AppFog was a cloud-agnostic application and infrastructure management platform used to manage workloads across on-premises and third-party cloud environments. It has been discontinued.
$0
AWS Batch
Score 7.7 out of 10
N/A
With AWS Batch, users package the code for batch jobs, specify dependencies, and submit batch jobs using the AWS Management Console, CLIs, or SDKs. AWS Batch allows users to specify execution parameters and job dependencies, and facilitates integration with a broad range of popular batch computing workflow engines and languages (e.g., Pegasus WMS, Luigi, Nextflow, Metaflow, Apache Airflow, and AWS Step Functions).
N/A
AWS Lambda
Score 8.3 out of 10
N/A
AWS Lambda is a serverless computing platform that lets users run code without provisioning or managing servers. With Lambda, users can run code for virtually any type of app or backend service—all with zero administration. It takes of requirements to run and scale code with high availability.
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Pricing
AppFog (discontinued)
AWS Batch
AWS Lambda
Editions & Modules
No answers on this topic
No answers on this topic
128 MB
$0.0000000021
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1024 MB
$0.0000000167
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10240 MB
$0.0000001667
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Offerings
Pricing Offerings
AppFog (discontinued)
AWS Batch
AWS Lambda
Free Trial
No
No
No
Free/Freemium Version
Yes
No
No
Premium Consulting/Integration Services
No
No
No
Entry-level Setup Fee
No setup fee
No setup fee
No setup fee
Additional Details
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More Pricing Information
Community Pulse
AppFog (discontinued)
AWS Batch
AWS Lambda
Features
AppFog (discontinued)
AWS Batch
AWS Lambda
Platform-as-a-Service
Comparison of Platform-as-a-Service features of Product A and Product B
AppFog (discontinued)
6.5
2 Ratings
18% below category average
AWS Batch
-
Ratings
AWS Lambda
-
Ratings
Ease of building user interfaces
7.01 Ratings
00 Ratings
00 Ratings
Scalability
5.32 Ratings
00 Ratings
00 Ratings
Platform management overhead
6.02 Ratings
00 Ratings
00 Ratings
Workflow engine capability
6.01 Ratings
00 Ratings
00 Ratings
Platform access control
6.01 Ratings
00 Ratings
00 Ratings
Services-enabled integration
6.62 Ratings
00 Ratings
00 Ratings
Development environment creation
7.42 Ratings
00 Ratings
00 Ratings
Development environment replication
8.42 Ratings
00 Ratings
00 Ratings
Issue monitoring and notification
6.01 Ratings
00 Ratings
00 Ratings
Issue recovery
6.42 Ratings
00 Ratings
00 Ratings
Upgrades and platform fixes
7.02 Ratings
00 Ratings
00 Ratings
Workload Automation
Comparison of Workload Automation features of Product A and Product B
AppFog (discontinued)
-
Ratings
AWS Batch
7.3
7 Ratings
13% below category average
AWS Lambda
-
Ratings
Multi-platform scheduling
00 Ratings
6.06 Ratings
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Central monitoring
00 Ratings
8.06 Ratings
00 Ratings
Logging
00 Ratings
10.06 Ratings
00 Ratings
Alerts and notifications
00 Ratings
5.06 Ratings
00 Ratings
Analysis and visualization
00 Ratings
5.95 Ratings
00 Ratings
Application integration
00 Ratings
8.76 Ratings
00 Ratings
Access Control and Security
Comparison of Access Control and Security features of Product A and Product B
It was very good to use in small scale projects. Considering the high end projects with many instances and multi-platform architectures, it is better to test before the application is deployed. I think few of the questions can be general - who are the system users and what size is the application focussing on? How much resources are required? Will the application require any additional services?
More appropriate if you have a tech group that can use more of the AWS Batch rather than one or 2 things. It works great for me, but there was a huge learning curve the first week of using it. Now, I love it - and I hope to dig deep into other parts not just S3.
Lambda excels at event-driven, short-lived tasks, such as processing files or building simple APIs. However, it's less ideal for long-running, computationally intensive, or applications that rely on carrying the state between jobs. Cold starts and constant load can easily balloon the costs.
Developing test cases for Lambda functions can be difficult. For functions that require some sort of input it can be tough to develop the proper payload and event for a test.
For the uninitiated, deploying functions with Infrastructure as Code tools can be a challenging undertaking.
Logging the output of a function feels disjointed from running the function in the console. A tighter integration with operational logging would be appreciated, perhaps being able to view function logs from the Lambda console instead of having to navigate over to CloudWatch.
Sometimes its difficult to determine the correct permissions needed for Lambda execution from other AWS services.
Key advantages include cost-effectiveness through dynamic resource provisioning and the use of spot instances. It auto-scales to meet workload demands, allowing easy job submission via the AWS Management Console or SDKs. It integrates seamlessly with other services like S3 and CloudWatch. It features automatic retries for failed jobs. It allows for a custom computing environment tailored to specific needs
I give it a seven is usability because it's AWS. Their UI's are always clunkier than the competition and their documentation is rather cumbersome. There's SO MUCH to dig through and it's a gamble if you actually end up finding the corresponding info if it will actually help. Like I said before, going to google with a specific problem is likely a better route because AWS is quite ubiquitous and chances are you're not the first to encounter the problem. That being said, using SAM (Serverless application model) and it's SAM Local environment makes running local instances of your Lambdas in dev environments painless and quite fun. Using Nodejs + Lambda + SAM Local + VS Code debugger = AWESOME.
Amazon consistently provides comprehensive and easy-to-parse documentation of all AWS features and services. Most development team members find what they need with a quick internet search of the AWS documentation available online. If you need advanced support, though, you might need to engage an AWS engineer, and that could be an unexpected (or unwelcome) expense.
Primarily because it used to have a good free tier earlier, which it does not anymore. It's simple, and things are available to use. Compared to it's competitors, it does has less features, but that kind of acts in its favor. That adds to the simplicity, and ease of use for a new user.
AWS Lambda is good for short running functions, and ideally in response to events within AWS. Google App Engine is a more robust environment which can have complex code running for long periods of time, and across more than one instance of hardware. Google App Engine allows for both front-end and back-end infrastructure, while AWS Lambda is only for small back-end functions
Positive - Only paying for when code is run, unlike virtual machines where you pay always regardless of processing power usage.
Positive - Scalability and accommodating larger amounts of demand is much cheaper. Instead of scaling up virtual machines and increasing the prices you pay for that, you are just increasing the number of times your lambda function is run.
Negative - Debugging/troubleshooting, and developing for lambda functions take a bit more time to get used to, and migrating code from virtual machines and normal processes to Lambda functions can take a bit of time.