When using Large Language Models (LLMs), there are two main pricing models:
- Hosting on Your Own Infrastructure: You pay for the computing resources and may need a license for the LLM.
- Token-Based Pricing: You pay for the number of tokens processed. A token could represent a character, word, or pixel.
AWS Global Infrastructure
AWS offers a globally resilient infrastructure with:
- Regions
- Availability Zones
- Edge Locations
This ensures high availability and fault tolerance for your applications.
AWS Services for LLMs
SageMaker JumpStart
- Quick deployment of pre-built models.
- Fine-tuning and scaling for production.
- Resources like blogs, videos, and example notebooks.
- Keep track of GPU costs when using SageMaker.
Amazon Bedrock
- Managed service with access to a variety of foundation models (FMs) via APIs.
- Supports AWS models and third-party models, like Cohere and Stability AI.
- Pay-as-you-go with no long-term commitment.
- Import custom weights for model architectures.
Amazon Titan
- Amazon’s foundation model, ideal for text generation.
Playgrounds and PartyRock
- Playgrounds in Amazon Bedrock let you test models and adjust inference parameters.
- PartyRock is a platform to create generative AI apps like trivia games or playlists.
Benefits of Using AWS for Generative AI:
- Cost-Effective: No need for heavy investments in infrastructure or training.
- Scalable: Build and scale generative AI applications securely.
- Flexible: Choose from various foundation models for your use case.
- Security and Privacy: AWS ensures enterprise-grade security for your data and models.
AWS helps you focus on creating customer and employee experiences while using LLMs and FMs without the high costs of hosting and training models.
