Deploy any open-weight model.
Declare the model in the UI. ContextOS provisions the runtime on dedicated GPU hardware. No inference framework to install, no CUDA to configure, no batching to tune. GPU utilisation is a first-class autoscaling signal.
Production AI inference.
Without the ML ops.
ContextOS stands up production AI inference instantly. Open-weight models, an authenticated gateway, and a continual RAG pipeline.
All managed by the platform.
Problem
The ML ops tax on every inference deployment.
Building an AI-native app isn't just a software problem. It's an infrastructure problem that consumes weeks before your product exists.
GPU infrastructure is a full-time job.
Provision GPU instances, configure CUDA environments, install inference frameworks, and tune batching parameters before serving a single request.
RAG pipelines are five systems in a trench coat.
Ingestion, chunking, embedding, vector storage, and retrieval. Assembled manually, wired together by hand, kept in sync forever.
Exposing the endpoint is its own project.
Configure TLS, set up authentication, add rate limiting, and manage API keys for every consumer before any real user reaches the model.
Everything your AI app needs.
Provisioned as one stack.
Solutions
ContextOS provisions every service, connects them automatically, and secures them by default.
RAG as one service.
Document ingestion, chunking, embedding, vector storage, and retrieval, assembled and connected automatically. Your app queries the pipeline through one authenticated endpoint. Continual or on-demand ingestion.
Authenticated gateway, automatic.
TLS termination, certificate rotation, authentication, rate limiting, and load balancing at the edge. Add a web gateway and your inference endpoint is production-grade the moment it goes live.
One platform to rule them all
A full-stack web app on ContextOS.
Every layer below is provisioned from the ContextOS UI. Every connection is established by the Zero Trust Bridge. Every credential is generated and rotated automatically.
What changes for your team
The work you'd be doing without ContextOS, replaced by the work you actually wanted to do.
Before
After
Provision GPU instances, configure CUDA, install inference frameworks, tune batching.
Declare a model runtime.
GPU allocation, batching, and autoscaling managed automatically.
Assemble a RAG pipeline from five separate systems and keep them in sync.
Add a RAG pipeline service.
Every component provisioned and connected end to end.
Configure TLS, set up auth, add rate limiting, and manage API keys for every consumer.
Add a web gateway.
ZEndpoint exposed with TLS, auth, rate limiting, and load balancing automatically.
GPU capacity is either over-provisioned and expensive, or under-provisioned and slow.
GPU utilisation is a first-class autoscaling signal.
Inference capacity scales up and down with demand.
How ContextOS makes this possible.
Technology
Every use case runs on the same platform.
4 architectural pieces work together to make this stack possible.
The Unified Layer
One resource pool across compute, storage, and networking.
Autoscaling
Policy-driven scaling for CPU, GPU, and stateful workloads.
Managed Services
Production-ready services provisioned from one catalog.
Zero Trust Bridge
Service-to-service auth, credential rotation, mutual TLS.
Your AI stack,
running in minutes.
Join the closed beta. Ship your first AI app this week.