Foundational Model

This section covers the key considerations and trade-offs involved in selecting a base foundation model within Amazon Bedrock. The choice of model depends on several factors including:

performance,
token capacity,
language support,
modality,
cost,
customization options, and
inference speed.

There is no single best option, as each model brings unique strengths and constraints. Therefore, experimentation and alignment with business needs are crucial.

Factors to Consider When Selecting a Model

Several key parameters influence the choice of a foundation model:

The required level of performance and capability
The maximum token context window, which determines how much input data the model can process
Whether the model supports multimodal input and output, such as text, image, audio, or video
The cost per 1,000 tokens or per request
The ability to perform fine-tuning with your own data
The licensing agreements, which may vary across models
The expected latency during inference

Some models are optimized for cost-effectiveness while others are designed to deliver high-accuracy outputs. Multimodal capabilities, in particular, are important for applications involving diverse media formats.

Amazon Titan and Its Role in the AWS Ecosystem (V Imp for Exam)

Since this course focuses on AWS, special attention is given to Amazon Titan, which is Amazon’s High-performing foundation model suite.

Titan supports text and image generation, as well as multimodal capabilities.

The model can be fine-tuned with custom datasets using a unified API within Amazon Bedrock.

Smaller versions of Titan may be more cost-effective but will likely have reduced knowledge coverage compared to larger, more capable models. Deciding which version to use is a balance between cost and quality.

Comparing Four Popular Foundation Models

The following comparison covers four commonly available models on Amazon Bedrock:

Model	Max Tokens	Features	Use Cases	Pricing (per 1K tokens)
Amazon Titan (Text Express)	8K	High-performance text model, supports 100+ languages	Content creation, classification, education	Input: $0.0008, Output: $0.0016
Llama 2 (70B-chat)	4K	Suited for large-scale tasks and English dialogue	Text generation, customer service	Input: $0.0019, Output: $0.0025
Claude 2.1	200K	High-capacity text generation, multilingual	Analysis, forecasting, document comparison	Input: $0.008, Output: $0.024
Stable Diffusion (SDXL 1.0)	77 Tokens/Prompt	Image generation only	Image creation for advertising, media...	$0.04–$0.08 per image

Observations Based on the Comparison

Claude 2.1 offers the largest context window (200K tokens), making it suitable for processing large codebases, books, or documents. This is critical in use cases that require deep memory of long inputs.
Amazon Titan is significantly cheaper than both Llama 2 and Claude, while still supporting multilingual capabilities.
Llama 2 provides strong performance for conversational and English-based tasks but has a smaller context window and slightly higher cost than Titan.
Stable Diffusion is purely for image-related generation and accepts shorter prompts. Its cost is per image rather than per token, and it supports features like object removal, background replacement, and visual modification.

Final Thoughts on Model Selection

While all these models are converging toward similar capabilities, the real decision comes down to testing each one for your specific needs:

Claude may be preferred for heavy document analysis and large prompt sizes.
Titan offers a strong balance between performance, cost, and multilingual support.
Llama 2 is ideal for scalable dialogue and customer-facing tasks.
Stable Diffusion is the go-to for image and creative generation needs.