Choosing between AWS Fargate and AWS Lambda can feel like one of those “pick your own adventure” stories, except the path depends on the workload you have lined up, not on your taste in biscuits. Both services slice out the server management fuzz, leaving you with more time to sneak in coffee between deploying features. Yet the two are like chalk and cheese under the same AWS roof.
In this article, I’ll peel back the lids on Fargate and Lambda and compare core features, performance, and wallet impact. You’ll see how Fargate lifts your containers into the serverless sky and what Lambda does with its single-purpose event triggers. I’ll highlight edge cases, like how each service scales and charges when the job running in your code is done, tipping the scales of serverless architecture in your favour. Jimm, the “cloud strategist” on your dev team, is in the backroom wondering the same. I’ll spill what grounding insights I’ve gathered so the entire team walks to that whiteboard armed and confident.
What is AWS Fargate?
AWS Fargate is a serverless compute service designed specifically for containers, operating seamlessly with both Amazon Elastic Container Service and Amazon Elastic Kubernetes Service. When you choose Fargate, you run containerized workloads without needing to provision or manage any EC2 instances or clusters underneath.
Key Features:
Container Orchestration: Fargate abstracts server management by automatically handling tasks such as scaling, patching, and infrastructure provisioning, allowing you to concentrate solely on building and running your container workloads.
Workload Isolation: Every Fargate task gets its own lightweight, dedicated compute instance, which guarantees full isolation of CPU, memory, and networking resources among tasks.
Long-Running Tasks: Built for lengthy, stateful workloads, Fargate exceeds Amazon Lambda’s duration constraints by enabling persistent memory, open connections, and substantial compute resources for extended periods.
Typical Use Cases: Deploy microservice architectures, conduct batch data processing, execute containerized CI/CD workflows, and run web applications needing predictable, extended performance. If your development and operations teams already leverage Docker, AWS Fargate serves as a straightforward on-ramp to serverless container management, allowing you to elevate resource provisioning, scaling, and security with minimal cognitive load.
What is AWS Lambda?
AWS Lambda lets you run code without managing servers. You simply upload your Lambda function, and scaling, availability, and server management are AWS’s concern. You pay only for the milliseconds your code runs and the requests it serves, eliminating idle resource costs.
Key Features:
Event-Driven: Your code runs only when needed, triggered by changes in data, system state messages, or calls. More than 200 AWS services can send triggers, from new S3 uploads to API Gateway HTTP requests.
Automatic Scaling: Requests that arrive are processed in parallel. Lambda starts as many instances as needed, scaling from zero to thousands of concurrent executions in less than a second.
Short-Lived Processes: Designed for transient tasks, each execution can run for up to 15 minutes.
Typical Use Cases: Use Lambda to process files as they upload, to build serverless APIs, to transform data at the edge, or to automate AWS resource management. Its responsiveness and stateless model make it ideal for sporadic, event-driven workloads that are easily distributed.
Fargate vs. Lambda: A Quick Comparison
Feature | AWS Fargate | AWS Lambda |
Execution Unit | Container (Task/Pod) | Function |
Execution Model | Continuous, Long-Running | Event-Driven, Stateless |
Max Runtime | No Limit | 15 minutes |
Max Memory | 120 GiB | 10 GB |
Max CPU | 16 vCPU | 6 vCPU |
Cold Start | Minimal (container pull time) | Yes (can be mitigated) |
Primary Use Case | Long-running containerized apps | Short-lived, on-demand tasks |
Orchestration | Amazon ECS or EKS | AWS Managed |
Technical Deep Dive: Fargate vs. Lambda
Now let’s break down the technical distinctions that will drive your architecture decision.
Architecture and Performance
At the architectural level, the two platforms reflect diverging operational models. Lambda embodies Function-as-a-Service, invoking code reactively in short bursts of execution. In contrast, Fargate embodies Container-as-a-Service, commanding pre-packaged applications to run unbroken over continuous time.
Lambda Cold Starts: Cold starts are the latency window framed by an idle Lambda function briefly warming up. Transfer of request to provisioned capacity, plus environment preparation, generates a secondary delivery delay typically between 100 and 1,000 milliseconds, though dependency-heavy runtimes may sometimes stall longer. Thankfully, over 99% of invocations escape the latency smoke, although workloads demanding steadily low latency may find remedial measures, such as provisioned concurrency, a legitimate cost.
Fargate Startup Time: Fargate steadiness, in exchange, exacts a delay incumbent to container start. Every Fargate task takes upwards of 30 to 120 seconds as the container image pulls from the registry and the operational manifest spins the pod. The grace period is longer than a Lambda function repurposed from warm memory, but unlike Lambda, the operational environment either consistently handles database peaks, network chatter, point-in-time restores, or customer storefronts, unburdened by stall hybrids.
Pricing and Cost Optimization
Understanding the pricing models is crucial when asking, "Which serverless option is cheaper?"
Fargate Pricing: You pay for the amount of vCPU and memory resources your containerized applications request per second. There's a one-minute minimum charge.
Linux/X86 (us-east-1):
$0.04048 per vCPU per hour
$0.004445 per GB RAM per hour
Linux/ARM: (cheaper due to newer CPU)
$0.03238 per vCPU per hour
$0.00356 per GB RAM per hour
Windows: Higher, plus OS license costs.
Ephemeral storage: First 20GB per task/pod is free; $0.000111 per GB per hour after that.
No free tier, but Spot pricing discounts are available for flexible use cases.
Lambda Pricing: You are charged based on the number of requests for your functions and the duration your code executes, measured in gigabyte-seconds.
Requests: First 1 million requests per month are free, then $0.20 per 1 million requests.
Duration: $0.0000166667 per GB-second.
Free tier: 400,000 GB-s monthly.
Memory allocation: More RAM = higher cost, but also potentially faster execution.
CPU architecture: Graviton2/ARM functions are ~34% cheaper than x86_64 in most scenarios.
Additional features: Savings Plans for up to a 17% discount, specialization for provisioned concurrency.
So, which is cheaper?
For event-driven, spiky workloads with idle periods, Lambda is almost always cheaper.
For consistent, high-traffic applications running 24/7, Fargate can be more cost-effective than running Lambda functions continuously.
Scalability and Resource Management
While both services deliver outstanding scalability, each tackles the challenge in its own way.
Lambda Scaling: Lambda delivers horizontal scaling practically in the blink of an eye. It launches a fresh instance of the function for every inbound request, constrained only by the account-level concurrency cap (typically 1,000 per region, although higher ceilings can be raised on demand and can exceed tens of thousands under the right circumstances). The design shines during unexpected traffic surges, absorbing spikes with no significant lag.
Fargate Scaling: Fargate scales automatically, but relies on shifting usage metrics, CPU, and memory utilization, rather than unbounded concurrency. When resource thresholds are crossed, fresh containers are initialized, a process that can take anywhere from 30 seconds to a few minutes. Because of the scale-up and scale-down lag, Fargate suits workloads with predictable, gradual traffic patterns. The service also permits detailed resource sliders, letting architects assign between 0.25 and 4 vCPU and between 0.5 and 30 GB of memory per container, thereby executing cost performance decisions right down to the task level.
When to Use Fargate vs. Lambda
Choose between AWS Fargate and AWS Lambda based on what your specific workload demands.
Choose AWS Lambda When:
You have event-driven workloads: Lambda naturally reacts to AWS service triggers, whether that’s resizing images, uploading to S3, or dequeuing messages from SQS.
Your tasks are short-lived: The Lambda ceiling of 15 minutes works beautifully for ephemeral tasks that spin up, do their work, and spin down.
You need rapid, automatic scaling: Lambda scales to match sudden, unpredictable spikes, no ahead-of-time provisioning.
You're building simple APIs: When connecting API Gateway to a simple backend, Lambda is the easiest, most cost-effective dance partner.
Choose AWS Fargate When:
You are running containerized applications: If Docker images are your bread and butter, Fargate runs them serverless with no conversion.
Your tasks are long-running: When tasks take longer than the Lambda limit, think batch jobs, ETL jobs, or lengthy data-processing Fargate handles.
You need consistent performance: For services that must avoid the cold-start penalty, Fargate maintains a steady and warm instance for predictable latency.
You require more control: Fargate gives you extra leverage to shape the networking setup, CPU, and memory allocations; those knobs matter when you’re orchestrating intricate apps.
Save on Your Serverless Bill with Pump
If you run any serverless workloads in the cloud, controlling cloud spend is mission-critical. Facilities like AWS Fargate and AWS Lambda do provide Savings Plans, with the potential to shave 45% off the bill, but juggling these commitments can get tricky. Pump simplifies that complexity. As a dedicated cloud optimization platform, we can help you to drive your overall cloud spend down by 10-60% by fully automating your cost-control playbook.
Here's how Pump saves you money on AWS:
Group Buying: We partner with hundreds of operators to pool spend and unlock the same tier of rate reductions that the largest enterprises access.
AI-Powered Optimization: Our AI engine autonomously acquires and trades Reserved Instances and Savings Plans, locking in the largest discounts without you needing to build forecasts.
Waterfall Coverage: Any unused, purchased commitments get redistributed across our user base, so no discount ever goes underused. We even indemnify you against any wastage.
The best part? You can use it completely free and no hidden charges.
Conclusion
You don’t need to sit in a conference room for days to choose between AWS Fargate and AWS Lambda. With a clear grasp of their fundamental advantages, the right choice becomes obvious: Lambda shines for fast, serverless event processing, while Fargate delivers configurability and longevity for container workloads.
Want to cut your AWS spending? Try Pump for free today at no charge and see just how much you can cut from your serverless bills.
