Bring your models to life with speed and accuracy
You worked hard on that model. OctoML can help you get it to the finish line.
Deploy with ease
OctoML uses the latest optimization techniques to shrink model size, reduce latency, and maintain accuracy, making it easier and faster to deploy cutting edge models to production.
Compatible across deep learning frameworks
Create your model in any framework — including TensorFlow, PyTorch, Keras, MXNet, CoreML and ONNX — and switch between frameworks to maximize your productivity.
Deploy on the cloud, hardware, or edge
Run your model across diverse hardware targets from server-class GPUs and CPUs to specialized accelerators (FPGAs, ASICs), mobile phones, IoT and edge devices.
Read about our work
How OctoML is designed to deliver faster and lower cost inferencing
2022 will go down as the year that the general public awakened to the power and potential of AI. Apps for chat, copywriting, coding and art dominated the media conversation and took off at warp speed. But the rapid pace of adoption is a blessing and a curse for technology companies and startups who must now reckon with the staggering cost of deploying and running AI in production.
OctoML attended AWS re:Invent 2022
Last week, 14 Octonauts headed out to AWS re:Invent. We gave more than 200 demos showing how OctoML helps you save on your AI/ML journey, and gave away a dream trip to one lucky winner.
Faster machine learning everywhere
Model acceleration through 5 engines and packaged for 100+ hardware targets.
Get the best performance and lowest cost for running models in production.
Deploy in minutes using the OctoML CLI which outputs a Docker image package.
The open-source deep learning compiler that is the backbone of OctoML.