MLOps & registry.
Model and prompt registry, evals, deployment and drift monitoring — versioned and auditable.
The operational backbone for AI — a registry for models, prompts and adapters, with evals, staged deployment and drift monitoring, so what ships is measured, versioned and auditable.
- Category
- AI & ML
- Deployment
- Managed → air-gapped
- Governance
- IAM · encryption · audit
Three steps to running.
Version models, prompts and adapters with lineage.
Quality and safety evals gate every promotion in CI.
Detect data and quality drift in production and alert.
MLOps & registry, in full.
Version, stage and promote models and prompts with lineage.
Quality and safety evals gate every promotion.
Detect data and quality drift in production and alert.
Every deploy traces to data, version and approval.
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";
// MLOps & registry on Aether Cloud
const res = await aether.ai.mlops_registry({
model: "aether-40b",
input,
stream: true,
});
for await (const token of res) process.stdout.write(token);At a glance.
- Registry
- Models · prompts · adapters
- Evals
- Gated in CI
- Monitoring
- Data + quality drift
- Lineage
- Deploy → data → version
- Audit
- One trail
Built for real work.
Governed model deployment
Prompt and adapter management
Production quality monitoring
On one model, not stitched together.
The usual stack runs mlops & registry 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 — mlops & registry sits next to the rest of the catalog, one identity, one bill.
The provider that runs Aether runs your mlops & registry — 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.
Registry, evals, deployment and drift monitoring — with one audit trail across data, model and approval.
Yes — evals in CI block a bad model or prompt from promoting.
Yes — production data and quality drift are detected and alerted.
Your boundary, your choice.
Pairs well with.
The simulation-native foundation model itself, served as an API — the core every other service builds on.
Low-latency, streaming inference for every model size, with pinned versions and structured output.
Pre-wired multi-node GPU clusters with scheduling, checkpointing and fault tolerance.
Customer-controlled fine-tuning and adapters on data that never leaves your boundary, with eval gates.
Managed notebooks wired to the data platform and GPU compute for exploration and analysis.
Build a working app, agent or simulation by describing it, deployable in a click. See Aether Studio.
Run MLOps & registry on Aether Cloud.
Model and prompt registry, evals, deployment and drift monitoring — versioned and auditable. Deployable managed, in your VPC, on-prem or fully air-gapped — talk to us about the configuration your workloads and your boundary require.