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Sub-second search & analytics with less
engine on cloud storage
Sub-second search & analytics
engine on cloud storage
The fastest search engine on cloud storage
- Optimized file format that reduces the number and size of I/O requests
- Smart I/O scheduling that maximizes throughput
- Written in Rust, no GC, vectorized processing, and SIMD included
- Powered by tantivy, the fastest search engine library
A perfect fit for logs and traces
- Data is stored and searched on unlimited, cost-efficient cloud storage
- Search and troubleshoot errors directly on object storage in sub-second
- Schemaless indexing
- OpenTelemetry and Jaeger native
- Highly available and trivially horizontally scalable
- Multi-tenancy: optimized indexing with many indexes and partitioning
- Retention and lifecycle policies
- Support for infrequent, targeted deletions for GDPR use cases
An architecture built for ease of deployment
- Decoupled compute and storage
- Single/Multi-Node, on-premise, or cloud
- Stateless searchers and indexers
- REST API
An architecture built for performance
True decoupled storage & compute with sub-second latency
As opposed to traditional search technologies designed for high QPS on limited volumes of data, Quickwit is optimized for search on raw data where QPS remains low but volume is limitless. Leverage Quickwit’s core architecture in Rust and Tantivy for optimized CPU and processing power, to execute queries directly on object storage for improved performance at a fraction of the usual cost.See in Action
The technological building block for your log management solution
Quickwit is cloud-native! Easily deploy Quickwit in your existing environment, on-premise or on Kubernetes, plug it into the object storage (Amazon S3, MinIO, Ceph...) and distributed queue (Apache Kafka, Amazon Kinesis...) of your choice.
Your data, your way.
Trusted by devops and data engineers
Quickwit with its original and highly efficient architecture proved to be the ideal candidate to be paired with our OLAP database Clickhouse to run Search + OLAP workloads. During our testing, we have observed that Quickwit could sustain our production workload with efficiency and reliability.
> Ryad Zenine
Lead engineer at Contentsquare
At Nuclia, we need an alternative to lucene for BM25 search built in Rust and Tantivy is the best match. Tantivy allows us to build a distributed indexing engine with a great performance and clear design. Quickwit's codebase was a great inspiration for building projects.
> Ramon Navarro Bosch
CTO at Nuclia
While analyzing solutions to replace our slow legacy search solution, we selected Tantivy for its high indexing throughput, its low search latency and its high-quality code. Thanks to Tantivy, HumanFirst offers a solution that scales to our customers' needs and allows rapid iteration on their NLU data.
> André-Philippe Paquet
VP Engineering at HumanFirst
Elastic was too time-consuming to maintain, and we wanted a more down-to-earth solution with an S3-compatible backend. We were looking to write our own Tantivy implementation, but Quickwit was released, so we decided to give it a shot. Ever since it has been our fastest AND cheapest log management solution. Additionally, the compatibility with vector.dev was just the cherry on top of the cake!
> Loïc Tosser
Co-Founder & CTO at Kalvad
Open and Free Community Based Software
We believe a company's success lays in its ability to hone all of its data. We also understand that building and maintaining an end-to-end search and analytics solution is already tedious enough without having to add vendor lock ins or black box architectures. That's why we, at Quickwit, build and deliver community-based software that is open and free. Search is only the beginning.