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How are Lambda and Fargate different?
They are both serverless compute options in the sense that AWS manages the underlying servers, but they expose different execution models. Lambda runs a function in response to an event. Fargate runs containerized tasks or pods, typically under an orchestrator such as Amazon ECS or Amazon EKS. AWS describes Fargate as “Serverless compute for containers. Run containers without managing servers or clusters.” AWS’s product comparison summarizes the services; its decision guide provides a more detailed comparison.
| Decision point | Lambda | Fargate |
|---|---|---|
| Execution unit | A function invocation triggered by an event | A container task or pod |
| Best-fitting work | Short, event-driven processing | Long-running containerized applications and processes |
| Packaging | AWS-provided or custom runtimes; container images are also supported | An application packaged as a compatible container |
| Scaling unit | Execution environments responding to concurrent invocations, subject to quotas | Task or pod count managed through orchestration and scaling policies |
| Primary compute meter | Requests and function duration, with allocated memory affecting compute charges | Configured vCPU, memory and other applicable resources over task or pod runtime |
When should you choose Lambda?
Lambda is a natural fit when the work starts with an event and can be completed as a bounded function invocation. Examples include processing an uploaded file, responding to a queue message, or running a short API operation. AWS provides integrations for common event sources, reducing the amount of event plumbing an application needs to supply itself.
A standard Lambda function invocation can run for up to 15 minutes. AWS states: “Lambda functions have a maximum execution time of 15 minutes per invocation.” That limit applies to one invocation, not an entire workflow. AWS also supports durable functions for stateful workflows that can persist for up to one year; a durable workflow is not a single function continuously executing for that period. Check the AWS decision guide for the distinction.
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Lambda is a strong fit when
- Work arrives as discrete events rather than needing a process to stay alive.
- Each unit of work fits within the invocation limit, or can be designed as a durable, stateful workflow.
- Native AWS event integrations simplify the architecture.
- Request-and-duration billing matches the workload’s frequency and runtime.
When should you choose Fargate?
Fargate fits applications already packaged as containers, or workloads that need container-level environment control and a process that runs longer than a Lambda invocation. It is designed for long-running containerized applications and processes. You still define and operate the container task or pod and its orchestration, but AWS manages the underlying Fargate infrastructure.
Fargate is not event-native in the same way as Lambda. For example, connecting sources such as SQS or Kinesis to work running on Fargate involves additional integration and orchestration logic. Choose it when that flexibility is worthwhile for a persistent service, long-running job, or existing container deployment.
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Fargate is a strong fit when
- The workload needs to remain active, handle persistent connections, or run beyond the maximum duration of one Lambda invocation.
- The application already runs in a container or depends on a packaged environment better suited to a container.
- You want to scale the number of tasks or pods through ECS or EKS arrangements and policies.
- Your team benefits from container-based deployment and operational patterns.
Which one is cheaper?
There is no universal cheaper option. Lambda function pricing is based on request count and execution duration, with memory allocation affecting compute usage. Fargate pricing is tied to resources such as vCPU, memory, operating system, architecture and storage during task or pod runtime. The result depends on region, CPU architecture, sizing, invocation volume and duration, idle time, storage, networking, logs, discounts and related AWS services.
Build a comparison from the same workload assumptions rather than comparing a Lambda rate with a Fargate task rate in isolation. Use the current regional prices and calculators on the AWS Lambda pricing page and AWS Fargate pricing page.
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Cost details that can change the decision
- The Lambda pricing page lists a free-tier allowance of one million requests and 400,000 GB-seconds per month. Eligibility and current terms matter; an allowance is not a complete production cost estimate.
- AWS says Fargate Spot can cost up to 70% less than regular Fargate pricing for interrupt-tolerant ECS tasks. “Up to” is a maximum, not a guaranteed discount for a particular workload.
- AWS says Savings Plans can offer up to 50% savings on Fargate usage in exchange for a one- or three-year compute commitment. Actual savings depend on configuration and terms.
How do runtime flexibility and scaling compare?
Fargate can run applications that can be packaged into a compatible container, making it a useful choice when the application needs a particular containerized environment. Lambda offers AWS-provided runtimes, custom runtimes and function container images, but the work still runs according to Lambda’s function-oriented model. If a specific language version is decisive, check AWS’s current runtime documentation before committing.
The scaling unit also differs. Lambda scales execution environments in response to concurrent invocations, within account and service quotas. Fargate scales task or pod counts through the chosen ECS or EKS setup and its policies. Plan for expected bursts, startup behavior and steady demand, and check the current regional quotas for your account; quota figures can vary or change.
Can you use Lambda and Fargate together?
Yes. A hybrid architecture can use Lambda for event handling and short processing, then hand work to a Fargate task when it needs a longer-running containerized process. This can preserve Lambda’s event-driven strengths without forcing a long job into a function invocation. It also adds integration and orchestration work, so use the split where the workload genuinely benefits from having two execution models.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.A practical decision checklist
- Describe the unit of work. If it begins with an event and finishes as a bounded operation, evaluate Lambda first. If it is a persistent service or long-running process, evaluate Fargate.
- Check runtime and packaging needs. Confirm whether Lambda’s function model and available runtime approaches fit, or whether the application needs a compatible container environment.
- Map scaling behavior. Estimate bursts and concurrency for Lambda, or task and pod counts and scaling policies for Fargate. Verify current quotas for the relevant region and account.
- Model the full cost. Include the same workload volume, runtime, resource sizing, idle periods, storage, networking, logs, regional rates and applicable discounts for both options.
- Consider a hybrid only when responsibilities differ. Keep short event-driven work in Lambda and move the long-running container work to Fargate when that boundary improves the design.
AWS maintains its broader decision guide directory alongside the individual service comparison.
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