Discover the Camber Cloud Platform and unlock the power of data, compute and AI in the cloud for science and research. Run state-of-the-art workflows with just a single line of code. Camber is revolutionizing HPC with intuitive, on-demand APIs that deliver seamless cloud-based functionality. Start your 14-day free trial today and enjoy $100 in usage credits to explore our features risk-free.
Camber is the first scientific and research computing platform built with modern cloud container technologies for HPC and AI workflows.
Camber is a scientific programming syntax that seamlessly deploys HPC infrastructure in the cloud. We offer notebook and CLI user experiences, where in a single line of code, users can deploy on-demand complex HPC workloads. Use cases include research in computational chemistry, engineering, physics, astronomy and genomics data analysis. Users have access to a wide range of publicly available Camber Science Compute engines, Science Web Apps as well as the Camber Stash, our managed cloud storage.
HPC teams can connect their on-premise queuing system to Camber to reduce or even eliminate queue wait times. Camber containerizes HPC compute environments so they can be deployed, on demand in our cloud. Output is stored in the Camber Stash (object store).
Within minutes, individual research groups can get up and running with Camber. Use notebooks to prototype and deploy HPC jobs. Share output and results with the team members via our easy-to-use integrated object store.
Camber is a pay-as-you-go service using the payment method associated with your AWS Marketplace account. Pay only for what you use and cancel at any time. For more information about our service rates for Camber Credits https://docs.cambercloud.com/docs/engines-pricing/.
Managed services are accessed via our Science Engines and Apps and include OpenMPI, Apache Spark, Cloud Object Store, Dask, Docker as well as containerized versions of popular scientific codes and tools such as Gromacs, LAMMPS, Gadget, MESA, Nextflow and Snakemake.
Reach out to info@cambercloud.com to get specialized configurations and pricing for Camber on AWS Marketplace on a contract basis.
Highlights
Infrastructure-free HPC and AI computing. In a few lines of code, run scientific and research workloads on-demand in the cloud from a notebook or the command line.
Burst your jobs submitted to your on-premise resource into the cloud. Easily convert module files into Camber Science Engines. That can be shared to anyone within your organization. Researchers at your organization no longer need to wait in queues before their job is run.
Camber Science Apps allow researchers to run HPC and AI workloads with a no-code user-experience.
AWS Marketplace now accepts line of credit payments through the PNC Vendor Finance program. This program is available to select AWS customers in the US, excluding NV, NC, ND, TN, & VT.
You pay through a single usage-based dimension called Camber Usage Credits. One credit equals one U.S. dollar. Your bill combines two factors: hourly compute consumption and monthly storage. Compute runs on on-demand engines, and credits accrue only while a job is actively running. Larger engine sizes and GPU-accelerated jobs consume more credits per hour. Storage is billed monthly based on how much data you keep. Because pricing scales with what you actually use, your total varies with job size, run time, and stored data volume.
Top-of-mind questions for buyers
What counts as one Camber Usage Credit?
One Camber Credit equals one U.S. dollar. Credits are consumed as your jobs run and as you store data. Compute credits accrue only while a job status shows RUNNING. Storage credits accrue monthly based on the data you keep in managed object storage.
Am I charged credits when a job is idle or not actively running?
Compute credits accrue only while a job status is RUNNING. Jobs that are not actively running do not consume compute credits. Storage is separate and bills monthly based on the data you keep, regardless of whether any jobs run.
What drives credit consumption more — compute or storage?
Both charges combine on the same usage total. Compute credits scale with engine size, GPU use, and how long jobs run. Larger engines and GPU-accelerated jobs consume more credits per hour. Storage credits scale monthly with data volume kept. Heavy compute jobs typically drive cost; large stored datasets add ongoing monthly charges.
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