Lambda promises zero-ops scaling. LLMs demand sustained compute. Here is where these two realities meet and when each deployment model wins.
AWS Lambda is the default choice for serverless backends. It scales automatically, you pay per invocation, and there are no servers to manage. But LLM workloads push against Lambda's constraints in ways that traditional API backends don't. Cold starts, 15-minute timeouts, 10 GB memory limits, and no GPU access create real challenges. This guide covers what works, what doesn't, and the architectural patterns that make Lambda viable for AI inference in specific scenarios.
If your Lambda function calls OpenAI, Anthropic, or AWS Bedrock APIs (not running the model itself), Lambda works well. The function constructs the prompt, calls the API, processes the response, and returns. This is an I/O-bound workload, exactly what Lambda is designed for.
If your AI workload is highly variable (e.g., processing batch uploads that arrive unpredictably), Lambda's auto-scaling from zero is cost-effective. You don't pay for idle instances during quiet periods.
Lambda triggered by S3 uploads, SQS messages, or DynamoDB streams works well for async AI tasks: document classification when files are uploaded, embedding generation on new content, or summarization triggered by database events.
Lambda functions with large dependencies (LangChain, FastAPI, OpenAI SDK) take 3-8 seconds to cold start. For chat applications where users expect sub-second response times, this is unacceptable. Provisioned concurrency mitigates this but adds cost and reduces Lambda's cost advantage.
LLM generation for long documents or complex agent workflows can exceed 15 minutes. Lambda hard-kills the function at this limit. For production agents that run multi-step tool calling loops, this timeout is a critical constraint.
Lambda does not support GPU instances. Running open-source models (Llama 3, Mistral) locally on Lambda is impractical. You are limited to CPU-only inference, which is 10-100x slower than GPU inference for model execution.
Lambda behind API Gateway does not natively support Server-Sent Events (SSE) or WebSocket streaming. For LLM chat interfaces that stream tokens, you need Lambda Function URLs or a separate WebSocket API, adding complexity.
| Use Case | Lambda | Containers (ECS/Fargate) | Dedicated (EC2/SageMaker) |
|---|---|---|---|
| Call external LLM API | Best fit | Good | Overkill |
| Real-time chat (streaming) | Poor | Best fit | Good |
| Self-hosted model inference | Not viable | Limited | Best fit |
| Batch document processing | Best fit | Good | Good |
| Agent with long workflows | Risky (timeout) | Best fit | Good |
| Spiky, unpredictable traffic | Best fit | Good | Wasteful |
Most production AI systems combine both. Use Lambda for lightweight, event-driven tasks (embedding generation, classification, async processing). Use containers (FastAPI on ECS/Fargate) for real-time chat, streaming, and long-running agent workflows.
For infrastructure automation, manage both deployment types with Terraform and AWS Bedrock.
Not practically. Even quantized versions of Llama 3-8B require more memory than Lambda's 10 GB limit, and CPU-only inference is too slow for production use. Use SageMaker endpoints or EC2 instances with GPUs instead.
Bedrock provides managed model endpoints within your AWS account, which is simpler for AWS-native architectures and supports models from Anthropic, Meta, and Amazon. Lambda calling Bedrock is cleaner than Lambda calling external APIs. The choice depends on model preference and pricing.
Use provisioned concurrency for user-facing endpoints. For async workloads, cold starts are acceptable. Minimize package size by excluding unnecessary dependencies. Use Lambda layers for large libraries.
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