#9 in Vector & RetrievalUpdated 2026-08

Quickwit

by Quickwit · Sub-second search and analytics engine on cloud storage

36 — BenchRank score out of 100

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Overview

Quickwit is a search and analytics engine that indexes logs and traces and queries them directly on object storage such as Amazon S3, MinIO or Ceph. Written in Rust on the tantivy library, it decouples compute from storage with stateless indexers and searchers, and exposes a REST API. Indexing is schemaless, with retention and lifecycle policies.

Best for: DevOps and data engineers running log and trace search over large volumes on cheap object storage.
Pricing: No prices are shown; the site describes Quickwit as open and free community-based software and offers a demo booking, with no published plans.
Runs on: Self-hosted

Strengths and trade-offs

Strengths

Queries data directly on object storage: S3, MinIO or Ceph
Native OpenTelemetry and Jaeger support for logs and traces
Runs single or multi-node, on-premise or on Kubernetes
Retention policies and targeted deletions for GDPR requests

Trade-offs

Built for low query rates on large volumes, not high-QPS search
Needs your own object store and queue (S3, Kafka) wired up first
Site shows no pricing, licence terms or support commitments
Banner says Quickwit has joined Datadog; future direction unstated

How this score is made up

Each dimension is scored out of 100 and combined into the headline score using fixed weights.

MCP support: 0 out of 100
API quality: 0 out of 100
Documentation: 70 out of 100
Agent friendliness: 65 out of 100
Changelog: 100 out of 100
Marketing site structure: 35 out of 100
Operational trust: 15 out of 100

Measured, but not part of the score

Useful to know, but not a mark for or against the product — so these do not affect the ranking.

Openness: 85 out of 100
Maintenance: 100 out of 100

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