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Here’s a video showcasing the platform and demonstrating how to easily manage new data and changes using the RAG stack of your choice: https://www.youtube.com/watch?v=-OdWRxMX1iA
If you want to try release.ai out, we’re offering a sandbox account with limited free GPU cycles so you can play around and get a feel for Release.ai: https://release.ai. We suggest playing around with some of the RAG AI templates and adding custom workflows like in the demo video. The sandbox comes with 5 free compute hours on an Amazon g5.2xlarge instance (A10 with 24GB VRAM, 8vCPUs and 32GB). You will also get 16 GB and 4vCPUs for cpu workloads such as web servers. You will be able to run an inference engine plus things like an api server, etc.
After the sandbox expires, you can switch to our free plan, which requires a credit card and associating an AWS/GCP account with Release to manage the compute in your cloud account. The free account provides 100 free managed environment hours a month. If you never go over, you never pay us anything. If you do, our pricing is here: https://release.com/pricing.
For those that like to read more, here’s the deeper background.
It’s clear that open source AI and AI privacy are going to be big. Yes, many developers are going to choose SaaS offerings like OpenAI to build their AI applications, but as open source frameworks and models improve, we’re seeing a shift to open source running on cloud. Security and privacy is a top concern of companies leveraging these SaaS solutions, which forces them to look at running infrastructure themselves. That’s where we hope to come in: we’ve built Release.ai so all your data, models and infrastructure stay in your cloud account and open source frameworks are first class citizens.
Orchestration - Integrating AI applications into a software development workflow and orchestrating their lifecycle is a new and different challenge than traditional web application development. Release also makes it possible to manage and integrate your web and AI apps using a single application and methodology.
To make orchestrating AI applications easier, we built a workflow engine that can create the complex workflows that AI applications require. For example, you can automate the redeployment of an AI inference server easily when underlying data changes using webhooks and our workflow engine.
Cost and expertise - Managing and scaling the hardware required to run AI workloads is hard and can be incredibly expensive. Release.ai lets you manage GPU compute resources across multiple clouds with different instance/node groups for various jobs within a single admin interface. We use K8s under the covers to pull this off. With over 5 years of building and running K8s infrastructure our customers have told us this is how it should be done.
Getting started with AI frameworks is time consuming and requires some pretty in-depth expertise. We built out a library of AI templates (https://docs.release.com/release.ai/release.ai-templates) using our Application Template format (which is kind of a super docker-compose: https://docs.release.com/reference-documentation/application...) for common open source frameworks to make it easy to get started developing AI applications. Setting up and getting these frameworks running is a hassle, so we made it one click to launch and deploy.
We currently have over 20 templates including temples for RAG applications, fine tuning and useful tools like Juypter notebooks, Promptfoo, etc. We worked closely with Docker and Nvidia to support their frameworks: GenAI and Nvidia NEMO/Nims. We plan to launch community templates soon after launch. If you have suggestions for more templates we should support, please let us know in the comments.
We’re thrilled to share Release.ai with you and would love to get your feedback. We hope you’ll try it out, and please let us know what you think!