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What's the Difference Between Amazon EMR and EMR Serverless?

Compare Amazon EMR and EMR Serverless side by side — features, pricing, and ideal use cases to help you choose the right product.

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Comparisons
Amazon EMR
EMR Serverless
Category

Analytics, Big data processing

Analytics, Serverless Big Data
Description

Easily run big data frameworks

Run big data frameworks like Apache Spark and Hive without configuring or managing clusters.
Best for
  • Big data processing
  • Machine learning
  • ETL
  • Clickstream analysis
  • Genomics
  • Serverless Spark
  • Serverless Hive
  • ETL jobs
  • Data transformation
Key features
  • Apache Spark
  • Apache Hive
  • Presto
  • Amazon EMR Serverless
  • EMR on EKS
  • No cluster management
  • Auto scaling
  • Spark & Hive support
  • Fine-grained scaling
  • Job-level isolation
Pricing model

Pay per instance hour + EBS storage

Pay per vCPU, memory, and storage per second
Free tier

No

No
Expert take

“EMR is the go-to for teams already invested in Spark, Hive, or Presto. Amazon EMR Serverless removes cluster sizing decisions entirely; you submit jobs and pay per vCPU-second. For teams that want SQL-only without Spark expertise, Athena or Redshift are simpler paths.”
— Didier Durand, re:Post Top Contributor [profile]

“Amazon EMR Serverless runs Spark and Hive jobs without managing clusters. Submit jobs and it provisions the right amount of compute, then releases it when the job completes.”
— Riku Kobayashi, re:Post Top Contributor [profile]
Product page

Learn more about Amazon EMR »

Learn more about Amazon EMR Serverless »

When to use Amazon EMR or Amazon EMR Serverless

Use Amazon EMR when:

  • Big data processing
  • Machine learning
  • ETL
  • Clickstream analysis
  • Genomics

Learn more about Amazon EMR »

Use Amazon EMR Serverless when:

  • Serverless Spark
  • Serverless Hive
  • ETL jobs
  • Data transformation

Learn more about Amazon EMR Serverless »

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