
What I like most about AWS Lambda is how it completely removes the need to manage servers. You just focus on writing code, and it takes care of everything else—scaling, patching, provisioning. It’s incredibly efficient, especially for event-driven tasks like processing S3 uploads, reacting to database changes, or triggering workflows based on user actions. It also scales automatically, so whether you’re handling one request or a thousand, it just works without you needing to plan for capacity. Plus, the pay-as-you-go model means you're only charged for the time your code actually runs, which is great for keeping costs low. I also appreciate how well Lambda integrates with the rest of the AWS ecosystem—whether it's SQS, DynamoDB, or Step Functions, it all ties together seamlessly. It’s a powerful tool for building modern, scalable, and cost-effective applications. Review collected by and hosted on G2.com.
One of the main things I dislike about AWS Lambda is the **cold start latency**, especially when using certain runtimes like Java or when functions are in a VPC. That delay, even if it's just a couple of seconds, can be noticeable in user-facing applications. Another challenge is **limited observability** out of the box—while you can plug in tools like CloudWatch or X-Ray, it often takes extra effort to get clear visibility into performance and failures. Also, **debugging and testing locally** can be tricky, especially when dealing with event sources like API Gateway or DynamoDB streams. And while the simplicity is great for small functions, managing a large number of Lambdas can get messy fast—things like versioning, environment variables, permissions, and deployment pipelines need to be tightly organized. Finally, the **timeout and memory limits** make Lambda unsuitable for certain long-running or resource-heavy workloads, so you have to design around those constraints. Review collected by and hosted on G2.com.
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