Time-series.
Purpose-built time-series storage for telemetry, metrics and sensor streams.
A database built for time-stamped data — high-rate ingestion, automatic downsampling and retention, and fast range queries over metrics, telemetry and sensor streams.
- Category
- Databases
- Deployment
- Managed → air-gapped
- Governance
- IAM · encryption · audit
Three steps to running.
Millions of points per second with time-series compression.
Continuous aggregates and rollups keep queries fast as data grows.
Optimized range, gap-fill and interpolation queries.
Time-series, in full.
Millions of points per second with compression tuned for time-series.
Automatic rollups and continuous aggregates for fast queries.
Age out raw data while keeping summaries indefinitely.
Optimized windowed, gap-fill and interpolation queries.
Provision it in a few lines.
Every service is reachable from the same SDK, CLI and infrastructure-as-code — one identity, one bill, one audit trail across the whole catalog.
import { aether } from "@aether/sdk";
// Provision time-series and query it
const time_series = await aether.databases.create({
service: "time-series",
name: "app",
region: "us-1",
});
const rows = await time_series.query(`select * from events limit 10`);At a glance.
- Ingest
- Millions of points/sec
- Compression
- Time-series optimized
- Rollups
- Continuous aggregates
- Retention
- Age raw, keep summaries
- Queries
- Range · gap-fill · interpolate
Built for real work.
Infrastructure and app metrics
IoT and sensor telemetry
Digital-twin histories
On one model, not stitched together.
The usual stack runs time-series in one product, the model in another and the data in a third — and the seams between them are the cost. Aether Cloud runs it on the same platform that serves the model, governs your identity and deploys into your boundary, with the rest of the catalog one hop away.
No stitching a vector DB to one place, a warehouse to another and a model to a third — time-series sits next to the rest of the catalog, one identity, one bill.
The provider that runs Aether runs your time-series — so the data and the model never leave the same governed boundary to talk to each other.
Need a capability that isn’t here yet? The model writes and deploys it into the same boundary — the catalog is a starting point, not a ceiling.
Good to know.
Purpose-built time-series storage — feeding digital twins and predictive maintenance on the same platform as the model.
Yes — millions of points per second with compression and downsampling.
Yes — age out raw data while retaining summaries indefinitely.
Your boundary, your choice.
Pairs well with.
Managed Postgres-compatible SQL with HA, read replicas, backups and point-in-time restore.
Elastic document and key-value stores for low-latency, high-throughput workloads.
Managed embeddings and similarity search at scale — the retrieval layer under grounded apps.
Sub-millisecond managed cache for sessions, hot data and rate limiting.
A property-graph database for relationships, knowledge graphs and path queries.
Columnar, separation-of-storage-and-compute warehouse for analytics at scale.
Run Time-series on Aether Cloud.
Purpose-built time-series storage for telemetry, metrics and sensor streams. Deployable managed, in your VPC, on-prem or fully air-gapped — talk to us about the configuration your workloads and your boundary require.