Amazon Bedrock
Bedrock is AWS's managed LLM service, offering API access to models from Anthropic, Meta, Mistral, Cohere, and Amazon's own Titan family. It handles infrastructure, scaling, and compliance. Bedrock integrates with AWS IAM, CloudWatch, and other AWS services, making it the default choice for teams already operating in AWS.
Official site ↗Why it ranks here
Bedrock is the easiest way to call Claude or other models if your GTM stack runs on AWS. It bundles compliance, data residency, and enterprise SLAs without requiring separate contracts with each model provider. For teams with strict security requirements, Bedrock keeps data inside AWS.
Where it sits
Bedrock's model selection is smaller than OpenRouter's, and pricing is slightly higher than calling Anthropic or OpenAI directly. It is a convenience play, not a cost or performance optimization. Use it when AWS integration and compliance justify the overhead.
How GTM Engineering teams use it
GTM engineers use Bedrock to call Claude, Llama, or Mistral from Lambda functions, Step Functions, or ECS tasks. Teams batch-process lead scoring, enrich data in S3, or trigger LLM calls from EventBridge rules. Bedrock's IAM integration means you manage model access via AWS roles instead of API keys, reducing credential sprawl.
In-depth notes
Pricing is per-token with a small markup over the base model provider. Latency is comparable to calling the provider directly. Bedrock supports prompt caching (for Claude) and fine-tuning (for select models). The service is available in most AWS regions but not all; check availability before committing. Bedrock's logging and monitoring via CloudWatch is better than most provider-native tools.
Best for
AWS-native teams, compliance-heavy environments, teams using multiple models without managing separate contracts.
Avoid if
You are not on AWS or you want the absolute lowest cost per token.
The rest of LLM APIs & intelligence
Claude (Anthropic) Leader Strong-reasoning LLM for research, qualification, drafting, and classification. #2 
OpenAI Leader Broad ecosystem of general-purpose LLMs available via API for GTM pipelines. #3 
Claygent Strong Clay's built-in AI research agent for web scraping, enrichment, and data extraction. #4 
Perplexity API Strong LLM with built-in search grounding for real-time research and fact-checking. #5 
OpenRouter Strong Model gateway providing unified API access to dozens of LLMs with automatic routing. #8 
Groq Situational Ultra-fast inference API for Llama, Mistral, and Gemma with sub-second latency. #9 
Together AI Situational Hosted inference for open-source models with fine-tuning and custom deployment. #10 
Cohere Niche Enterprise LLM with strong embeddings, reranking, and classification APIs. #13 
Replicate Niche Model marketplace and hosting platform for open-source LLMs and AI models. Learning guide
Setup
Enable Bedrock in your AWS region, grant IAM permissions (bedrock:InvokeModel) to your Lambda role or ECS task, install boto3, and call invoke_model with a model ID (e.g., anthropic.claude-3-sonnet). Your first pipeline call scores leads from a Lambda function triggered by S3 uploads.
First thing to build
Build a lead scoring Lambda that reads a CSV of inbound leads from S3, calls Claude via Bedrock to score each row, and writes scored results back to S3. Use Bedrock IAM integration to avoid managing API keys in environment variables.
What actually matters
- Bedrock model IDs differ from provider APIs (e.g., us.anthropic.claude-3-sonnet-20240229-v1:0 vs anthropic claude-3-sonnet); check the Bedrock docs for exact IDs
- Use prompt caching (Anthropic models) to reduce cost on repeated context like ICP definitions or system prompts
- Set up CloudWatch logging to monitor invocations, latency, and token usage for cost forecasting
- Check model availability in your AWS region before deploying; not all models are available everywhere
Watch out
Bedrock pricing includes a small markup over direct provider APIs; validate the cost difference justifies the AWS integration benefits.