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AWS Lambda vs IronWorker: What are the differences?

What is AWS Lambda? Automatically run code in response to modifications to objects in Amazon S3 buckets, messages in Kinesis streams, or updates in DynamoDB. 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.

What is IronWorker? High-Scale Async Task Processing. IronWorker provides the muscle for modern applications by efficiently isolating the code and dependencies of individual tasks to be processed on demand. Run in a multi-language containerized environment with streamlined orchestration, IronWorker gives you the flexibility to power any task in parallel at massive scale.

AWS Lambda and IronWorker can be categorized as "Serverless / Task Processing" tools.

Some of the features offered by AWS Lambda are:

  • Extend other AWS services with custom logic
  • Build custom back-end services
  • Completely Automated Administration

On the other hand, IronWorker provides the following key features:

  • Containerized Environment
  • High-Scale Processing
  • Flexible Scheduling
Decisions about AWS Lambda and IronWorker
Tim Nolet
Tim Nolet
Founder, Engineer & Dishwasher at Checkly · | 13 upvotes · 20.3K views

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

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    What is 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.

    What is IronWorker?

    IronWorker provides the muscle for modern applications by efficiently isolating the code and dependencies of individual tasks to be processed on demand. Run in a multi-language containerized environment with streamlined orchestration, IronWorker gives you the flexibility to power any task in parallel at massive scale.
    What companies use AWS Lambda?
    What companies use IronWorker?

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    What tools integrate with AWS Lambda?
    What tools integrate with IronWorker?
      No integrations found

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      What are some alternatives to AWS Lambda and IronWorker?
      Serverless
      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.
      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.
      AWS Elastic Beanstalk
      Once you upload your application, Elastic Beanstalk automatically handles the deployment details of capacity provisioning, load balancing, auto-scaling, and application health monitoring.
      AWS Step Functions
      AWS Step Functions makes it easy to coordinate the components of distributed applications and microservices using visual workflows. Building applications from individual components that each perform a discrete function lets you scale and change applications quickly.
      Google App Engine
      Google has a reputation for highly reliable, high performance infrastructure. With App Engine you can take advantage of the 10 years of knowledge Google has in running massively scalable, performance driven systems. App Engine applications are easy to build, easy to maintain, and easy to scale as your traffic and data storage needs grow.
      See all alternatives
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