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In this post, we will compare two of Amazon Web Services’ (AWS) most popular computing services: AWS Lambda and Amazon EC2. Both services offer unique advantages and can be used for different purposes.
AWS Lambda is a serverless compute service that runs your code in response to events and automatically manages the underlying compute resources for you.
With Lambdas, you pay only for the compute time you consume – there is no charge when your code is not running. Use our Lambda cost calculator to estimate your costs.
Learn more: 6 Lambda use cases
Amazon Elastic Compute Cloud (Amazon EC2) is a web service that provides resizable compute capacity in the cloud. It is designed to make web-scale cloud computing easier for developers.
You pay for the compute capacity by the hour or second, depending on which instances you run.
Consider a scenario where you’re running a high-traffic web application that requires a constant online presence and has predictable traffic patterns. In this case, EC2 instances would be a more suitable choice.
For instance, if you’re running an e-commerce website that gets consistent traffic throughout the day, EC2 instances would provide the constant availability you need. You can also manually scale up the instances during peak times, like during a sale or holiday season, to handle the increased traffic.
Moreover, if your application requires long-running processes, complex computations, or specific operating system configurations, EC2’s flexibility and control over the computing environment make it a better choice.
On the other hand, consider a scenario where you’re building a microservices-based application or a real-time file processing system. In this case, AWS Lambda would be a more suitable choice.
For example, if you’re building a real-time image processing application where users upload photos and you need to process those photos (resize, apply filters, etc.) in real-time, AWS Lambda would be ideal. Since Lambda functions are event-driven, they would only run when a user uploads a photo, making it cost-effective for sporadic usage patterns.
Similarly, for a microservices architecture where each microservice is exposed as a function and the load on each function can vary drastically, AWS Lambda’s automatic scaling would be beneficial. It allows each function to scale independently based on the demand, providing efficient resource utilization.
For stateful workloads, such as setting up databases like Couchbase or MongoDB, or hosting environments for backup and disaster recovery, EC2 is the preferred choice. Similarly, for high-performance computing applications that require high compute power, EC2, with its unrestricted memory limit, is more suitable than Lambda, which has a memory limit of 3 GB.
If your application deals with highly sensitive data and security is a primary concern, it’s advisable to run Lambda functions under a dedicated VPC or use EC2. However, running Lambda under a VPC comes with its own challenges, such as increased cold start time and limited concurrent executions.
While DevOps for EC2 has reached a good level of maturity, Lambda is still evolving in this area. Local testing is another aspect to consider when using Lambda due to its limitations.
Lambda is designed primarily for handling event-based functions, making it the best fit for scenarios where a new record is added to DynamoDB or a new file is added to an S3 bucket. Similarly, for infrequently accessed applications or scheduled jobs, Lambda is the right choice as it saves cost by not needing to run the server all the time.
In this article, we’re covering 4 tips for AWS Lambda optimization for production. Covering error handling, memory provisioning, monitoring, performance, and more.
In this article we’ll go through the ins and outs of AWS Lambda pricing model, how it works, what additional charges you might be looking at and what’s in the fine print.
By increasing memory, your function runs faster and may end up saving you money!
Dashbird was born out of our own need for an enhanced serverless debugging and monitoring tool, and we take pride in being developers.
Dashbird gives us a simple and easy to use tool to have peace of mind and know that all of our Serverless functions are running correctly. We are instantly aware now if there’s a problem. We love the fact that we have enough information in the Slack notification itself to take appropriate action immediately and know exactly where the issue occurred.
Thanks to Dashbird the time to discover the occurrence of an issue reduced from 2-4 hours to a matter of seconds or minutes. It also means that hundreds of dollars are saved every month.
Great onboarding: it takes just a couple of minutes to connect an AWS account to an organization in Dashbird. The UI is clean and gives a good overview of what is happening with the Lambdas and API Gateways in the account.
I mean, it is just extremely time-saving. It’s so efficient! I don’t think it’s an exaggeration or dramatic to say that Dashbird has been a lifesaver for us.
Dashbird provides an easier interface to monitor and debug problems with our Lambdas. Relevant logs are simple to find and view. Dashbird’s support has been good, and they take product suggestions with grace.
Great UI. Easy to navigate through CloudWatch logs. Simple setup.
Dashbird helped us refine the size of our Lambdas, resulting in significantly reduced costs. We have Dashbird alert us in seconds via email when any of our functions behaves abnormally. Their app immediately makes the cause and severity of errors obvious.
End-to-end observability and real-time error tracking for AWS applications.