Question: I need a platform to build and deploy AI models quickly, can you suggest a solution?

If you want to quickly develop and deploy AI models, Together is a great choice. This cloud service is geared for fast development and deployment of generative AI models. It supports a range of models, including LLaMA-3, Arctic-Instruct, DBRX-Instruct and Stable Diffusion XL, and has scalable inference for high-scale workloads at fast and economical prices. It also offers collaboration tools for model fine-tuning and AI solution deployment, which makes it a good choice for companies that want to incorporate their own private AI models into their products.

Another good choice is Anyscale. This service is geared for developing, deploying and scaling AI applications, with features like workload scheduling, cloud flexibility, intelligent instance management and GPU and CPU fractioning for efficient use of resources. Anyscale is built on the open-source Ray framework and supports a broad range of AI models. It can cut costs by up to 50% with spot instances. It also has native integrations with popular IDEs and automated workflows for running, debugging and testing code at scale, making it a good choice for enterprise environments.

If you prefer a low-code approach, Gooey provides a single interface to many private and open-source AI models, including GPT-4o, LLaMA3, Gemini and Claude3. It makes it easy to build and deploy AI applications quickly with its low-code interface and hot-swappable AI models. Gooey is flexible, supporting a variety of use cases and offering tiered pricing, including a free starter plan and custom enterprise plans, so it should be useful for many organizations.

Replicate is another good option, particularly for those who value ease of use and simplicity in deploying AI models. It provides a library of pre-trained, production-ready models for tasks like image and text generation, fine-tuning and image restoration. Replicate's API-based service lets developers deploy and scale models with one-line deployment and automatic scaling. Pricing is based on hardware usage, so it should be economical for many use cases.