AWS Fargate vs. Lambda in 2 Minutes: Serverless Compute Models
AWS Fargate and AWS Lambda both eliminate the operational burden of managing servers, but they differ fundamentally in packaging format, execution lifecycle, resource sizing, and cost structure.
AWS Lambda executes function-level code on-demand in response to events with zero idle cost, whereas AWS Fargate runs persistent container images on a serverless compute engine designed for continuous workloads and steady state services.
Core Components
- Execution Unit: AWS Lambda Function (packaged as a ZIP or container image) vs. AWS Fargate Task (packaged strictly as an OCI-compliant container image).
- Scaling Model: Lambda scales concurrently per incoming request/event down to zero vs. Fargate auto-scaling policies scale task counts up and down based on CPU, memory, or custom metrics.
- Resource Coupling: Lambda ties CPU power proportionally to the memory you allocate vs. Fargate allows independent, granular configuration of vCPU and memory.
- Duration Limits: Lambda enforces a maximum execution time limit per invocation vs. Fargate tasks run indefinitely without execution time caps.
- Network Interface: Lambda utilizes managed elastic network interfaces (ENIs) via Hyperplane vs. Fargate assigns a dedicated Elastic Network Interface (ENI) with its own private IP address directly to every single task.
Selection Logic
- Choose Lambda for event-driven architectures, webhooks, spiky or unpredictable traffic, and short-lived tasks where you want to pay strictly per millisecond of execution.
- Choose Fargate for containerized microservices, long-running background workers, persistent connections (WebSockets/gRPC), or high-volume APIs where fixed infrastructure provisioning out-economizes per-request pricing
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