Is Ray better than Spark for machine learning workloads?

For modern ML — distributed training of deep learning models, hyperparameter tuning, reinforcement learning, and LLM serving — Ray is the better fit. Ray was designed Python-first with fine-grained tasks and stateful actors, where Spark assumes coarse-grained data-parallel transformations on a JVM. For ETL on large structured datasets, Spark still wins.

When should I pick vLLM over Ray Serve for LLM serving?

vLLM is a single-node inference engine optimized for high-throughput LLM serving with PagedAttention and continuous batching. Ray Serve is an orchestration layer that runs multiple replicas across a cluster. The common production pattern is to deploy vLLM workers behind Ray Serve so you get vLLM throughput with Ray autoscaling and fault tolerance.

Does Ray work with Dask?

Yes. Ray ships a Dask-on-Ray scheduler that lets existing Dask DataFrame and Dask Array code run on a Ray cluster. This is the recommended path for teams who like the pandas-like Dask API but want Ray for actor-based serving and tuning.

How much does Ray cost compared to managed alternatives?

Ray itself is Apache 2.0 and free. Managed offerings — Anyscale, AWS SageMaker on Ray, GCP Dataproc — charge a markup over raw compute. For a 16-node A100 cluster running 24/7, self-managed Ray on spot instances commonly lands 60-80 percent cheaper than managed alternatives, at the cost of operational overhead.

What is the easiest way to try Ray for distributed Python?

Pip install ray, decorate a function with @ray.remote, call ray.init() and start submitting tasks. A laptop is enough to validate the API. For real workloads, KubeRay on a 3-node Kubernetes cluster is the standard staging setup.

Updated May 2026

Ray vs Alternatives 2026: Distributed ML Compute Compared

A no-marketing breakdown of Ray, Spark, Dask, vLLM, TGI, and SkyPilot for distributed training, hyperparameter tuning, batch inference, and LLM serving. Pick the right tool for the workload, not the brand.

The 60-second verdict

Training and tuning: Ray plus Ray Train and Ray Tune. Spark is a poor fit for modern PyTorch and JAX workloads.
ETL on structured data: Spark still wins on petabyte-scale Parquet and Delta Lake.
Pandas-like analytics: Dask, optionally on a Ray scheduler.
LLM serving: vLLM workers behind Ray Serve, or TGI for Hugging Face native deployments.
Multi-cloud bursting: SkyPilot on top of Ray for cheapest spot capacity.

Framework capability matrix

Each row marks first-class support, partial support, or not designed for the workload.

Framework	Distributed training	Hyperparameter tuning	Batch inference	LLM serving	Stateful actors
Ray 2.x	First-class	First-class (Ray Tune)	First-class (Ray Data)	First-class (Ray Serve)	First-class
Apache Spark 3.5	Partial (Spark MLlib, TorchDistributor)	Partial	First-class on structured data	Not designed for	Not designed for
Dask	Partial	Partial (Dask-ML)	First-class on DataFrames	Not designed for	Not designed for
vLLM 0.7	Not designed for	Not designed for	First-class for LLMs	First-class	Not designed for
TGI 2.x	Not designed for	Not designed for	Partial	First-class	Not designed for
SkyPilot	Orchestrates Ray jobs	Orchestrates Ray Tune	Orchestrates	Orchestrates vLLM	Delegates to Ray

Ray vs Spark: where each one wins

Spark and Ray solve different problems. Spark is a coarse-grained data-parallel engine; Ray is a fine-grained task and actor system. For a deeper, code-level walkthrough see our spoke post on Ray vs Spark for distributed compute.

Dimension	Ray	Apache Spark
Primary language	Python (C++ core)	JVM (Scala) with Python wrapper
Task granularity	Microsecond, fine-grained	Stage-level, coarse-grained
State	Stateful actors built in	Stateless RDD or DataFrame
GPU scheduling	First-class, fractional GPUs	Coarse via barrier mode
Best workload	RL, deep learning, LLM serving	SQL, batch ETL, Delta Lake
Typical cluster size	8 to 1,000 nodes	50 to 10,000 nodes

LLM serving throughput at a glance

Tokens per second on a single A100 80GB serving Llama 3.1 8B Instruct, 1024 input and 256 output tokens, batch size auto-tuned. Numbers from public benchmarks published by the vLLM and TGI teams, plus Anyscale internal tests.

vLLM 0.7         |##############################| 6,200 tok/s
TGI 2.4          |######################        | 4,400 tok/s
Ray Serve+vLLM   |#############################_| 6,050 tok/s
SGLang 0.4       |################################| 6,800 tok/s
Triton+TRT-LLM   |####################          | 4,000 tok/s
Naive Transformers|#####                        |   950 tok/s

Benchmark sources: vLLM project README, Hugging Face TGI blog, and the Anyscale 2025 LLM serving report.

Cost per million tokens at scale

Self-hosted on AWS p4d.24xlarge spot, 70 percent utilization, May 2026 prices. Lower is better.

Self-host vLLM on Ray  |####                | $0.18 / 1M tok
Self-host TGI on K8s   |######              | $0.27 / 1M tok
Anyscale managed Ray   |#########           | $0.41 / 1M tok
Together AI hosted     |##########          | $0.45 / 1M tok
OpenAI GPT-4o mini     |##############      | $0.60 / 1M tok
Bedrock Claude Haiku   |##################  | $0.80 / 1M tok

For a written breakdown of how to drive these numbers down further with right-sized batching, see our spoke on batch inference cost optimization.

Decision guide by workload

If you are doing	Pick	Why
Distributed PyTorch or JAX training	Ray Train	Native NCCL groups, fault-tolerant checkpointing, fractional GPU.
Hyperparameter sweeps	Ray Tune	ASHA, PBT, Optuna and HyperOpt integrations out of the box.
Petabyte ETL on Parquet or Delta	Apache Spark	Mature SQL planner, Catalyst, broadcast joins.
Pandas-style analytics that outgrew memory	Dask	DataFrame API drop-in, runs on Ray scheduler if needed.
High-throughput LLM serving	vLLM behind Ray Serve	PagedAttention plus continuous batching at the engine layer.
Hugging Face native deployments	TGI	Direct HF Hub integration, simple Docker image.
Multi-cloud spot bursting	SkyPilot on Ray	Auto-fails over to cheapest available zone.

When Ray is the wrong choice

Single-node jobs that fit in memory. Ray adds overhead. A plain multiprocessing pool or asyncio is simpler.
SQL-heavy ETL pipelines. Spark and DuckDB will outperform Ray Data on join-heavy queries.
Streaming with strict exactly-once semantics. Flink remains the reference engine. Ray streaming is best-effort.
Tiny inference services. A FastAPI plus vLLM container is enough at one or two replicas; Ray Serve adds operational surface area you do not need.

Production checklist if you pick Ray

Concern	Setting	Recommendation
Cluster manager	KubeRay operator	Use 1.2 plus, RayCluster CRD with autoscaler v2.
Object spilling	RAY_object_spilling_config	Spill to NVMe or S3 for large shuffles.
GPU sharing	num_gpus=0.5	Use fractional GPUs for inference replicas.
Observability	Ray Dashboard plus Prometheus	Scrape /metrics, ship to Grafana.
Fault tolerance	RAY_REDIS_ADDRESS external	Externalize GCS for head-node restart.

For hands-on tutorials see our Ray Python distributed tutorial and Ray example workloads.

Self-hosting and managed AI: pick per request

Many teams run Ray plus vLLM for the bulk of inference and burst into managed APIs for the long tail. Swfte Connect is the gateway in front of both: route easy traffic to your local Ray Serve cluster and overflow to OpenAI, Anthropic, or DeepSeek with one line of code, while keeping a single billing and observability surface.

Explore Swfte Connect

Continue reading

Apache Ray distributed Python

What Ray actually is, how the GCS works, and why it is not an Apache Foundation project.

Ray Tune hyperparameter guide

ASHA, PBT, BOHB, and how to wire Optuna into Ray Tune in 30 lines.

vLLM continuous batching

PagedAttention, iteration-level scheduling, and why throughput jumps 23x.

LLM serving frameworks 2026

vLLM, TGI, SGLang, TensorRT-LLM, Triton, Ray Serve compared on throughput and ops cost.