Automated Model Deployment at Peak Performance Anywhere
Optimize and package your trained model in minutes so you can deploy it to any hardware target for faster, more cost-efficient inference. See the benefits with our free model analysis.
Empowering teams building intelligent applications
Maximize operational efficiency
Eliminate the need to hand tune models to improve engineer productivity while reducing cloud spend
Achieve hardware independence
Manage compute scarcity and become cloud provider agnostic without cost/performance compromise
Accelerate time-to-market
Innovate faster than your competition with model deployment that takes hours, not weeks
Improve end user experience
Create delightful product experiences by achieving optimal latency and throughput
To evaluate OctoML’s value to our content moderation work at WatchFor, we optimized our key vision model and realized 1.2x - 3x higher throughput and substantial inference speedups. We deemed the results worth moving to production.
Matthai Philipose
Senior Principal Researcher, Microsoft
Three quick steps to deploy faster models to any hardware
1. Upload your pre-trained model and define your hardware
You can upload any model or choose one from our accelerated model hub. Next, select your current hardware – we support over 80 cloud targets from all three providers.
2. Set your goals and evaluate prospective hardware
Define your hardware parameters to find the ideal balance of latency, throughput and cost. See real, actionable before/after performance data and select your ideal instance type.
3. Deploy your model optimized for chosen hardware
The OctoML platform automatically produces a downloadable container with your model that is accelerated, configured, and ready to deploy on the hardware target of your choice.
OctoML Customers & Partners
Reduce AI/ML production workload costs more than 70% by automating hardware independence with OctoML
Empower your team with OctoML
Get started by requesting a model analysis where we’ll use the OctoML Platform to show you the performance gains and time savings you could realize with your model, either on your existing or alternative hardware targets.