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    <title>Serverless on Daman Arora</title>
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      <title>AWS Batch vs Lambda: Choosing the Right Runner</title>
      <link>https://damanarora.netlify.app/posts/aws-batch-vs-lambda/</link>
      <pubDate>Sun, 17 May 2026 16:00:00 +0000</pubDate>
      
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      <description>Both AWS Batch and Lambda run your code without making you manage servers — but they are built for very different shapes of work. Knowing which is which saves you from forcing a long job into a service that caps out at 15 minutes.
The core difference Lambda Batch Built for Short, event-driven tasks Long, heavy batch jobs Execution time ≤ 15 min Hours or days — no limit Trigger Events (S3, API, schedule) Job submission Resources Up to 10 GB RAM Any EC2/Fargate size, including GPUs Runs on Lambda&amp;rsquo;s managed runtime EC2 or Fargate (you don&amp;rsquo;t manage them, but they exist) Scaling Instant, per invocation Queues jobs, then provisions compute Use case Glue code, reactions, APIs Data processing, ML training, simulations Mental model Lambda: &amp;#34;Run this small function NOW, fast.</description>
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      <title>AWS Lambda: Serverless Compute</title>
      <link>https://damanarora.netlify.app/posts/aws-lambda-serverless/</link>
      <pubDate>Sun, 17 May 2026 14:00:00 +0000</pubDate>
      
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      <description>Serverless is a shift in how you think about compute: instead of provisioning and running servers, you just deploy code. AWS Lambda is the service that pioneered it, and it is the third compute option in the AWS model — alongside EC2 and Fargate.
What serverless means A new paradigm — developers don&amp;rsquo;t manage servers, they just deploy code (functions). It started as FaaS (Function as a Service), pioneered by AWS Lambda.</description>
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