OwlyScan, our darknet search engine for companies, leverages Quickwit for indexing darknet content efficiently. Quickwit's Rust-based design ensures low server resource use and easy maintenance, speeding up our project's development. This efficiency sets us apart from competitors, making Quickwit a game-changer for us.

Stedi chose Quickwit for its fantastic performance and fully-serverless implementation, which allows us to offer search functionality to our customers that scales from zero cost at rest to practically infinite volume. The fact that it is written in Rust and optimized for AWS Lambda makes it a perfect fit for our architecture and infrastructure. We couldn't be happier with the results.

Formal selected Quickwit for its speed and scalability, which stood out in our benchmark comparisons. The combination of Quickwit's efficient architecture, along with the use of Rust are perfectly aligned with Formal technical vision. Quickwit enabled our customers to aggregate data and execute complex queries more efficiently. The ease of deployment, coupled with Quickwit's outstanding support, affirmed our confidence in Quickwit.

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!

At Rho, we needed to collect our application traces with no sampling, and to retain them for long periods of time. By providing a blazing fast search over cheap object storage, Quickwit has saved us up to 90% of our tracing telemetry costs, while maintaining the stability and availability of more expensive options.

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.

Milvus is a highly scalable and blazing-fast open-source vector database. It powers next generation AI applications with embedding similarity search and strives to make vector databases accessible to every organization. Tantivy is integrated into Milvus as an inverted index on scalar data and helps to accelerate filtering speed and full text search and it works awesome.

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.

Databend has chosen Tantivy for the implementation of its inverted index, a choice we believe to be the best for our Rust-based cloud data warehouse thus far. This integration enables us to offer users significantly faster full-text search functionalities within the warehouse, thereby substantially improving the efficiency of data retrieval.

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.

Tantivy is one of the best search libraries in existence. The flexibility, performance and code quality is world-class and the authors are very friendly and approachable. Combined with the safety from Rust's type system, this made it a no-brainer to choose Tantivy as the foundation of Stract's search index.

When building bloop's low latency code search engine, we wanted low level control of the search, without having to build a new system from scratch. Using Tantivy, we were able to get a reliable implementation up quickly, freeing up time to invest in other areas - which being in the early stages of product development is the most important thing.

We needed a fast solution in Rust and Python for full-text search that would work well on top of Lance columnar format. We also needed it to be an embedded solution since we're also embedded. Tantivy was the only choice that fit our needs. It was easy to integrate and performed very well. With Tantivy we're able to deliver better retrieval quality to LanceDB users.

ParadeDB selected Tantivy to power its BM25 scoring functionality due to its ergonomic developer interface, Lucene-inspired design, and high-performance search. Thanks to Tantivy, ParadeDB offers BM25 scoring within PostgreSQL, enabling our customers to get high-quality search natively within their database.

We developed MyScaleDB to allow users to query vector embeddings and structural data using SQL in a single platform. To incorporate full-text search and hybrid search features requested by our users, we selected Quickwit's Tantivy library for its rich features similar to Lucene, implementation in Rust for speed and security, and strong performance based on our benchmarking.

When building Tabby's repository context feature to retrieve relevant code snippets for code completion, we aimed for a solution that could be embedded directly into our binary distribution without the need to construct a separate service. By utilizing Tantivy, we were able to accomplish a reliable implementation swiftly, boasting exceptionally high performance in both indexing and searching.