Atracker vs Tinybird

Atracker

Professional tracker for media buying and paid traffic

Atracker is a powerful analytics and tracking system designed for media buyers and performance marketers. It allows you to control every click, optimize ROI, and scale profits. Built for media buying teams, Atracker records every click and conversion in real time, so you can see exactly which campaigns, creatives, placements and traffic sources make money and which ones drain budget. Detailed reports break performance down by any parameter you pass, letting you cut losing zones fast and reinvest into what converts. Because tracking, reporting and optimization live in one system, performance marketers spend less time stitching spreadsheets together and more time scaling profitable paid traffic campaigns with confidence. Typical users are affiliate marketers, media buying teams and agencies running paid traffic across native, social and search sources, where a small optimization decision changes profitability. Atracker centralizes click data, campaign costs and conversion events, so reporting stays consistent no matter how many sources are connected. Because every parameter is tracked, teams can compare offers, landing pages, angles and placements side by side, spot patterns early, cut waste and push budget toward the combinations that return the highest ROI. The result is a clearer picture of paid traffic performance and faster, better informed scaling decisions for performance marketing campaigns of any size.

Tinybird

Build real-time analytics APIs over your data

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.