---
source_url: "https://www.anyscale.com/compare/ray-vs-spark"
title: Comparing Ray to Apache Spark
mirrored_at: 2026-08-29T03:03:08.001Z
host: www.anyscale.com
cited_in_42a: true
mirror_canonical: "https://index.42a.ai/www.anyscale.com/compare/ray-vs-spark"
---

> **Original source:** https://www.anyscale.com/compare/ray-vs-spark

Explore which tool is right for you based on your use case, and see why Ray is the leading option for AI training, serving, unstructured data processing, and batch inference.

![Ray Spark V2](https://images.ctfassets.net/xjan103pcp94/1lP8WOCGrjQ5Ot0CEX8QLF/ce88a180f9bce6a1fb901955e7fd258a/Ray_Spark.png)

## At a Glance: Ray vs. Spark

## 4 Fundamental Differences Between Ray and Spark

![amazon-quote-logo](https://images.ctfassets.net/xjan103pcp94/2Uu9qXZ5EyTwvCeFudfDv2/135392f05c346d36858918a2bce408aa/Quote-logo.png)

### How Amazon Saved $120 Million Per Year by Choosing Ray Over Spark

With Ray, Amazon could compact 12X larger datasets than Apache Spark, improve cost efficiency by 91%, and process 13X more data per hour.

## AI Compute Engine for Any Workload

Ray is Python-native, cloud-first, and future-proof, making it the best choice for modern and future distributed computing challenges.

### Heterogeneous Compute

with CPUs and GPUs

![Ray](https://images.ctfassets.net/xjan103pcp94/72ilTz1HHwuM0198EeTxA0/3d031588c369491e8b81c75f74f1d39b/Frame_297.png)

![Apache Spark](https://images.ctfassets.net/xjan103pcp94/5LABkNikSqp9qz2pcCn8xh/d8d1849aa1e7b82adc81ffae02e5e50c/Apache_Spark_logo_2.png)

![image-processing](https://images.ctfassets.net/xjan103pcp94/29qJQYjgbVArrEOwWEBqxB/9ad16fe3d8026ae09bb62d4f9647f784/Frame_174.svg)

## Unstructured Data Processing

![Ray](https://images.ctfassets.net/xjan103pcp94/72ilTz1HHwuM0198EeTxA0/3d031588c369491e8b81c75f74f1d39b/Frame_297.png)

Ray was built to support modern AI/ML workloads, and it excels at processing unstructured data, such as video, images, and text. Ray’s ability to orchestrate heterogeneous compute resources makes it ideal for processing multimodal data across GPUs and CPUs, significantly improving efficiency and reducing cost.

Ray was built to support modern AI/ML workloads, and it excels at processing unstructured data, such as video, images, and text. Ray’s ability to orchestrate heterogeneous compute resources makes it ideal for processing multimodal data across GPUs and CPUs, significantly improving efficiency and reducing cost.

![Apache Spark](https://images.ctfassets.net/xjan103pcp94/5LABkNikSqp9qz2pcCn8xh/d8d1849aa1e7b82adc81ffae02e5e50c/Apache_Spark_logo_2.png)

While Spark does offer some support for unstructured data processing, its SQL and Dataframes APIs are optimized for structured and semi-structured data. Spark is less efficient at processing unstructured data, including multimodal data, especially when requiring heterogeneous compute resources.

While Spark does offer some support for unstructured data processing, its SQL and Dataframes APIs are optimized for structured and semi-structured data. Spark is less efficient at processing unstructured data, including multimodal data, especially when requiring heterogeneous compute resources.

![Map](https://images.ctfassets.net/xjan103pcp94/jUx7eNttUZ8KLQMqIynpf/8058629be988e93d9440e5476d72c2fd/Map.svg)

## Model Training

![Ray](https://images.ctfassets.net/xjan103pcp94/72ilTz1HHwuM0198EeTxA0/3d031588c369491e8b81c75f74f1d39b/Frame_297.png)

Ray is not just a framework for processing unstructured data. Once you’ve processed your data, Ray Data integrates seamlessly with the other Ray libraries including Ray Train for training deep-neural networks (DNNs), large language models (LLMs), as well as classic ML models.

Ray is not just a framework for processing unstructured data. Once you’ve processed your data, Ray Data integrates seamlessly with the other Ray libraries including Ray Train for training deep-neural networks (DNNs), large language models (LLMs), as well as classic ML models.

![Apache Spark](https://images.ctfassets.net/xjan103pcp94/5LABkNikSqp9qz2pcCn8xh/d8d1849aa1e7b82adc81ffae02e5e50c/Apache_Spark_logo_2.png)

Spark is primarily a data processing platform. While it supports classic ML workloads, it is less effective at training LLMs and other deep neural networks, which require hardware-accelerator support.

Spark is primarily a data processing platform. While it supports classic ML workloads, it is less effective at training LLMs and other deep neural networks, which require hardware-accelerator support.

![connect](https://images.ctfassets.net/xjan103pcp94/2MtfS5NGNQOUFMpUIfQSyk/588259f399ad299ce5334e54e2518027/Frame_174.svg)

## Model Serving

![Ray](https://images.ctfassets.net/xjan103pcp94/72ilTz1HHwuM0198EeTxA0/3d031588c369491e8b81c75f74f1d39b/Frame_297.png)

Ray Data connects seamlessly to Ray Serve, making it possible to serve any AI model after pre-processing your data.

Ray Data connects seamlessly to Ray Serve, making it possible to serve any AI model after pre-processing your data.

![Apache Spark](https://images.ctfassets.net/xjan103pcp94/5LABkNikSqp9qz2pcCn8xh/d8d1849aa1e7b82adc81ffae02e5e50c/Apache_Spark_logo_2.png)

Spark focusses on data processing, and has little support for LLM/DNN serving, especially in online settings.

Spark focusses on data processing, and has little support for LLM/DNN serving, especially in online settings.

![Magic](https://images.ctfassets.net/xjan103pcp94/4I80OgpBvcOAedh26NyIOU/5a440d3a0c1313f49cb162f7cea76e3b/Frame_174-1.svg)

## Gen AI Workloads

![Ray](https://images.ctfassets.net/xjan103pcp94/72ilTz1HHwuM0198EeTxA0/3d031588c369491e8b81c75f74f1d39b/Frame_297.png)

Ray’s ability to orchestrate heterogeneous compute across GPUs and CPUs, as well as its Pythonic API, make it the leading choice for scaling AI and GenAI workloads, no matter how sophisticated these workloads are.

Ray’s ability to orchestrate heterogeneous compute across GPUs and CPUs, as well as its Pythonic API, make it the leading choice for scaling AI and GenAI workloads, no matter how sophisticated these workloads are.

![Apache Spark](https://images.ctfassets.net/xjan103pcp94/5LABkNikSqp9qz2pcCn8xh/d8d1849aa1e7b82adc81ffae02e5e50c/Apache_Spark_logo_2.png)

While Spark is effective for classic ML workloads, many modern AI and GenAI workloads require support for hardware accelerators and more flexible, low-level APIs.

While Spark is effective for classic ML workloads, many modern AI and GenAI workloads require support for hardware accelerators and more flexible, low-level APIs.

![structure](https://images.ctfassets.net/xjan103pcp94/5kpba89iw43fjNqoyEJxF1/12ee5b9df687c3ec7d8b78336399c5f1/Frame_174-2.svg)

## Structured Data Processing

![Ray](https://images.ctfassets.net/xjan103pcp94/72ilTz1HHwuM0198EeTxA0/3d031588c369491e8b81c75f74f1d39b/Frame_297.png)

Ray was built for modern and future AI/ML workloads, where engineers are largely manipulating unstructured data. Ray isn’t optimized for most structured data processing, though it is effective at last-mile preprocessing.

Ray’s flexibility supports Spark on Ray for structured data processing.

Ray was built for modern and future AI/ML workloads, where engineers are largely manipulating unstructured data. Ray isn’t optimized for most structured data processing, though it is effective at last-mile preprocessing.

Ray’s flexibility supports Spark on Ray for structured data processing.

![Apache Spark](https://images.ctfassets.net/xjan103pcp94/5LABkNikSqp9qz2pcCn8xh/d8d1849aa1e7b82adc81ffae02e5e50c/Apache_Spark_logo_2.png)

Spark is optimized for high-performance processing of structured and semi-structured relational data through its SQL and Dataframe APIs. While effective for classic ML workloads, many modern AI and GenAI workloads include large amounts of unstructured, multimodal data.

Spark is optimized for high-performance processing of structured and semi-structured relational data through its SQL and Dataframe APIs. While effective for classic ML workloads, many modern AI and GenAI workloads include large amounts of unstructured, multimodal data.

![data-framework](https://images.ctfassets.net/xjan103pcp94/7mlb16CqWf39jAJnR21zw5/711465864ae5e3979a488ff0b44f5267/data-framework.svg)

## Scaling Arbitrary Python Programs

![Ray](https://images.ctfassets.net/xjan103pcp94/72ilTz1HHwuM0198EeTxA0/3d031588c369491e8b81c75f74f1d39b/Frame_297.png)

With its general, Python-native API, Ray supports arbitrary Python applications. Developers can take their existing applications and scale them by just adding a few lines a code. No need to rewrite them!

With its general, Python-native API, Ray supports arbitrary Python applications. Developers can take their existing applications and scale them by just adding a few lines a code. No need to rewrite them!

![Apache Spark](https://images.ctfassets.net/xjan103pcp94/5LABkNikSqp9qz2pcCn8xh/d8d1849aa1e7b82adc81ffae02e5e50c/Apache_Spark_logo_2.png)

Spark is implementing a data parallel computation model. While a good fit for scaling data workloads, it is not flexible enough to scale arbitrary workloads.

Spark is implementing a data parallel computation model. While a good fit for scaling data workloads, it is not flexible enough to scale arbitrary workloads.

## Explore the Difference

For a more detailed technical comparison, see our full breakdown.

## FAQs

#### The Best Option for Data Processing At Scale

Get up to 60% cost reduction on unstructured data processing with Anyscale, the smartest place to run Ray.