An Essential Guide to AI Hosting: Finding the Right Platform

Azure OpenAI Service

Azure OpenAI Service is a cloud-based platform that brings the power of OpenAI’s advanced language models to Microsoft Azure’s secure and scalable infrastructure. This service enables developers and businesses to integrate AI capabilities, such as natural language processing and conversational AI, into their applications. 

• Overview: API service accessible in Azure. Offers a playground to test prompts and content filters (violence/hate/etc.).
• Models: Several GPT flavors with varying context sizes, model sizes, and prices. 
• Deployment: Can run globally (requests routed to whatever region has capacity, higher throughput limits, latency may vary) or locked to a specific region. 
• Pricing: Token-based pricing. pay-as-you-go (PAYG) and Provisioned Throughput Units (PTU) are offered (PTU only if you have an MS account team).  
• RAG: Supported through Azure AI Search.
• Data Privacy: Customer data is not available to other customers, OpenAI, and not used to train/improve any MS or 3rdparty products or services. Offers a BAA for HIPAA compliance.

AWS Bedrock

AWS Bedrock is Amazon’s cloud service that simplifies the deployment of foundational AI models. It provides access to powerful pre-trained models, enabling businesses to build and scale AI applications quickly within the secure and flexible AWS environment.

• Overview: AWS Bedrock has some of the most popular models running and ready for access without any particular deployment needed. It offers a playground to test prompts, fine-tuning support, content filters (violence/hate/etc.), Prompt Flows (a Logic App-like builder supporting chaining of prompts, logic, other tools, and execution tracing), Agents, governance, and audibility.
• Models: 30-40 of the most popular models already running today. Custom models can be imported if based on Mistral, Flan, or LLaMA.
• Deployment: Models are already running.
• Pricing: Token-based pricing
• RAG: Supported through Knowledge Bases.
• Data Privacy:  Customer data is not available to other customers and not used to train/improve any products or services. It also offers a BAA for HIPAA compliance.
• Other: Supports a feature called Prompt Flows, which allows users to visually design the chaining together of various prompts to models, conditionals, and other tools. It supports guardrails, which are content filters for violence, hate, explicit, etc. It supports a playground for testing prompts and offers a full auditing of calls.

GCP Vertex AI

GCP Vertex AI is Google Cloud’s platform for developing, deploying, and managing machine learning models. It provides a unified interface for building custom models, automating workflows, and leveraging pre-trained models. Vertex AI integrates seamlessly with other Google Cloud services, offering tools for scalable and efficient AI solutions.

• Overview: Vertex AI offers several popular models, some of which are already running and ready for access without any special deployment needed. It also offers a playground to test prompts and fine-tuning support. With their Gemini and PaLM models, this hosting platform provides content filters (violence/hate/etc.) and grounding (connecting model output to verifiable sources of information to prevent hallucinations).
• Models: 80-90 of the most popular models.
• Deployment: Some models are already running (managed APIs), and some must be deployed to a specific machine size.
• Pricing: Token-based pricing for managed API models, and per-hour pricing for models you deploy.
• RAG: They provide a reference architecture for you to build it using their document AI technology.
• Data Privacy:  Customer data is not available to other customers and not used to train/improve any products or services. Offers a BAA for HIPAA compliance.
• Other: It supports direct Google Colab Enterprise integration, a playground for testing prompts, full call auditing, content filters for Gemini and PaLM models, and Apache Airflow via several operators.

Hugging Face Enterprise

Hugging Face Enterprise is a cloud-based platform offering advanced tools for deploying and managing state-of-the-art machine learning models. It provides access to a wide range of pre-trained models, including those for natural language processing and computer vision, with tons of support for customization and fine-tuning.

• Overview: Hugging Face Enterprise offers many open-source and commercial models and fine-tunings, some of which have a playground to test with.
• Models:  It has over 800,000 base and fine-tuned models.
• Deployment: The models must be deployed, but you can choose Hugging Face’s AWS, Azure, GCP instance.
• Pricing: Pricing is per hour.
• RAG: Not built in. Instead, RAG models can be deployed, and Python code must be written/run on your environment to invoke them and wire them together with other models.
• Data Privacy: Customer data is not available to other customers and is not used to train/improve any products or services. It offers a BAA for HIPAA compliance, but it is very expensive.
• Other: Supports direct Google Colab Enterprise integration. It supports a playground for testing prompts, full auditing of calls and content filters for Gemini and PaLM models.

BentoML

BentoML is an open-source platform designed to deploy, manage, and scale machine learning models across environments such as on-premises, Data Centers, Edge, and embedded offerings.

Overview: BentoML is a development library for building AI applications with Python. It contains everything you need to boot up an open-source model of your choice and make it accessible as an API endpoint for your application. Typically, you would download an open-source model from Hugging Face, package it with BentoML, export it as a Docker image, and run it anywhere you like.

Hugging Face TGI

Hugging Face TGI (Text Generation Inference) is designed to deploy and manage Hugging Face models across various environments such as on-premises, Data Centers, Edge, and embedded offerings.

Overview: Hugging Face TGI is a development toolkit for deploying and serving LLMs. It has built-in support for buffering multiple API requests and support quantization, token streaming, and telemetry (using Open Telemetry and Prometheus). Specialized versions are available for different GPU lines (Nvidia, AMD, AWS Inferentia). Delivered as a Docker image, you typically boot it up with parameters anywhere you like.

NVIDIA Triton Inference Server

NVIDIA Triton Inference Server is a powerful platform for deploying and managing large-scale AI models. It supports multiple frameworks and provides a unified interface for model serving.

Overview: Triton Inference Server is an open-source software toolkit developed by Nvidia to serve one or more models of many types concurrently on Nvidia GPUs. It supports model ensembles (allowing multiple models to be chained together), a C and Java API (to link directly with application code), metrics (via Prometheus), and both HTTP and gRPC APIs. It is available as a Docker image.

When selecting the best hosting solution for your AI model, several factors must be considered, such as scalability, cost, deployment options, and platform-specific features.  

Azure AI Studio, Azure OpenAI Service, AWS Bedrock, and GCP Vertex AI are strong contenders for cloud-based flexibility and ease of use. If you don’t require a BAA, Hugging Face Enterprise is an incredibly cost-effective option. Hugging Face TGI, BentoML, and Nvidia Triton offer robust deployment options for those preferring on-premises solutions.   

Regardless of the hosting platform you want to deploy your AI solution, our Rōnin Consulting team can help. Contact us today to learn how we can set up your team with the right on-premises or cloud platform.