Multi-Cloud Providers
We strive to provide the latest GPUs & cheapest compute by partnering with multiple providers. Our compute platform integrates with multiple cloud providers (AWS, GCP, Lambda & OCI), ensuring high-performance GPUs for AI training, inference optimization, and deep learning deployment.
Backed and Supported by
Multiple Compute Partners
- Companies often rely on a single compute provider, which can be costly for outdated GPUs and inefficient for deep learning deployment.
- Matrice.ai selects the cheapest and most efficient compute from multiple providers, including AWS, GCP, OCI, and Lambda Labs, ensuring optimal model selection and inference optimization.
Dedicated Compute for AI Workloads
- Reliability requires dedicated compute, especially for AI training, model monitoring, and hyperparameter tuning.
- Easily request dedicated compute for a specific period to reduce costs from our partners, ensuring high-performance GPUs for the latest computer Vision Transformers & CNN Models and MLOps workflows.
On-Demand Compute for Computer Vision
- Dedicated compute is ideal for long-term needs, but immediate compute needs can arise for AI inference, detection, and segmentation tasks.
- Reserve On-Demand Compute for new experiments with auto-shutdown features to save costs, while leveraging the latest state-of-the-art models for object tracking and activity recognition.
Auto-Management & Cost Optimization
- Manual start and stop of compute instances lead to unwanted costs and inefficient data management.
- Our platform automatically terminates compute instances after actions are completed, avoiding unnecessary expenses and ensuring seamless cloud storage integration for model versioning and data annotation tools like LabelBox.
