Cheap, aggregate-first analytics platform for self-hosted data

Executive Summary

Modern data platforms are expensive because they store event-level rows and run heavy compute. An aggregate-first analytics tool that stores summaries in the customers own DB/bucket and provides automated analysis and anomaly detection can cut costs for 80 percent of use cases. Cloud storage and compute bills have become a visible line-item for analytics teams, pushing teams to seek cheaper models rather than more features. The source complaint is about cost and a desire to store aggregates in their own DB, which aligns with a wider shift to customer-owned data lakes and lakehouse architectures. Advances in efficient time-series anomaly detection, materialized view tooling, and serverless compute make fast, automated analysis on aggregated data feasible without a full row-level stack. In short, rising vendor bills plus mature lightweight analytics libraries and ubiquitous object storage (S3/GCS) create a narrow window to sell an aggregation-first, self-hosted analytics alternative. Focus on aggregate-first storage plus automated analysis to target the 70-80 percent of common analytics queries that do not require event-level data. The source explicitly notes aggregates are adequate about 80 percent of the time and wants data kept in their own database or bucket, which creates a product wedge: provide pre-built aggregation schemas, lightweight time-series and CTR-style analytics, and anomaly detection that runs on small, pre-aggregated tables. Combine that with a self-hosted or bring-your-own-storage model (S3, Snowflake, Postgres, cloud buckets) to deliver immediate cost savings versus row-level vendors, and use automated inference to create aggregations and recommend rollups from existing event schemas to speed time-to-value.