Deci’s platform optimizes your deep learning models, to maximize the utilization of any cloud compute instance, enabling compute-flexibility and cost-efficient cloud inference without compromising accuracy.Book a Demo
Operating your deep learning models in cloud environments is a costly matter and high inference costs may dramatically cut down your product’s profitability. Companies' clear goal is to offer their end-users a high performing deep learning model, without compromising on the model’s accuracy and SLA. However, customers are also challenged with the appropriate cloud-based compute configuration required for their scalable model inference tasks. For example, in today’s general-purpose cloud environments, customers may need to select and trade-off between using a lower-cost cluster of CPU-based compute instances to the higher-cost, top-performing single dedicated GPU-based compute instance. At the end of the day, companies face the dilemma of executing model inference models at a lower operating margin but with high performance or sacrificing the user experience with poorly-performing deep learning models.
Connecting your model to the platform and using the AutoNAC technology will enable a cost-effective deep-learning model workload that is performance-optimized for any selected target cloud compute instance. You can save cloud operating costs by maximizing the utilization of your existing cloud environment or even consider switching to cheaper instances, while preserving the same model accuracy and SLA.
All without compromising on performance or accuracy!
Boost your trained model’s throughput/latency for any hardware, with Deci’s AutoNAC algorithmic optimization, without compromising on accuracy.
"We are excited to be working with Deci's platform - it provided amazing results and achieved 4.6x acceleration on a model we ran in production and helped us provide faster service to our customers.”