Aether Cloud.
The infrastructure under the model.
Managed cloud, elastic compute, GPU-as-a-service, a data platform and inference endpoints — the same substrate that trains and serves Aether, available to run your own workloads. Deploy managed, in your VPC, on-prem or fully air-gapped, with the trust, observability and provenance built in rather than bolted on.
- Compute
- Elastic CPU · GPU
- Serve
- Endpoints · fine-tune
- Data
- Unified platform
- Deploy
- Managed → air-gapped
Run the model, your way.
The cloud is the model's home turf — the infrastructure built to train, serve and govern Aether, opened up so you can run inference, fine-tuning and your own workloads on it, in the boundary your data requires.
Managed cloud
The fastest path to the model — Aether served on managed infrastructure with autoscaling, SLAs and usage-based billing. Nothing to operate; an endpoint and a key.
Inference endpoints
Low-latency, streaming inference for every model size, with rollout forwarding for simulation, function calling and structured output — pinned versions and reproducible behaviour.
Fine-tuning & customization
Customer-controlled fine-tuning and adapters on data that never leaves your boundary, with eval gates so a tuned model is measured before it ships.
Private & sovereign deploy
The same model in your VPC, on-prem or fully air-gapped, with an export-control posture and an audit trail — zero bytes leaving the boundary when that's the requirement.
Observability
Traces, evals, cost and drift across every app and endpoint, with provenance that answers “why did it do that” for an operator and an auditor alike.
Security & compliance
SOC 2 / ISO 27001 controls, capability-gated tool use, secrets isolation and tenant separation — the trust story is part of the platform, not bolted on.
GPUs by the hour, clusters on demand.
The accelerated compute that trains Aether — elastic, scheduled and fault-tolerant — available for your training, inference and simulation jobs.
GPU-as-a-service
On-demand and reserved GPU capacity — latest-generation accelerators by the hour or the cluster, with fast interconnect for distributed training and high-throughput inference.
Elastic compute
Autoscaling CPU and GPU pools that expand for a training run or a batch sweep and contract when idle — you pay for the work, not the rack.
Training clusters
Pre-wired multi-node clusters with the scheduling, checkpointing and fault tolerance long runs need — bring a job, not a cluster-ops team.
Batch & simulation jobs
Massively parallel rollout and simulation jobs — the same engine that trains Aether, available for your own forward-rolled workloads.
One platform for data, analytics and AI.
The full lakehouse stack as one Aether platform — open lakehouse storage, data engineering, Spark, streaming, SQL warehousing, unified governance, vector and orchestration — with one difference no incumbent stack has: the model lives here too. Data engineering, BI and AI run on one copy of the data, one catalog and one identity, so nothing crosses a boundary to reach the model.
Open table formats over object storage with ACID transactions and time travel — one copy of the data for engineering, BI and AI. The lakehouse, without the lock-in.
Visual and code-first pipelines with scheduling, retries, schema-drift handling and column-level lineage — declarative ingestion and transform.
Managed Spark and distributed compute for large transforms and ML feature pipelines — lake-native, autoscaling and spot-aware.
Exactly-once stream processing for real-time pipelines, enrichment and detection — feeding dashboards, alerts and the model live.
Columnar, separation-of-storage-and-compute SQL at petabyte scale — on the one platform that also runs the model against the same governed tables.
One catalog, lineage and access model across every table, file and model — row/column security and audit from source to output.
Managed embeddings, indexing and hybrid search so apps and agents are grounded in your governed data, with provenance per answer.
The model, training, fine-tuning and serving on the same governed data — no copy to a separate AI stack, no boundary crossed to reach the model.
Durable workflows that schedule and chain the whole pipeline — ingest, transform, train, serve — with retries and lineage.
Everything the hyperscalers ship, on one model.
Compute, storage, networking, databases, analytics, identity, integration and the rest — the full surface of AWS, Azure and GCP, Aether-native and governed by one trust story. And when you need a service the hyperscalers don't have, the model builds it.
If the service doesn't exist, the model builds it.
The platform is the model — and the model writes software, simulates systems and stands up infrastructure. Describe the service you need and Aether generates it on the cloud: a bespoke database engine, a domain-specific simulator, a compliance control no vendor ships. The catalog is a starting point, not a ceiling.
Your model, your boundary.
The same platform across every deployment posture — from fully managed to fully air-gapped, and hybrids in between.
Run Aether on infrastructure you trust.
From a managed endpoint to an air-gapped cluster, Aether Cloud is the compute, serving and data platform under the model — with the trust story built in. Talk to us about the deployment your workloads and your boundary require.
Security and compliance details live in the Trust Center.