Cloud
The model training, inference & continuous-improvement cloud for Enterprise AI Agents.
We train, evaluate, deploy and continuously improve the small models behind your agents, served on dedicated GPUs through one OpenAI-compatible API — in our cloud or deployed entirely inside yours. Our Machine Learning team supports you along the journey to make your Specialized AI better.
nacecloud — the foundational layer
one openai-compatible api
The model lifecycle, closed
The layer underneath your agents.
- [01]
Train
specialized small models, on your data and workflows
- [02]
Eval
measured against your ground truth before anything ships
- [03]
Deploy
served on dedicated GPUs behind one OpenAI-compatible API
- [04]
Continuously improve
production feedback flows back into training
the foundation under your agents — in our cloud or entirely inside yours
Small models, built for the job
We train, eval, deploy and continuously improve models for you.
Three families of specialized small models, each trained on your data and evaluated against your ground truth before it ships — frontier-quality output on your domain, without frontier cost.
Small models for Agentic Use
Fast, reliable tool-calling and multi-step execution — the workhorse models your agents run on. Optimized serving (FP8, speculative decoding, prefix caching) delivers frontier-quality output on your domain at a fraction of frontier-API cost and latency.
- search
- sql
- http
observe → plan · loops until the task is done
Small models for Data Processing
Document extraction, grounding, classification and structured output at scale — tuned on your documents and workflows, evaluated against your ground truth, shipped straight into serving.
{
vendor·············0.99
date·············0.98
total·············0.97
items[]·············0.95
}
structured output · confidence per field
Small models for Institutional Reasoning
Models that encode how your institution thinks — policies, methodology, domain judgment — so agents reason the way your experts do, not the way the internet does.
Metamodel training & deployment
One base model. Ten thousand personalities.
Metamodels separate what a model knows from who it serves — personalization scales as adapters, not as deployments.
10,000 adapters
… +9,996 more
Model Personalization at scale
One base model, thousands of lightweight adapters — per client, per team, per engagement — hot-swapped at inference time. Personalization becomes a routing decision, not a new deployment. Your adapters remain your property, under an explicit opt-in data-use policy built to survive auditor review.
Adaptive Real-time Reasoning
Multi-task learning for real-time use cases with SLMs — one small model handling several live tasks within the latency budget of an interactive product, adapting as your workload mix shifts.
Why teams choose it
Built like a trusted utility, not an AI experiment.
Infrastructure that survives security review — yours to inspect, meter and move.
On-prem & private-cloud deployment
Runs in our cloud, your cloud account, or your own datacenter — see the four deployment tiers.
Continuous training, on your terms
Production feedback becomes better weights, on a schedule you control. Reviewer corrections turn into training data, and the resulting models reflect your methodology — deployed for your use only.
Compliance-first by construction
Trust tiers decide which hardware may touch which data — enforced by policy, not convention — and they map directly onto the four deployment tiers.
Unit economics you can see
Every request metered per caller and per model. Know what each workload costs, weekly.
No lock-in
OpenAI-compatible API, open-source serving stack (vLLM), portable deployment artifacts, multi-provider GPU economics.
Deployment spectrum
One platform. Four boundaries.
Same models, same OpenAI-compatible API, same serving stack at every tier. The only thing that changes is the boundary around your data — from our cloud to your metal.
Your data trains your models. The weights, adapters and eval sets stay yours, inside the boundary you pick.
Managed Cloud
Start on our GPUs, behind our API. Live in days.
- Shared GPU pool with strict tenant isolation
- Public API over TLS, no training on your data
- Usage-based pricing, no infrastructure to run
For: pilots, evaluations, mid-market teams moving fast.
Dedicated Cloud
Your own GPU cluster in our cloud, reached over private links instead of the public internet.
- Single-tenant compute, pinned to your region
- PrivateLink or VPC peering into your network
- Customer-managed keys, dedicated throughput
For: regulated teams that are cloud-first but done with shared infrastructure.
Your Cloud
The platform installs inside your AWS or Azure account. Data stays within your boundary.
- Runs in your VPC with your keys, your logs, your IAM
- Updates pull from our registry; nothing flows back out
- The deployment model our audit and banking customers run in production
For: banks, Big 4 firms, anyone whose security review starts with “show us the network diagram.”
Talk to us →On-Prem & Air-Gapped
Your datacenter, your hardware, zero outbound.
- No egress, no telemetry, offline license activation
- Signed offline update bundles on your schedule
- Ships as software on your servers or as a pre-configured appliance
For: government, defense, and sovereign environments where the network cable isn’t there.
Talk to us →| 01 Managed | 02 Dedicated | 03 Your Cloud | 04 Air-Gapped | |
|---|---|---|---|---|
| Where it runs | Our cloud | Our cloud | Your cloud account | Your datacenter |
| Network path | Public TLS | Private link | Inside your VPC | None |
| GPU tenancy | Shared | Dedicated | Dedicated | Dedicated |
| Updates | Continuous | Continuous | Pull from registry | Signed offline bundles |
Your agents deserve models that keep getting better.
Train, eval, deploy, continuously improve — with a Machine Learning team that supports you along the whole journey.
Talk to us