Agentic AI for data science
Create custom AI agents in minutes. Share them instantly and evolve continuously.
Supporting research at
Introducing Nova:
Science's most powerful AI
The future of research, guided by Nova, our Science AI.
Run simulations, analyze large datasets, and train AI models all in one place. Nova connects to our intuitive APIs to deliver seamless functionality.
Stress-free scaling
Flexible, scalable compute engines run on the cloud, not through on-premise infrastructure, so you have limitless resources with zero wait time.
Collaboration, built in
Camber Stash simplifies dataset sharing and management across teams, enabling real-time collaboration and synchronized research.
Bring your own code
Easily customizable to your specific code packages or use our preconfigured popular scientific libraries and applications for fast, easy set-up.
AI-powered workflows.
Accelerated research.
Genomics
Run any workflow with our no-code Science Apps.
Physics Simulations
Low code APIs for on-demand, scalable science.
Cloud Bursting
Mirror on-prem clusters in the cloud to ease workload pressure
Benefits
AI-powered analysis
Our agent runs your research end-to-end.
Low/no-code deployment
Access CPU/GPU clusters, run simulations, and train models through intuitive interfaces—no engineering team required.
No wait times
Skip the line and launch workloads on demand - no queueing, no delays.
Scale science, not servers
Infrastructure-free compute for instant scalability with zero hardware investment.
Work together, anywhere
Collaborate across teams and organizations to sync work, data, and insights instantly.
Unlimited storage
Store and access massive datasets without worrying about limits or local capacity.
Pay only for what you use
Affordable, on-demand compute for every team, at any scale.
"Without Camber it would be almost impossible to support my group. We love using Camber!"
“It’s unbelievable how simple it is. It just works. Camber is fantastic.”
“Camber is an amazing tool for undergrad and grad education. I plan on using it for all my future workloads.”
