customer might actually notice.

Data had a warehouse. Developers wanted a door.

The company’s origin follows the same logic. Tinybird says its founders worked in companies with plentiful data but poor developer access: the information sat in warehouses behind organizational gatekeepers. A secure, real-time REST API offered a way to put it to work. Founded in Madrid in 2019, the business subsequently established a New York presence.

Its five founders are Jorge Gómez Sancha, Sergio Álvarez-Leiva, Javier Santana, Javier Álvarez Medina, and Raul Ochoa. Sancha is CEO; Álvarez-Leiva is COO. The company’s stated principles favor ownership, quick feedback, documented decisions, and respectful disagreement. That is a sensible constitution for a company whose customers are trying to shorten their own development cycles.

The backing grew from a reported $3 million seed round to a $37 million Series A in 2022 and a $30 million Series B led by Balderton in June 2024. Together, those rounds total $70 million. Investors funded the proposition that software teams would increasingly need data while their users were still looking at the screen.

A database beside the database

Ghost makes the market position particularly clear. Its native web analytics uses Tinybird, while its publishing system retains MySQL for core application data. Ghost(Pro) users receive analytics inside the publishing interface. Self-hosted installations have specific version and Docker setup requirements. The two databases have different jobs, and the user need not inspect the arrangement.

Tinybird’s wider customer roster includes Canva, FanDuel, Vercel, Framer, and Resend. Those names span design, betting, web development, and email. Their common ground is software with events to understand and users waiting for answers. Tinybird’s expertise lies in analytical SQL, ClickHouse operations, ingestion, and the delivery of results into products.

The closest buying comparison is ClickHouse Cloud or a self-managed ClickHouse deployment. The question is how much of the ingestion, API, deployment, and monitoring work a team wants to assemble. An existing PostgreSQL or warehouse setup may be perfectly adequate when data volumes are modest or a daily report does the job.

Try the workload, then buy the time

Tinybird offers a limited free tier and paid cloud plans, with custom arrangements for larger SaaS and enterprise workloads. Compute, storage, concurrency, and support affect the economics. A customer’s migration saving is an invitation to measure, rather than a universal discount coupon.

Developers can also work locally using Tinybird’s Docker environment. Santana says feedback changed the original cloud-focused approach. Local iteration brings the data project closer to the application’s development loop, though laptop memory and single-node execution impose limits.

The pattern worth copying is restrained: choose one expensive query, test realistic traffic, compare results, and migrate gradually. If the workload depends heavily on transactional updates, rethink the model before moving it. If nobody needs fresh answers, urgency is an expensive decoration. Tinybird earns its place when faster data and fewer infrastructure chores make a useful product easier to ship.