Quickwit is an open-source, Rust-built search engine for logs and traces that runs directly on object storage. Learn about its features and Datadog acquisition.
Category
Databases
Pricing
Open source under Apache License 2.0 for self-managed deployment; enterprise and managed support now offered through Datadog following its acquisition of Quickwit, from Free (open source, self-hosted); Datadog-managed offering priced separately by Datadog
Verified
Not yet
Last updated
July 19, 2026
Founded
2020
Headquarters
San Francisco, California, USA (founding team originally distributed across Paris, Tokyo and New York)
APIOpen SourceSelf-Hosted
Overview
Quickwit is a search engine designed specifically for the scale and cost challenges of modern log management, letting teams run sub-second searches directly against data stored in cheap object storage like Amazon S3, rather than requiring that data to be duplicated into expensive, locally-attached search indexes.
Founded in 2020 and built by a team with deep search-technology expertise, Quickwit was acquired by Datadog in January 2025 to strengthen Datadog's log management and observability capabilities, particularly for regulated industries with strict data residency requirements. As part of the deal, Quickwit committed to releasing a major update under the Apache License 2.0 to keep the technology open for its existing community.
Key Features
Quickwit is written in Rust and built on the Tantivy search library, using vectorized processing and SIMD instructions for high query throughput. Its architecture separates compute from storage, so search capacity can scale independently of data volume, and it queries object storage directly rather than requiring a separate, costly ingestion pipeline into local disk-based indexes.
The engine is schemaless, horizontally scalable, and supports multi-tenant partitioning, with native ingestion support for OpenTelemetry and Jaeger tracing formats, GDPR-compliant deletion, and a REST API for integration into existing observability pipelines. It can be deployed as a single node or as a multi-node cluster, on-premise or in the cloud.
Pricing
Quickwit itself is free and open source, distributed under the Apache License 2.0, so any organization can download and self-host it without licensing fees. Prior to its acquisition, Quickwit also offered commercial support and a managed cloud option directly.
Following the January 2025 acquisition by Datadog, enterprise and managed support for Quickwit's underlying technology is now accessed through Datadog's commercial products and pricing, while the open-source engine remains separately available for self-managed deployments.
Key Features
Search Directly on Object Storage — Queries data in place on S3, MinIO, Ceph and similar object storage systems without requiring a separate local-disk ingestion step.
Rust and Tantivy-Based Performance — Built in Rust on the Tantivy search library with vectorized processing and SIMD instructions for high-throughput, sub-second search.
Decoupled Compute and Storage — Search compute scales independently of data volume, allowing cost-efficient scaling for very large log and trace datasets.
Native Observability Data Support — Ingests OpenTelemetry and Jaeger tracing formats natively, making it suited for observability and distributed tracing pipelines.
Schemaless, Multi-Tenant Indexing — Supports schemaless data ingestion and multi-tenant partitioning for teams managing logs across multiple products or customers.
GDPR-Compliant Deletion — Built-in support for compliant data deletion, relevant for regulated industries with strict data retention rules.
REST API Access — Exposes a REST API for integration into existing log pipelines, dashboards and observability tooling.
Flexible Deployment Models — Deployable as a single node or multi-node cluster, on-premise or in the cloud, under the Apache License 2.0.
Pros & Cons
Pros
Free and open source under the permissive Apache License 2.0
Searches data directly on low-cost object storage, reducing infrastructure spend versus disk-replicated search engines
High-performance Rust and Tantivy-based architecture built for large-scale log and trace search
Native support for OpenTelemetry and Jaeger, easing adoption in existing observability stacks
Now backed by Datadog's engineering resources following the 2025 acquisition
Cons
Future commercial direction is now tied to Datadog's roadmap rather than an independent company
Self-managed deployment requires in-house expertise to operate and tune at scale
Smaller community and ecosystem compared to long-established engines like Elasticsearch
Documentation and tooling maturity may lag more established, decade-old search platforms
Enterprise support now flows through Datadog rather than a dedicated Quickwit team
Pricing
Open Source (Self-Managed) Free No subscription; self-hosted under Apache License 2.0
Datadog-Managed Offering Custom pricing (via Datadog) Enterprise agreement through Datadog
Frequently Asked Questions
What is Quickwit used for?
Quickwit is used as a search and analytics engine for logs and distributed traces, allowing teams to run sub-second searches directly on data stored in cloud object storage.
Is Quickwit free to use?
Yes. Quickwit is open-source software released under the Apache License 2.0, so it can be self-hosted and used without licensing fees.
Who owns Quickwit now?
Quickwit was acquired by Datadog in January 2025. The open-source engine remains available, while enterprise and managed support are now offered through Datadog.
What programming language is Quickwit built in?
Quickwit is built in Rust and uses the Tantivy full-text search library as its core indexing engine.
Does Quickwit require local disk storage?
No. Quickwit is designed to query data directly on object storage systems like Amazon S3, MinIO and Ceph, avoiding the need to replicate data onto local disks.
When was Quickwit founded?
Quickwit was founded in 2020 by a team of engineers with backgrounds in search technology.
Does Quickwit support distributed tracing data?
Yes. Quickwit natively supports ingesting OpenTelemetry and Jaeger tracing formats, making it suitable for observability use cases beyond plain log search.
How much funding did Quickwit raise before being acquired?
Quickwit raised a $2.6 million seed round in March 2022 led by firstminute Capital and FirstMark before being acquired by Datadog in January 2025.