Quickwit is a cloud-native search engine for observability, and we have just released a significant upgrade with performance improvements, extended Elasticsearch API compatibility, and better integration with OpenTelemetry tools, Grafana, and Jaeger.

This release is also a significant milestone as we have been witnessing users of the nightly version of Quickwit deploy clusters with hundreds of nodes, ingest hundreds of terabytes of data daily, and enjoy additional cost savings. We are very excited to put it in the hands of all of our users.

One report particularly compelling is coming from a company ingesting hundreds of terabytes of logs daily and migrating from Elasticsearch to Quickwit, dividing their compute costs by 5x and storage costs by 2x while increasing retention from 3 to 30 days. They also increased durability, accuracy with exactly-once semantics coming from native Kafka support, and elasticity; you can read more about their performance achievements in our release blog post [0].

This gives us great confidence in our ability to deliver a robust OSS alternative to Elasticsearch, Datadog, or Splunk and we are eager to get feedback and suggestions. To give you a glance at our roadmap, here are the next key features:

- Quickwit on AWS Lambda (Q1)

- Native distributed ingest (Q1)

- Pipe-based query language (Q3-Q4)

- Metrics support (Q3-Q4)

[0] Release blog post: https://quickwit.io/blog/quickwit-0.7

[1] Open Source Repo: https://github.com/quickwit-oss/quickwit

[2] Home Page: https://quickwit.io