AI & ML, on one model.
The model, the compute to train it and the tools to ship it. 8 services, Aether-native and governed by one trust story — deployable managed, in your VPC, on-prem or fully air-gapped. And when you need one that isn’t listed, the model builds it.
Everything in ai & ml.
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.
Model and prompt registry, evals, deployment and drift monitoring — versioned and auditable.
Build a working app, agent or simulation by describing it, deployable in a click. See Aether Studio.
Run quantum circuits on real QPUs and high-performance simulators through one API, with hybrid quantum-classical workflows.
From request to running.
Train, fine-tune or call the model with the data platform and GPU compute one hop away.
Gate every model and prompt on quality and safety evals before it ships.
Deploy versioned endpoints with streaming, batching and drift monitoring, in your boundary.
One SDK, whole catalog.
import { aether } from "@aether/sdk";
// The Aether model on Aether Cloud
const res = await aether.ai.the_aether_model({
model: "aether-40b",
input,
stream: true,
});
for await (const token of res) process.stdout.write(token);- Model
- Aether 7B · 40B · 280B MoE
- Context
- 1M tokens
- Serving
- Streaming + batch, autoscaled
- Governance
- Registry · evals · drift
- Boundary
- Managed → air-gapped
Your boundary, your choice.
The rest of the platform.
Run ai & ml on infrastructure you trust.
AI & ML on Aether Cloud — Aether-native, governed and deployable into the boundary your data requires. Talk to us about the services and the deployment your workloads need.