Question: I need a solution that allows me to deploy and scale machine learning models with serverless GPU inference, can you help?

If you need a way to deploy and scale machine learning models with serverless GPU inference, Mystic is worth a look. It's a cost-effective, scalable architecture that integrates directly with AWS, Azure and GCP. Mystic supports multiple inference engines, parallelized GPU usage and automated scaling so data scientists and engineers can concentrate on model development. The service is built on a managed Kubernetes environment and open-source Python library, with pricing that scales with per-second compute usage.

Another contender is Cerebrium. It's built for serverless GPU infrastructure, with pay-per-use pricing that's a fraction of what you'd pay otherwise. Cerebrium comes with features like 3.4s cold starts, 5000 requests per second and 99.99% uptime, so you can scale with low latency. It also comes with real-time logging and monitoring, so you can debug and monitor performance. The service lets you use your own AWS/GCP credits or on-premise infrastructure, so you can get the best of both worlds in terms of cost savings.

RunPod is another option worth considering. It's a globally distributed GPU cloud that can be used for serverless ML inference with autoscaling and job queuing. You can spin up GPU pods immediately and use more than 50 preconfigured templates for frameworks like PyTorch and Tensorflow. RunPod features like instant hot-reloading for local changes and 99.99% uptime make it a good option for developing, training and running AI models. Pricing varies depending on the type of GPU instance and usage, so you can pick what's best for your needs.

If you're looking for a platform that supports a broad range of AI models and offers cost savings, Anyscale is worth a look. Built on the open-source Ray framework, it offers workload scheduling with queues, cloud flexibility and smart instance management. Anyscale supports a range of AI models and integrates with popular IDEs and persists storage, so it's good for both traditional and custom generative AI models. It also offers a free tier and flexible pricing, including volume discounting for larger enterprises.