Tinybird is a real-time analytics platform that lets developers build, deploy, and monitor data products instantly — without sacrificing speed or control. We rate it 82/100 — the best choice for teams that need sub-second analytics queries without learning yet another data stack.
Tinybird, founded in 2021 by Javi Santana and Miguel Fernández, launched as an answer to a persistent developer frustration: traditional analytics stacks are slow, complex, and require weeks of setup. Built on ClickHouse (a blazing-fast columnar database), Tinybird lets you ingest real-time data via HTTP, SQL, or Kafka, then instantly publish APIs that return sub-second queries — all without maintaining servers or writing infrastructure code. The platform has grown rapidly, trusted by teams at Miro, GitHub, and numerous Series B+ startups who need analytics that don't bottleneck their product.
Developers consistently praise Tinybird for eliminating the "analytics tax" — that months-long slog of setting up data warehouses, hiring engineers to build ETL pipelines, and waiting for queries to complete. On Product Hunt and Reddit, users highlight the speed of getting from idea to live dashboard (often 30 minutes), the transparent pricing, and the excellent CLI tooling. Some mention early-stage limitations around complex JOINs and certain analytical functions, but these are improving rapidly. A common sentiment: "This is what data analytics should have been from day one."
Free Tier: 100GB ingestion per month, 1M API calls, perfect for prototyping and small projects.
Starter ($29/month): 1TB ingestion, 100M API calls, priority support.
Growth ($99/month): 10TB ingestion, 1B API calls, custom domains, audit logs.
Scale ($399+/month): Unlimited ingestion, custom SLAs, dedicated support, bring-your-own-infrastructure option.
No long-term contracts. Cancel anytime. Public pricing is transparent with no hidden tiers.
Best for: Teams building SaaS products with real-time analytics features, startups that need fast dashboards without hiring data engineers, existing data warehouse users who want to expose live APIs, and developers who value simplicity over feature maximalism.
Not ideal for: Organizations deeply invested in Snowflake or BigQuery with complex legacy ETL logic, teams needing advanced ML integrations (though Tinybird has no philosophical opposition), or use cases requiring offline analytics on isolated networks.
Pros:
Cons:
Vercel Analytics Engine: If you're already on Vercel, their managed analytics engine offers similar real-time query patterns, but with less flexibility and higher lock-in.
Datadog RUM + Custom Events: Great for monitoring and product analytics combined, but pricier and more opinionated around agent instrumentation.
Segment + Data Warehouse: More flexible but requires assembling components yourself; slower time-to-value.
Materialize: Open-source streaming SQL if you want to self-host; requires Kubernetes and more operational overhead.
If you've ever cursed at Snowflake query times or spent weeks configuring data pipelines, Tinybird feels like relief. For teams shipping SaaS products that need live analytics features, the ROI is immediate: launch your first dashboard in an afternoon, let real-time data flow in, and spend your engineering time on product, not infrastructure.
The free tier is genuinely useful and attracts talented engineers who'll evangelize it internally once they realize how much faster they ship. The paid tiers are fair. And the team clearly uses their own product — the CLI is delightful, the docs are clear, and the roadmap is public and responsive to user feedback.
Tinybird isn't the "everything data platform" — it's the "analytics without the pain" platform. For that specific job, it's exceptional.
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