quickwit
Quickwit is an open-source, cloud-native search engine built in Rust, designed for observability workloads like logs and distributed traces. It decouples compute from storage, enabling sub-second search directly on cloud object storage (S3, Azure Blob, GCS) at significantly lower cost than traditional solutions.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Repository | quickwit-oss/quickwit |
| Owner | quickwit-oss |
| Primary language | Rust |
| License | Apache-2.0 — OSI-approved |
| Stars | 11.4k |
| Forks | 564 |
| Open issues | 776 |
| Latest release | def4e26e9 (2026-05-20) |
| Last updated | 2026-07-08 |
| Source | https://github.com/quickwit-oss/quickwit |
What quickwit is
Quickwit uses a stateless, distributed architecture with Elasticsearch-compatible APIs and native support for OpenTelemetry, Jaeger, and Kafka/Kinesis ingestion. Built on Rust and the Tantivy search library, it optimizes I/O paths for cloud storage and supports schemaless or strict-schema indexing with multi-tenancy, retention policies, and GDPR-compliant deletion.
Get the quickwit source
Clone the repository and explore it locally.
git clone https://github.com/quickwit-oss/quickwit.gitcd quickwit# follow the project's README for install & configurationNeed it deployed, integrated, or customized instead? DEV.co ships production installs.
Best use cases
Implementation considerations
- Verify that your ingestion pipeline (Kafka, Kinesis, Pulsar, or custom HTTP) is compatible; Elasticsearch-compatible ingest API mitigates some vendor lock-in but not all.
- Plan schema design upfront or accept schemaless indexing tradeoffs; strict schemas enable better optimization but require upfront definition.
- Test Elasticsearch/OpenSearch query compatibility against your existing queries; missing aggregations or endpoints may require query rewriting.
- Allocate resources for Kubernetes deployment (Helm chart provided) or design containerized orchestration; cloud-native is the assumed deployment model.
- Budget for cold-start latency on sub-second cloud storage queries; benchmark your data volume and query patterns before production commitment.
When to avoid it — and what to weigh
- Real-time metrics ingestion required now — Metrics support is listed as roadmap only; currently only logs and traces are supported. Do not assume metrics are production-ready.
- Strict high-availability requirements for indexing — HA is available for search but indexing HA is only available with Kafka sources. Other ingestion paths do not guarantee HA indexing out-of-the-box.
- Heavy reliance on advanced Elasticsearch features — Quickwit supports a 'large subset' of Elasticsearch/OpenSearch API; not all endpoints, DSL queries, or aggregations are documented as complete. Requires vetting against your specific query workload.
- Organization unfamiliar with cloud-native, stateless architectures — Quickwit's design assumes comfort with object storage, cloud infrastructure, and horizontal scaling; it is not a drop-in replacement for traditional, stateful search appliances.
License & commercial use
Quickwit is licensed under Apache License 2.0 (Apache-2.0), a permissive OSI-approved license that permits commercial use, modification, and distribution with minimal restrictions (attribution required, no warranty or liability).
Apache-2.0 permits commercial use and modification. However, verify your internal compliance policies around dependency auditing, as Quickwit is a complex distributed system with transitive dependencies. Consider engaging with the maintainers ([email protected]) for commercial support or SLAs if required; no commercial offering is documented in the provided data.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Active |
| Documentation | Strong |
| License clarity | Clear |
| Deployment complexity | Moderate |
| DEV.co fit | Good |
| Assessment confidence | High |
Rust-based implementation reduces memory safety risks. OpenSSF Scorecard presence indicates security awareness. Considerations: review cloud storage credential management (S3/Azure/GCS), network policies for multi-tenancy, encryption at rest/in-transit (not detailed in provided data), and deletion task audit trails for GDPR compliance. No known exploits or CVEs mentioned; independent security audit status unknown.
Alternatives to consider
Elasticsearch / Elastic Cloud
Industry standard for observability; richer query DSL and ecosystem, but higher licensing and infrastructure costs. Quickwit claims 10x cost advantage for observability via cloud storage.
OpenSearch (AWS or self-hosted)
Open-source Elasticsearch fork; similar feature richness and API compatibility, but less optimized for cloud storage. Quickwit API compatibility aids migration but does not guarantee feature parity.
Jaeger or Grafana Loki (specialized)
Jaeger excels at distributed tracing; Loki at log aggregation. Quickwit combines both in a single cloud-native system, but lacks metrics (roadmap). Choose if you need single-vendor simplicity or specialized performance in one domain.
Build on quickwit with DEV.co software developers
Quickwit enables sub-second search on cloud storage with Elasticsearch compatibility. Evaluate cost savings, test schema design, and pilot on Kubernetes. Review ES-API compatibility for your queries before full migration.
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quickwit FAQ
Can I migrate from Elasticsearch to Quickwit without rewriting my log shippers or queries?
Is Quickwit production-ready for critical observability pipelines?
What cloud storage backends does Quickwit support?
Does Quickwit support metrics?
Software developers & web developers for hire
Need help beyond evaluating quickwit? DEV.co is a software development agency offering software development services and web development for teams of every size. Our software developers and web developers build custom software, web applications, APIs, and open-source observability integrations — and maintain them long-term.
Ready to optimize your observability stack?
Quickwit enables sub-second search on cloud storage with Elasticsearch compatibility. Evaluate cost savings, test schema design, and pilot on Kubernetes. Review ES-API compatibility for your queries before full migration.