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Supported by Tech Leaders
Why EfficientAI
Integrate with Prominent Cloud Providers
Your AI Workloads
EfficientAI
One-click model deployments
Serverless & Hosted Endpoints
Rate Limiting & Budgeting
Tenant Isolation
Centralized Monitoring & Observability
Centralized Billing
AWS Bedrock
AWS SageMaker
Azure Foundry
Azure ML
Azure OpenAI
Vertex AI
Multicloud AI Deployments
Save time and money with EfficientAI from Day 1
Up to 0%
of costs saved by choosing the cheapest deployment options.
Over 0
engineering hours saved.
Leverage Industry-Leading Foundational Models All in One Place for Your AI Journey
BYOC
Bring Your Own Cloud
Bring Your
Connect your existing cloud in minutes. Use efficientAI's control plane to seamlessly orchestrate your LLM workloads, while keeping your data plane secure within your own cloud environment.
Features
EfficientAI Simplifies LLMops and AI Workflows across all cloud providers
Unified Observability & Monitoring
Monitor tokens consumed and LLM performance.
Track and Visualize your experiment's data.
Monitor your hyperparameters.
Trusted by Enterprise Leaders
Abhishek Kumar Gupta
Senior Staff Engineer @ Nvidia
EfficientAI is great place to start deploying models quickly into production in a multi-cloud environment. It is a great tool for data scientists and machine learning engineers to securely deploy models in a few clicks.
Gururaj Nagaraj Rao
Principal Engineer @ Walmart
Efficient AI cloud platform definitely simplifies the multi-cloud management of deploying and operating LLM Models. It was impressive to see the demo of different capabilities this platform supports which would reduce the turnaround time to build and operate GenAI applications
Ipsita Bhattacharya
Senior Director @ NASDAQ
This platform would revolutionize various workflows by enabling seamless deployment of multiple LLM models across various cloud providers. With the advent of AI and its uptick in usage across various industry sectors,this surely hits the right set of challenges that the end user would have to navigate through eventually ( for instance, multi cloud multi model scalability metrics).
Frequently Asked Questions