Being active in the AI field since 2014, I know this is one of the biggest bottlenecks for enterprises performing training, evaluation, or inference on AI models.
About the CEO
My name is Harel Boren, a serial entrepreneur, party to 4 technology exits, and 8 patents. Prior to founding SwarmOne, I founded Inspekto, later acquired by Siemens, where I introduced the world to the “Autonomous Machine Vision” category, an AI-driven platform for anomaly detection on manufacturing lines. There was nothing more gratifying than seeing numerous QA managers eliminating all the integrator-centric fuss associated with prior technologies, and enjoy an out-of-the-box run-and-gun QA platform.
I decided to replicate the same concept in AI infrastructure, and founded SwarmOne – an Autonomous AI Infrastructure Platform built to abstract away the heavy lifting of DevOps, MLOps, and SecOps involved in the AI LifeCycle. This time around I’m up to relieving data scientists of the current painful infra-setup process, and to focus exclusively on their science: AI training, evaluation, and inference.
At the heart of The Platform is a lightweight client that performs a real-time “workload X-ray” on your models, datasets, parameters, and environment. This analysis feeds into SwarmOne’s autonomous scheduling engine that makes intelligent decisions on where and how to run the workload (some complex AI driven logic driving this too).
About SwarmOne
The Platform’s most distinctive breakthrough is that (as opposed to all contemporary technologies) it disaggregates the hardware from the user experience, beginning with a one-click install on GPU nodes. Whether you’re training on local infrastructure, major cloud GPUs, or leveraging SwarmOne’s own “compute burst-mode”, the developer experience remains consistent.
SwarmOne Studio – a no-code solution running on your own compute (whether on premises or in the Cloud) – lets enterprise teams build and deploy models using a seamless interface, eliminating the need for ML framework expertise. SwarmOne Studio is model-agnostic, i.e. it can run any model, allowing you to support proprietary architectures without any manual optimization. It also frees no-code to be done INSIDE the enterprise, freeing from the high costs of 3rd party providers, and their inherent limitations on the number of models available.
Technically, SwarmOne deploys a new, futuristic stack that starts right above CUDA and goes all the way to the user interface. An Autonomous AI Infra Platform that just works. It applies cutting-edge optimization techniques (like DeepSpeed, Axolotl, and gradient checkpointing) tailored dynamically per workload, based on model type, data dimensions, precision settings, available infrastructure, and much more.
The result is instant running, high throughput, reduced runtime, and lower operational overhead (actually: no operational overhead!) – all achieved without modifying the user’s code, and ready to go within seconds.
Monitoring and observability are built-in via native integrations with popular experiment tracking tools, such as Weights and Biases, MLFlow, and others.
Importantly, the platform’s key value is independent of commoditized hardware prices, and plots a way out of the nickels-and-dimes competition to the bottom. Its value lies with speed to execute, with the elimination of DevOps, MLOps, SecOps and FinOps efforts, and the utilization of 100% of the data scientist’s time.
SwarmOne transforms infrastructure into an invisible layer – not by simplifying Kubernetes or Slurm, but by replacing them altogether with an intelligent, autonomous platform.
From workload setup and run, to scheduling, optimization, execution, and monitoring, the entire AI lifecycle is managed automatically – and autonomously. Whether you’re an enterprise with existing compute or a research team scaling fast, SwarmOne offers a stack that empowers results, and eliminates roadblocks on the highway to the future of AI.
Yours in service,
Harel Boren
CEO, SwarmOne
