AWS Lambda vs Fargate for AI Workloads

Lambda and Fargate are both serverless compute options on AWS, but they differ significantly in how they handle AI workloads. Lambda offers event-driven, short-lived functions. Fargate runs containers without managing servers. For AI workloads, the differences in cold start behavior, resource limits, runtime duration, and GPU support drive the choice.

Quick Comparison

AI Inference Workloads

Lambda for Inference

Works well for: Lightweight inference with small models. Calling external AI APIs (Bedrock, OpenAI). Preprocessing and postprocessing around AI calls. Low-volume, sporadic inference requests.

Example: A Lambda function receives an API request, calls Bedrock for LLM inference, and returns the result. The model runs on Bedrock’s infrastructure; Lambda handles the orchestration.

Limitations: No GPU means CPU-only local inference. 10 GB memory limits model size. 15-minute timeout limits long-running inference. Cold starts add 1-10 seconds of latency depending on package size.

Cold start mitigation: Use Provisioned Concurrency to keep Lambda functions warm. This eliminates cold starts but adds ongoing cost (you pay for the provisioned capacity whether it is used or not).

Fargate for Inference

Works well for: Running model serving containers (TGI, vLLM, Triton). Longer-running inference pipelines. Workloads that need more memory than Lambda provides. Applications that need persistent connections (WebSocket, gRPC).

Example: A Fargate task runs a FastAPI container with a scikit-learn or small transformer model loaded in memory. An Application Load Balancer routes inference requests to the container.

Limitations: No GPU support on Fargate. For GPU inference, use ECS with EC2 GPU instances. Scaling is slower than Lambda (new container spin-up takes 30-60 seconds).

AI Processing Workloads

Lambda for Processing

Document processing. Lambda triggered by S3 uploads to process documents: extract text, classify, extract entities. Each document is processed independently, and Lambda’s parallel execution handles bursts efficiently.

Event-driven AI pipelines. Lambda functions orchestrated by Step Functions for multi-step AI processing: ingest, preprocess, call AI service, postprocess, store results.

Preprocessing for training. Lambda functions transform raw data (resize images, clean text, validate records) before feeding into training pipelines.

Fargate for Processing

Batch AI processing. Long-running batch jobs that process large datasets. No time limit, more memory, persistent storage.

Model training preprocessing. Complex data preparation that exceeds Lambda’s 15-minute limit or 10 GB memory.

Streaming AI pipelines. Continuous processing of streaming data (Kinesis, Kafka) where maintaining state across messages is important.

Cost Comparison

Lambda pricing: $0.20 per million requests + $0.0000166667 per GB-second. You pay only when code runs. Idle time costs nothing.

Fargate pricing: Per-second billing based on vCPU and memory. 1 vCPU + 2 GB memory costs approximately $0.04/hour ($29/month continuous).

Break-even analysis:

For sporadic workloads (less than 20% utilization), Lambda is cheaper because you pay nothing when idle.

For steady workloads (more than 40% utilization), Fargate is cheaper because the per-invocation overhead of Lambda accumulates.

For AI inference specifically: if you are calling external AI APIs (Bedrock), Lambda’s cost is usually a small fraction of the AI API cost itself. The compute cost matters more when running models locally.

Scaling Behavior

Lambda scales instantly by running more concurrent executions. Can handle sudden spikes (0 to 1000 concurrent executions in seconds). Each execution is isolated. Default limit: 1000 concurrent executions per account (adjustable).

Fargate scales by launching new tasks. Each new task takes 30-60 seconds to start (container image pull, startup). For predictable scaling, use target tracking auto-scaling. For bursty workloads, maintain a minimum task count to absorb initial spikes.

For AI workloads with unpredictable burst patterns, Lambda’s instant scaling is a significant advantage. For steady-state workloads, Fargate’s scaling is adequate.

Architecture Patterns

Pattern 1: Lambda + Bedrock

Lightest weight. Lambda handles API requests and calls Bedrock for AI inference. No local model loading. Fastest to implement.

Pattern 2: Fargate + Local Model

Medium weight. Fargate runs a container with a model loaded in memory. Good for small to medium models that fit in Fargate’s memory (up to 120 GB). No GPU means CPU inference only.

Pattern 3: ECS on EC2 + GPU

Heavyweight. ECS tasks on GPU EC2 instances for large model inference. Full GPU access. Most complex to manage but necessary for large models.

Pattern 4: Lambda + Fargate Hybrid

Lambda handles lightweight requests and preprocessing. Fargate handles heavy processing and model serving. Step Functions orchestates between them.

Recommendation

Use Lambda for: AI API orchestration (calling Bedrock, OpenAI), event-driven document processing, lightweight preprocessing, and sporadic workloads.

Use Fargate for: running model serving containers, batch processing, long-running AI pipelines, and workloads needing more than 10 GB memory.

Use ECS on EC2 for: GPU-dependent inference (large transformers, vision models) where Fargate and Lambda cannot provide the required compute.

For most LLM-based applications that use managed AI services (Bedrock), Lambda is sufficient and simpler. For applications that run models locally, Fargate (or EC2 for GPU) is necessary.