Netflix Atlas
Netflix Atlas: Open-source, self-hosted metrics storage with tagged data and distributed queries. Ranked #30 of 36 in Time-Series Databases by our editors (5.1/10); pricing: Open source; best for in-memory dimensional metrics systems.
At a glance
- Editor score5.1 / 10
- PricingOpen source
- Best forIn-memory dimensional metrics systems
- Paid fromNone
- DeploymentSelf_hosted
- Facts checked22 Sep 2026
Where it wins
- Tagged time-series model for dimensional metric analysis
- Atlas Stack Language with distributed query and aggregation
- Graph and Tags APIs with PNG, CSV, TXT, and JSON outputs
Where it doesn't
- In-memory storage may require careful capacity planning
- Self-hosted deployment requires infrastructure and operational ownership
- Integration options center on Spectator, SpectatorD, and Edda
Our verdict on Netflix Atlas
Netflix Atlas is an open-source backend for dimensional time-series data and operational metrics. It is designed for teams that need near-real-time monitoring, tagged metric selection, and distributed querying in a self-hosted environment. Data is stored in memory, while the tagged time-series model supports dimensional analysis across operational measurements. Atlas also provides metric instrumentation through Spectator libraries and accepts data through Spectator clients, UDP, Unix domain sockets, and file output.
Querying is centered on Atlas Stack Language, a stack-based language intended for working with time-series data. The distributed query and aggregation layer supports high-volume metric analysis, while the Graph API retrieves and visualizes results. The Tags API helps select metrics by tag, and graph output can be returned in PNG, CSV, TXT, or JSON formats. Retention policies and horizontal scaling are part of the deployment specifications, giving teams controls for managing stored data and expanding a self-hosted system.
Atlas fits organizations that want an open-source, in-memory metrics system with a focused dimensional model and API-driven access. Its ecosystem is centered on Spectator, SpectatorD, and Edda, so teams should assess how those components align with their existing instrumentation and operational stack. The self-hosted model also places deployment, capacity planning, and ongoing operation with the adopting team. Choose Atlas when tagged metrics, near-real-time access, and distributed aggregation are priorities; consider another time-series database when a broader integration ecosystem or a different storage approach is more important.
Netflix Atlas pricing
Netflix Atlas fact sheet
| Free plan | Not verified |
|---|---|
| Paid from | None |
| Deployment | Self_hosted |
| SQL support | No |
| Retention policies | Yes |
| Horizontal scaling | Yes |
| Query languages | Atlas Stack Language (ASL) |
| Ingestion methods | SpectatorD text protocol, UDP, Unix domain socket, StatsD |
| Deployment | Self-hosted |
| Support | Docs |
| Built for | Mid-market, Enterprise (editorial estimate) |
| Pricing | Open source |
| Website | netflix.github.io |
| Facts checked | 22 Sep 2026 |
Alternatives to Netflix Atlas
- Apache IoTDBA focused open-source choice for high-volume IoT ingestion, SQL, retention, and scaling.9.0
- Amazon TimestreamA managed AWS option with flexible ingestion, retention, SQL, and InfluxDB support.8.6
- LindormTSDBA broad compatibility layer for teams managing scalable telemetry and metrics workloads.8.1
See all Netflix Atlas alternatives →
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Reviewed by iTechGuides Editors · Editorial team · Updated Sep 2026
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