Metrics observability workloads are growing in scale, resulting in (a) higher cost to operate observability infrastructure, and (b) slower query latencies.

The usual approaches to deal with these are:
- sample data
- roll up data
- reduce data cardinality
- send less queries

All of these approaches compromise the coverage of the observability infrastructure and can result in missing important anomalous behavior.

Through our research, we have developed a radically new approach to achieve large scale, low cost, and low latency without compromising the coverage of the observability infrastructure.

Our system reduce querying cost and latency by 100x by using 2 key techniques:
- streaming precomputation
- sketch-based approximation

Our system is developed as a drop-in accelerator to an existing Prometheus-Grafana stack i.e. we require no changes to and replacement of Prometheus or Grafana.

We will release an open-source prototype in the coming months.