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AWS Fargate vs Lambda: Choosing the Right Serverless Service

21 Jan 2025 Cloud Computing No Comments
aws fargate vs lambda

Cloud computing has transformed how businesses deploy applications, and AWS leads the charge with two powerful serverless options: AWS Fargate and AWS Lambda. As organizations move away from traditional server management, choosing between these services has become crucial for developers and architects.

According to Business World, The serverless computing market is expected to reach $36.84 billion by 2028. Both services eliminate the need to manage servers, but they serve different purposes and come with distinct advantages.

This comprehensive guide breaks down AWS Fargate vs Lambda in terms of performance, cost, scalability, and use cases. Whether you’re building a new application or modernizing existing infrastructure, we’ll help you decide between these two serverless computing solutions. You’ll learn exactly when to use each service and how to maximize their benefits for your specific needs.

AWS Fargate Overview

AWS Fargate is a serverless compute engine designed specifically for containers. It allows you to run applications without managing the underlying infrastructure. Fargate handles all server management tasks automatically, including capacity provisioning, operating system maintenance, and security patching. You simply define your application’s container requirements like CPU, memory, and networking, and Fargate takes care of the rest.

With Fargate, you focus on app development while AWS handles provisioning, scaling, and maintenance. It supports both Amazon ECS and EKS, making it flexible for containerized workloads. Fargate ensures resource isolation, enhancing security and performance.

Key features include automatic scaling, task-level isolation, and integration with AWS services like IAM and CloudWatch. 

It suits tasks like microservices, batch processing, and machine learning workloads. Developers can quickly deploy applications with minimal overhead. AWS Fargate is ideal for long-running apps or applications requiring fine control over runtime environments. It’s a great choice for containerized solutions needing scalability and security.

 

AWS Lambda Overview

AWS Lambda is a serverless computing service that runs your code in response to events without requiring server management. It automatically scales your applications by running code in parallel, processing each trigger individually. Lambda handles all the underlying infrastructure, from server provisioning to maintenance, allowing developers to focus solely on writing code.

With Lambda, you upload code, and AWS takes care of scaling, monitoring, and maintenance. It supports multiple programming languages like Python, Node.js, and Java. Lambda automatically scales to handle spikes in demand and executes tasks in milliseconds.

Lambda’s standout features include automatic scaling from zero to thousands of concurrent executions, pay-per-use billing where you only pay for compute time used, and native integration with over 200 AWS services

It’s particularly effective for event-driven tasks like real-time file processing, API backends, and data transformation. Lambda functions can run for up to 15 minutes, making them perfect for short-term, stateless operations that need rapid scaling capabilities.

How Fargate and Lambda Work?

Understanding how AWS Fargate and Lambda work is crucial for choosing the right serverless solution. While both services eliminate server management, they operate differently to serve distinct use cases in cloud computing. Let’s explore their core working mechanisms.

How AWS Fargate Works?

AWS Fargate is designed for running containerized applications. It works with Amazon ECS and EKS to manage container workloads. To start,

  • Containerized Application Management: AWS Fargate runs containerized workloads, integrating with Amazon ECS or EKS for container orchestration.
  • Task or Pod Definition: Developers define tasks (ECS) or pods (EKS) specifying the container image, memory, CPU, and networking needs.
  • Serverless Provisioning: Fargate automatically provisions and manages the compute resources needed to run the containers.
  • Resource Isolation: Each task or pod runs in its isolated environment, ensuring security and dedicated performance.
  • Dynamic Scaling: Fargate adjusts the number of running containers based on real-time demand, scaling up for spikes and down to save costs during low traffic.
  • Simplified Operations: It eliminates the need to manage server clusters, patch operating systems, or handle complex scaling configurations.
  • Seamless Integration: Fargate works with other AWS services like IAM for security, CloudWatch for monitoring, and VPC for networking.
  • Cost Efficiency: Billing is based on the vCPU ( virtual central processing unit) and memory used by the containers, with no upfront costs or idle resource charges.
  • Ideal for Long-Running Workloads: Fargate is best suited for applications requiring continuous processing and fine-grained control over runtime environments.

How AWS Lambda Works?

AWS Lambda, on the other hand, works by executing code in response to specific triggers or events. You upload your code to Lambda as a function, and AWS manages everything required to run and scale that code. When an event occurs, such as an HTTP request through API Gateway or a file upload to S3 Lambda automatically executes your function.

Lambda’s working process involves three key steps:

  1. Your function is triggered by an event
  2. Lambda instantly provisions the required computing resources
  3. Your code executes in an isolated environment

The main difference in their operation lies in the execution model. Fargate runs containers continuously, making it suitable for long-running applications. Lambda executes code on-demand in response to events, making it ideal for short-lived, event-driven tasks.

Both services handle scaling differently. Fargate scales by adding or removing container tasks based on demand, while Lambda automatically scales by running multiple copies of your function in parallel. This fundamental difference in operation helps determine which service best suits your specific application needs.

Fargate vs Lambda: Detailed comparison


When comparing AWS Fargate and Lambda, organizations face a critical decision that impacts their application architecture, performance, and costs. Recent studies show that 76% of enterprises are using serverless computing, with both Fargate and Lambda being popular choices. Let’s examine their key differences across essential parameters.

Feature AWS Fargate AWS Lambda
Execution Model Runs containerized workloads (ECS/EKS integration) Executes event-driven functions
Infrastructure Serverless but requires container setup Fully managed, no setup required
Task Duration Ideal for long-running tasks Limited to 15 minutes per function
Pricing Model Based on vCPU, memory, and runtime Based on requests and execution time (ms)
Use Cases Microservices, batch jobs, ML models Real-time processing, lightweight automations
Security Task-level isolation with IAM control Function-level isolation, fully managed
Runtime Control Full control over OS, dependencies, and resources Limited control, predefined environments
Integration Works with ECS, EKS, VPC, IAM Integrates with 200+ AWS services (S3, API Gateway)
Cost Efficiency Suitable for continuous workloads Best for short, infrequent tasks

 

Resource Management and Scaling

Fargate offers more granular control over resources, allowing you to specify exact CPU and memory allocations for your containers. You can provision up to 4 vCPUs and 30GB of memory per task. Lambda, meanwhile, provides up to 10GB of memory and automatically allocates proportional CPU power based on memory settings.

Pro Tip: Use Fargate for tasks requiring more control over runtime environments

In terms of scaling, Lambda excels with near-instantaneous scaling from zero to thousands of concurrent executions. According to AWS performance data, Lambda can scale to process millions of requests per second. Fargate scales more gradually but can maintain consistent performance for long-running applications.

Stat Fact: Lambda can scale from 0 to 1,000 concurrent executions in seconds, making it ideal for unpredictable workloads.

Cost Structure and Efficiency

Fargate charges are based on vCPU and memory resources allocated to your containers per second. The average cost ranges from $0.04048 per vCPU hour to $0.004445 per GB hour in US East regions. Lambda bills based on request count and execution duration, with pricing starting at $0.20 per million requests and $0.0000166667 per GB-second.

Example: Running a container with 2 vCPU and 4 GB memory for one hour costs $0.10493 in the US East region.

Recent analysis shows that Lambda can be more cost-effective for sporadic workloads, while Fargate often proves economical for consistent, long-running applications. Organizations report average cost savings of 30-40% when choosing the right service for their specific use case.

Pro Tip: Use Lambda’s free tier for up to 1 million requests and 400,000 GB-seconds of compute time per month.

Execution Models

AWS Fargate: Containerized Application Support

AWS Fargate is a serverless compute engine designed for containerized workloads. It allows you to run containers without managing servers or clusters. Developers define container requirements, such as vCPU and memory, while Fargate handles provisioning and scaling.

Example: Imagine hosting a microservices-based application. Fargate runs each container independently with resource isolation. It scales dynamically when traffic spikes.

AWS Lambda: Event-Driven Functionality

AWS Lambda operates on an event-driven model. It runs code in response to triggers, such as HTTP requests or file uploads. Unlike Fargate, Lambda doesn’t require container definitions. You upload your code, and Lambda manages scaling and infrastructure.

Example: Use Lambda to process images when uploaded to an S3 bucket. It executes the function and scales instantly to handle demand.

Key Differences:

  • Lambda functions have a 15-minute execution limit
  • Fargate containers can run indefinitely
  • Lambda cold starts typically range from 100ms to 1s
  • Fargate container startup times average 10-30 seconds

Key Takeaway: Choose Fargate for containerized applications and Lambda for quick event-driven tasks.

Development and Deployment

Flexibility with Fargate

Fargate supports a wide range of runtime environments through containers. Developers can use Docker to package their apps, offering flexibility in development.

Example: Use Fargate to run machine learning models with specific runtime requirements.

Ease of Use with Lambda

Lambda simplifies development by managing the entire infrastructure. Developers only focus on writing functions in supported languages like Python, Java, or Node.js.

Pro Tip: Use Lambda for simple, event-driven processes like sending notifications or processing API requests.

Takeaway: Fargate is ideal for complex applications needing customized environments, while Lambda simplifies lightweight task automation.

Performance Metrics:

Recent benchmarks indicate that Lambda achieves average response times of 100-200ms for optimized functions, while Fargate maintains more consistent performance with average response times of 10-50ms once containers are running.

Integration with AWS Services

Fargate’s Seamless Integration

Fargate integrates with ECS and EKS for container orchestration. It works with services like CloudWatch for monitoring, VPC for networking, and IAM for security.

Stat Fact: Fargate’s task isolation enhances security, with each container running in its own environment.

Lambda’s Deep Integration

Lambda integrates deeply with over 200 AWS services. It’s commonly paired with S3 for file triggers, DynamoDB for database events, and API Gateway for backend processing.

Pro Tip: Use Lambda with Step Functions to build serverless workflows with multiple tasks.

Key Takeaway: Fargate is suited for container-based AWS ecosystems, while Lambda is perfect for serverless workflows.

Security and Isolation

Both services provide robust security features but implement them differently:

Fargate:

  • Task-level isolation
  • Custom security groups
  • Integration with AWS VPC
  • Container-level security controls

Lambda:

  • Function-level isolation
  • Built-in DDoS protection
  • Automatic security patches
  • IAM role-based access control

Monitoring and Observability Takeaway:

Each service requires different monitoring approaches.

Fargate integrates with:

  • Amazon CloudWatch
  • AWS Container Insights
  • Third-party container monitoring tools

Lambda provides:

  • Built-in CloudWatch integration
  • X-Ray tracing
  • Detailed execution metrics
  • Real-time logging

Use Case Optimization

Industry data shows distinct patterns in service adoption:

Fargate excels for:

  • Microservices architectures (Used by 65% of enterprises)
  • Long-running applications
  • Complex processing tasks
  • Applications requiring specific runtime environments

Lambda performs best for:

  • Event-driven processing (Adopted by 78% of serverless users)
  • Real-time file processing
  • Backend APIs
  • Scheduled tasks

Integration Capabilities Both services offer extensive AWS ecosystem integration, but Lambda provides native triggers for over 200 AWS services, while Fargate typically requires additional configuration for service integration.

Recent statistics indicate that organizations using these services in their intended use cases report 40% faster deployment times and a 50% reduction in operational overhead compared to traditional server-based solutions.

Fargate Vs Lambda: Use Cases

AWS Fargate and Lambda excel in different scenarios. Knowing where each service works best helps you choose the right option for your cloud setup. Let’s look at the situations where Fargate and Lambda are the most useful.

Use Cases for AWS Fargate

Microservices Architecture: Fargate is ideal for running multiple containerized microservices. Each service operates independently, enabling better scalability and modularity.

Example: Hosting an e-commerce platform with separate containers for inventory, payments, and user management.

Batch Processing Jobs: Use Fargate for jobs requiring predictable, long-running processes. It handles container orchestration while maintaining resource isolation.

Example: Generating daily sales reports or processing bulk data imports.

Machine Learning Models: Fargate supports custom runtimes for AI/ML workloads that require specific dependencies.

Example: Training a machine learning model using TensorFlow in a Docker container.

Use Cases for AWS Lambda

Event-Driven Workloads: Lambda is excellent for responding to real-time triggers, such as file uploads or database updates.

Example: Automatically resizing images when uploaded to an S3 bucket.

API Backends: Pair Lambda with API Gateway to handle HTTP requests without managing infrastructure.

Example: Building a serverless backend for a mobile app.

Automation and Alerts: Use Lambda for lightweight tasks like sending notifications or running scheduled jobs.

Example: Sending alerts when cloud resource usage exceeds thresholds.

Takeaway: Choose Fargate for containerized, continuous workloads and Lambda for real-time, lightweight, and event-driven tasks.

Fargate Vs Lambda: Costs Optimization

Optimizing costs for AWS Lambda and Fargate is key to maximizing your cloud budget. Both services offer flexible pricing, but small changes can lead to significant savings. Here’s how to manage costs effectively for each.

Cost Optimization Tips for AWS Lambda

Optimize Function Execution Time: Keep your Lambda functions efficient. Avoid unnecessary processing and optimize your code to reduce execution time.

Example: Use efficient algorithms and asynchronous operations where possible.

Choose the Right Memory Allocation: Allocate just enough memory for your function to perform well. Lambda pricing is tied to memory, so avoid over-allocation.

Pro Tip: Use AWS Lambda Power Tuning to find the optimal memory settings.

Leverage the Free Tier: Take advantage of Lambda’s free tier, offering 1 million requests and 400,000 GB-seconds of compute time monthly.

Use Reserved Concurrency: Set limits on concurrency for less critical functions to control spending during traffic spikes.

Cost Optimization Tips for AWS Fargate

Right-Size Resources: Select the appropriate vCPU and memory for your containerized workloads. Avoid over-provisioning.

Example: Use AWS Cost Explorer to analyze resource usage and adjust configurations.

Leverage Savings Plans: Commit to an AWS Compute Savings Plan to save up to 50% on long-term Fargate usage.

Use Fargate Spot Instances: Deploy tasks on Spot instances for significant savings, especially for non-critical workloads.

Takeaway: By fine-tuning resource allocation and leveraging AWS features, you can minimize costs for both Lambda and Fargate without compromising performance.

Factors to Consider When Choosing Between Fargate and Lambda

Making an informed choice between AWS Fargate and Lambda requires careful consideration of several key factors. Each service offers distinct advantages, and your decision should align with your specific operational requirements, technical needs, and business objectives. Let’s examine the crucial factors to consider.

Workload Type and Duration

The nature of your workload significantly influences your choice. Lambda functions have a 15-minute execution limit, making them ideal for short-term, event-driven tasks. If your application needs to run continuously or exceeds this time limit, Fargate would be more appropriate. Consider how your application processes data and responds to user requests when making this decision.

Performance Requirements

Your application’s performance needs play a crucial role in service selection. Lambda provides automatic scaling with consistent performance for concurrent executions but may experience cold starts. Fargate offers more predictable performance and allows fine-tuned resource allocation, which can be essential for applications requiring specific performance guarantors.

Scalability Patterns

Understanding your scalability needs is vital. Lambda excels at handling sudden spikes in traffic, automatically scaling from zero to thousands of instances within seconds. Fargate provides more controlled scaling, which may be preferable for applications with steady traffic patterns or those requiring gradual scaling.

Budget and Cost Management 

Financial considerations often drive the final decision. Lambda’s pay-per-use model typically proves more cost-effective for intermittent workloads, as you only pay for actual execution time. Fargate’s pricing model, based on allocated resources, often works better for consistent workloads where you can predict and optimize resource usage.

Development and Maintenance Requirements 

Consider your team’s expertise and capacity. Lambda requires less operational overhead but may limit runtime environments. Fargate provides more flexibility through container customization but requires container management skills and ongoing maintenance efforts. The choice should align with your team’s capabilities and available resources.


These are the things you have to consider when choosing between fargate and lambda.

FAQs

Does Lambda run on Fargate?
No, AWS Lambda and AWS Fargate are separate services with different purposes; Lambda doesn’t run on Fargate.

Are Lambdas cheaper than ECS?
Lambda is often cheaper for short, event-driven tasks, while ECS can be more cost-effective for long-running workloads.

How many vCPU can you have in Fargate?
AWS Fargate supports up to 16 vCPUs per task or pod, depending on the configuration.

What is the maximum size of AWS Fargate?
AWS Fargate allows a maximum of 200 GiB of memory.

What is the maximum size of the AWS Lambda package?
AWS Lambda packages can be up to 250 MB for direct upload or 10 GB when using container images.

Final Thoughts

AWS Fargate and Lambda are essential tools for building efficient serverless applications. Each service addresses different use cases effectively. Fargate is ideal for containerized, long-running workloads, providing fine-grained control over resources and configurations. Lambda excels in handling short, event-driven tasks with instant scalability.

Understanding the strengths of these services ensures they align perfectly with your application’s demands and budget. Fargate offers consistent performance for continuous processes, while Lambda minimizes costs for lightweight, reactive operations.

By utilizing the right service for the right task, businesses can achieve greater efficiency, reduced costs, and streamlined cloud operations, driving innovation and growth in the evolving serverless ecosystem.

Ashikul Islam

Ashikul Islam is an experienced HR Generalist specializing in recruitment, employee lifecycle management, performance management, and employee engagement, with additional expertise in Marketing lead generation, Content Writing, Designing and SEO.

Shadhin Lab LLC.229 West 36th Street, New York, NY 10018, USA.

Shadhin Technologies Ltd.Riajbag, Road-6, Rampura, Dhaka – 1219, Bangladesh.