Why AI Model Serving
Deploying AI models in-house usually requires significant resources, expertise, and infrastructure. With our AI Model Serving, businesses can instantly integrate AI/ML capabilities into their applications via a cloud-based API—without the burden of building, training, or managing models themselves. Whether you choose Model-as-a-Service for instant deployment or Bring Your Own Model on dedicated GPUs, our confidential computing environment ensures top-tier security and compliance. Scale effortlessly, reduce costs, and focus on innovation while we handle the complexities of AI infrastructure.
Key Features of AI Model Serving
Flexible and On-Demand
Deploy and run proprietary models tailored to specific business needs and dynamically scale with demand, optimizing your compute resources, that are fully managed.
Easy Integration and Cost Efficient
Easily integrate embedding models via a universal API with a full management stack, while reducing infrastructure expenses with pay-as-you-go pricing and managed hosting.
Security, Compliance and Monitoring
We offer robust security with built-in role-based access control (RBAC) within a technical assured environment and real-time tracking, logging, and model lifecycle management.
Full Control and Interoperability
Self govern CI/CD pipelines, model versioning, and automated deployments or build upon our support for multiple frameworks (TensorFlow, PyTorch, ONNX, etc.) and deployment options.
Choose a model, or bring your own!
Users can choose from our pre-offered models for seamless integration into their applications.
Llama 4 Maverick
Llama-3.3-70B
DeepSeek-R1-70B
Inference-Multilingual-e5l
Inference-bge-m3
Bring Your Own Model
