Choosing the Right Foundation Model on Amazon Bedrock

This is the fifth and final post in our Amazon Bedrock series, building on getting started, Knowledge Bases, Agents, and Guardrails.

Bedrock's actual value proposition — one consistent API and IAM model across multiple providers' foundation models, covered in the first post in this series — only pays off if you're genuinely choosing between models deliberately, rather than picking one and never revisiting the decision. We flagged the same instinct for routing tasks to a smaller, cheaper model in our OpenAI series; Bedrock's multi-provider access makes that same discipline available across an even broader set of options.

The trade-offs that actually matter

Capability vs. cost vs. latency. A larger, more capable model (Claude 3.5 Sonnet, for instance) genuinely outperforms a smaller one (Claude 3 Haiku, or Amazon's Titan Lite) on complex reasoning, nuanced writing, and multi-step tasks — and costs meaningfully more per token, with correspondingly higher latency. A classification task, a simple extraction task, or the internal summarization pattern covered in our OpenAI context-management post rarely needs the larger model's additional capability at all.
Context window size, if an application genuinely needs to process long documents in a single call, rather than relying on chunked retrieval (covered throughout our RAG posts) to surface only the relevant portion.
Fine-tuning support varies by model and provider — relevant specifically if the style-and-format-consistency fine-tuning case covered in our OpenAI series applies to your use case, on a model actually available through Bedrock.

Comparing models directly, on your own actual data

import boto3
import json
 
client = boto3.client("bedrock-runtime", region_name="eu-west-2")
 
models_to_compare = [
    "anthropic.claude-3-haiku-20240307-v1:0",
    "anthropic.claude-3-5-sonnet-20241022-v2:0",
    "meta.llama3-1-70b-instruct-v1:0",
]
 
test_prompt = "Summarize the key benefits of Kubernetes autoscaling in 2 sentences."
 
for model_id in models_to_compare:
    if model_id.startswith("anthropic"):
        body = {
            "anthropic_version": "bedrock-2023-05-31",
            "max_tokens": 200,
            "messages": [{"role": "user", "content": test_prompt}],
        }
    else:
        body = {"prompt": test_prompt, "max_gen_len": 200}
 
    response = client.invoke_model(modelId=model_id, body=json.dumps(body))
    result = json.loads(response["body"].read())
    print(f"--- {model_id} ---")
    print(result.get("content", result.get("generation")))

Because Bedrock exposes multiple providers through the same client and the same billing account, direct comparison on your own real prompts — not a generic public benchmark that may not reflect your actual task at all — is genuinely straightforward to set up. This is the practical payoff of Bedrock's consolidation: evaluating three different providers' models costs nothing beyond the API calls themselves, with no separate account, contract, or billing relationship required for any of them.

A framework for the actual decision

Start with the smallest, cheapest model that could plausibly handle the task — Claude 3 Haiku or a similarly-sized model — and evaluate its output on real examples from your own use case, not a hypothetical one.
Escalate to a larger model specifically when evaluation reveals a genuine capability gap — noticeably worse reasoning, missed nuance, or an accuracy shortfall on your own real test cases — rather than defaulting to the largest available model preemptively, "just in case."
Re-evaluate periodically, not just once. Model pricing and capability both shift as providers release new versions — a choice that was correct six months ago is worth revisiting, the same "rebuild and rescan on a schedule" instinct we recommended for Docker base images earlier in this blog, applied here to model selection instead.

Provisioned throughput: a distinct decision from model choice

aws bedrock create-provisioned-model-throughput \
  --model-id "anthropic.claude-3-5-sonnet-20241022-v2:0" \
  --provisioned-model-name "novuspark-production-throughput" \
  --commitment-duration "OneMonth" \
  --model-units 2

On-demand Bedrock pricing (pay per token, no capacity reservation) is the right default for most applications, including genuinely variable production traffic. Provisioned throughput — committing to reserved capacity for a specific model, at a different pricing structure — becomes worth evaluating specifically once an application has consistently high, predictable volume, where guaranteed throughput and a different cost structure at that specific volume level actually pay off. This is a distinct decision from which model to use at all, worth evaluating separately, and only once real production usage patterns are actually established and predictable.

What to actually remember from this series

Model choice is a real, ongoing decision — not a one-time pick, made once and never revisited, no differently than routing tasks to cheaper models is an active discipline in our OpenAI series.
Bedrock's consolidated billing and IAM model make direct, low-friction comparison across providers genuinely practical — a real advantage over managing separate accounts and credentials per provider.
Start small, escalate based on evaluated capability gaps on your own real data — not a default assumption that the largest available model is always the safer choice.
Provisioned throughput is a separate decision from model selection, worth evaluating only once real, predictable production volume actually justifies it.