One platform. All Services.
Add a vector DB, RAG pipeline, model runtime, queue, or gateway from the ContextOS UI. Each provisioned and connected in minutes.
Build the AI product.
Not the AI infrastructure.
Vector DB, RAG, GPU inference, embeddings, queues. ContextOS provisions them all as one stack. From your first commit.
Problem
The AI infrastructure problem
The AI infrastructure problem
nobody talks about.
Building an AI-native app isn’t just a software problem. It’s an infrastructure problem that consumes weeks before your product exists.
Ten vendors to wire up.
Vector DB, embeddings, RAG, LLM runtime, queues, gateway. Each from a different provider. Each with its own API, billing, and security model.
RAG is a five-step build.
Ingestion, chunking, embedding, vector storage, retrieval. Five systems to build, wire, and maintain before you can query a document.
GPU is expensive and hard to scale.
Too much capacity and you burn money. Too little and your model can't keep up. Provisioning right takes a full-time engineer.
Everything your AI app needs.
Provisioned as one stack.
Solutions
ContextOS provisions every service, connects them automatically, and secures them by default.
GPU managed automatically.
Add a vector DB, RAG pipeline, model runtime, queue, or gateway from the ContextOS UI. Each provisioned and connected in minutes.
RAG as one service.
Add a vector DB, RAG pipeline, model runtime, queue, or gateway from the ContextOS UI. Each provisioned and connected in minutes.
One platform to rule them all
A typical AI-native 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
Less of the work you have to do. More of the work you want to do.
Before
After
Provision GPU clusters manually. Configure CUDA, build inference servers, manage request queuing, auto-scale GPU instances.
Declare the model. Done.
GPU allocation, batching, and autoscaling managed by the platform.
Build RAG from five separate systems. Wire them together and keep them in sync as the pipeline evolves.
Query a RAG pipeline immediately.
Declared as one service. Assembled and connected for you.
Manage credentials for every service. Configure network access. Keep secrets in sync across environments.
Credentials handled at runtime.
Zero Trust Bridge generates, rotates, and delivers them automatically.
Weeks of plumbing before launch. By the time the infrastructure works, your product hasn't shipped.
Stack live the same day.
Infrastructure is never the bottleneck.
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.