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  1. Stackups
  2. Application & Data
  3. Serverless
  4. Serverless Task Processing
  5. AWS Firecracker vs Serverless

AWS Firecracker vs Serverless

OverviewDecisionsComparisonAlternatives

Overview

Serverless
Serverless
Stacks2.2K
Followers1.2K
Votes28
GitHub Stars46.9K
Forks5.7K
AWS Firecracker
AWS Firecracker
Stacks6
Followers34
Votes0
GitHub Stars31.0K
Forks2.1K

AWS Firecracker vs Serverless: What are the differences?

## Introduction
This markdown provides a comparison between AWS Firecracker and Serverless focusing on key differences between the two technologies.

1. **Execution Environment**: AWS Firecracker provides a lightweight virtual machine monitor, allowing for fast boot times and a minimal attack surface compared to traditional virtual machines. On the other hand, Serverless abstracts the infrastructure management entirely, enabling developers to focus on code without worrying about servers, scaling, or maintenance.
   
2. **Resource Utilization**: AWS Firecracker enables users to run container workloads efficiently with minimal overhead, making it suitable for scenarios requiring a high degree of isolation. In contrast, Serverless automatically scales resources up or down based on demand, optimizing cost efficiency by charging only for resources consumed during execution.

3. **Cold Start Performance**: AWS Firecracker offers improved cold start performance for containers by reducing the time needed to launch and execute workloads. Serverless, while also designed for rapid scaling and execution, may experience slightly longer cold start times due to container initialization and scaling mechanisms.

4. **Flexibility**: AWS Firecracker provides users with more control over the underlying infrastructure, allowing for customized configurations and bespoke setups to optimize performance. In comparison, Serverless abstracts infrastructure details, offering a quick and easy way to deploy applications without the need for manual configuration or management.

5. **Integration with Ecosystem**: AWS Firecracker integrates seamlessly with the broader AWS ecosystem, enabling users to leverage existing services and resources for enhanced functionality. Serverless, while offering integrations with various cloud providers and services, may require additional configurations or adaptations to work optimally within specific cloud environments.

6. **Deployment Model**: AWS Firecracker is typically used for container-based applications that require a lightweight and secure virtualization environment. In contrast, Serverless is ideal for event-driven applications and microservices, allowing developers to focus on writing code without the need to manage underlying infrastructure.

In Summary, the key differences between AWS Firecracker and Serverless lie in their execution environments, resource utilization, cold start performance, flexibility, integration with ecosystems, and deployment models.

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Advice on Serverless, AWS Firecracker

Tim
Tim

CTO at Checkly Inc.

Sep 18, 2019

Needs adviceonHerokuHerokuAWS LambdaAWS Lambda

When adding a new feature to Checkly rearchitecting some older piece, I tend to pick Heroku for rolling it out. But not always, because sometimes I pick AWS Lambda . The short story:

  • Developer Experience trumps everything.
  • AWS Lambda is cheap. Up to a limit though. This impact not only your wallet.
  • If you need geographic spread, AWS is lonely at the top.

The setup

Recently, I was doing a brainstorm at a startup here in Berlin on the future of their infrastructure. They were ready to move on from their initial, almost 100% Ec2 + Chef based setup. Everything was on the table. But we crossed out a lot quite quickly:

  • Pure, uncut, self hosted Kubernetes — way too much complexity
  • Managed Kubernetes in various flavors — still too much complexity
  • Zeit — Maybe, but no Docker support
  • Elastic Beanstalk — Maybe, bit old but does the job
  • Heroku
  • Lambda

It became clear a mix of PaaS and FaaS was the way to go. What a surprise! That is exactly what I use for Checkly! But when do you pick which model?

I chopped that question up into the following categories:

  • Developer Experience / DX 🤓
  • Ops Experience / OX 🐂 (?)
  • Cost 💵
  • Lock in 🔐

Read the full post linked below for all details

357k views357k
Comments

Detailed Comparison

Serverless
Serverless
AWS Firecracker
AWS Firecracker

Build applications comprised of microservices that run in response to events, auto-scale for you, and only charge you when they run. This lowers the total cost of maintaining your apps, enabling you to build more logic, faster. The Framework uses new event-driven compute services, like AWS Lambda, Google CloudFunctions, and more.

Firecracker is an open source virtualization technology that is purpose-built for creating and managing secure, multi-tenant container and function-based services that provide serverless operational models. Firecracker runs workloads in lightweight virtual machines, called microVMs, which combine the security and isolation properties provided by hardware virtualization technology with the speed and flexibility of containers.

Statistics
GitHub Stars
46.9K
GitHub Stars
31.0K
GitHub Forks
5.7K
GitHub Forks
2.1K
Stacks
2.2K
Stacks
6
Followers
1.2K
Followers
34
Votes
28
Votes
0
Pros & Cons
Pros
  • 14
    API integration
  • 7
    Supports cloud functions for Google, Azure, and IBM
  • 3
    Lower cost
  • 1
    Auto scale
  • 1
    Openwhisk
No community feedback yet
Integrations
Azure Functions
Azure Functions
AWS Lambda
AWS Lambda
Amazon API Gateway
Amazon API Gateway
No integrations available

What are some alternatives to Serverless, AWS Firecracker?

AWS Lambda

AWS Lambda

AWS Lambda is a compute service that runs your code in response to events and automatically manages the underlying compute resources for you. You can use AWS Lambda to extend other AWS services with custom logic, or create your own back-end services that operate at AWS scale, performance, and security.

Azure Functions

Azure Functions

Azure Functions is an event driven, compute-on-demand experience that extends the existing Azure application platform with capabilities to implement code triggered by events occurring in virtually any Azure or 3rd party service as well as on-premises systems.

Google Cloud Run

Google Cloud Run

A managed compute platform that enables you to run stateless containers that are invocable via HTTP requests. It's serverless by abstracting away all infrastructure management.

Google Cloud Functions

Google Cloud Functions

Construct applications from bite-sized business logic billed to the nearest 100 milliseconds, only while your code is running

Knative

Knative

Knative provides a set of middleware components that are essential to build modern, source-centric, and container-based applications that can run anywhere: on premises, in the cloud, or even in a third-party data center

OpenFaaS

OpenFaaS

Serverless Functions Made Simple for Docker and Kubernetes

Nuclio

Nuclio

nuclio is portable across IoT devices, laptops, on-premises datacenters and cloud deployments, eliminating cloud lock-ins and enabling hybrid solutions.

Apache OpenWhisk

Apache OpenWhisk

OpenWhisk is an open source serverless platform. It is enterprise grade and accessible to all developers thanks to its superior programming model and tooling. It powers IBM Cloud Functions, Adobe I/O Runtime, Naver, Nimbella among others.

Cloud Functions for Firebase

Cloud Functions for Firebase

Cloud Functions for Firebase lets you create functions that are triggered by Firebase products, such as changes to data in the Realtime Database, uploads to Cloud Storage, new user sign ups via Authentication, and conversion events in Analytics.

AWS Batch

AWS Batch

It enables developers, scientists, and engineers to easily and efficiently run hundreds of thousands of batch computing jobs on AWS. It dynamically provisions the optimal quantity and type of compute resources (e.g., CPU or memory optimized instances) based on the volume and specific resource requirements of the batch jobs submitted.

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