AI adoption is accelerating, and enterprises want the best models—without being tied to a single provider or dealing with integration headaches. AWS Bedrock and LiteLLM make this possible, offering a streamlined path to powerful, flexible, and secure AI. This combination also unlocks advanced capabilities such as streaming, async calls, and function calling—now essential for modern, production-grade AI applications.
Picture AWS Bedrock as a premium airport lounge: one entry grants you access to a world-class lineup of 'airlines'—foundation models from Anthropic, Meta, Cohere, AI21 Labs, and Amazon (Titan, Nova). Instead of juggling separate APIs, you use Bedrock’s unified API to experiment and deploy across providers, all backed by AWS’s robust infrastructure. Bedrock now also supports features like private fine-tuning and retrieval-augmented generation (RAG), making it a strategic choice for enterprise AI.
However, Bedrock’s API still brings its own challenges. Each model may require different parameters, authentication, or even input formatting. LiteLLM steps in as your universal remote: it translates your code to the right format for any Bedrock model. You write your business logic once—LiteLLM handles the provider-specific details, including parameter mapping, authentication, and response normalization.
Why does this matter for enterprises? Bedrock offers advanced security (like private VPC endpoints and granular IAM controls), compliance, and scalability. LiteLLM adds agility: switch models, try new providers, or migrate workloads with a simple code change. No need to rewrite your application for each vendor. LiteLLM also supports advanced features such as streaming responses, asynchronous calls, and function calling, letting you build real-time and interactive AI applications while keeping your codebase clean and provider-agnostic.
Let’s see how this works in practice. Suppose you’re building a document summarization tool for a regulated industry. You want to compare Anthropic Claude (great for nuanced reasoning) and Meta Llama (open-weight flexibility)—all while keeping data inside AWS for compliance. With LiteLLM and Bedrock, this is as simple as changing a model string. You can also take advantage of streaming responses for faster user feedback, or use function calling to extract structured data.
import litellm
# Summarize a contract using Anthropic Claude via Bedrock
response = litellm.completion(
model="bedrock/anthropic.claude-instant-v1", # Change this string to switch models
messages=[
{"role": "user", "content": "Summarize the attached contract in plain English."}
]
)
# Print the model's summary
print(response['choices'][0]['message']['content'])
# For streaming responses (see Chapter 4 for details):
# for chunk in litellm.completion(
# model="bedrock/anthropic.claude-instant-v1",
# messages=[{"role": "user", "content": "Summarize the attached contract in plain English."}],
# stream=True
# ):
# print(chunk['choices'][0]['delta']['content'], end="")
Notice the 'messages' parameter: it’s a list of conversation turns, with roles like 'user' or 'assistant'. This structure is consistent across LiteLLM, making it easy to swap models or providers. For real-time applications, both Bedrock and LiteLLM support streaming responses and asynchronous calls (see Chapter 4 for details).
Want to try Meta Llama or Cohere? Just update the model string. No changes to authentication, data routing, or output parsing. This flexibility lets you prototype, compare results, and avoid vendor lock-in—all with minimal effort. In addition to basic completions, LiteLLM and Bedrock support advanced features like function calling and structured outputs—enabling more complex workflows without vendor lock-in.
Key benefits of combining Bedrock and LiteLLM:
Of course, each foundation model may have unique features or require specific parameters—a process called 'parameter mapping.' LiteLLM simplifies this, but understanding what’s happening under the hood will help you get the best results. AWS Bedrock also now supports private model customization (fine-tuning) and retrieval-augmented generation (RAG) workflows, both of which can be orchestrated via LiteLLM for advanced use cases. Tracking model versions and updates is also important, as Bedrock and providers regularly release new variants.
Enterprise deployments benefit from observability features such as logging, tracing, and monitoring—see Chapter 13 for how to instrument your LiteLLM-powered applications. Next, we’ll walk step-by-step through connecting LiteLLM to Bedrock, selecting models, and mapping parameters. For a deeper dive into LiteLLM’s unified API, streaming, async, and model routing, see Chapters 3 and 4. Ready to unlock scalable, enterprise-ready AI? Let’s get started.