.png)
Serving machine learning models efficiently at scale has become one of the biggest challenges in the MLOps lifecycle. As machine learning workloads grow increasingly complex, with multiple models, asynchronous workflows, real-time inference requirements, and hybrid cloud deployment scenarios, there’s an urgent need for a serving solution that is scalable, flexible, and developer-friendly.
This is where Ray Serve shines. Built on top of the Ray ecosystem, Ray Serve is a high-performance, Python-native model serving framework that simplifies the process of turning ML models into production APIs. Whether you’re deploying a single model, a chain of models, or a multi-tenant ML service architecture, Ray Serve offers a scalable, unified, and composable solution to help developers serve machine learning models effortlessly.
In this detailed guide, we’ll walk through:
Whether you're a machine learning engineer, backend developer, or MLOps practitioner, this is your ultimate guide to getting started with Ray Serve for high-performance model serving.
One of Ray Serve’s most compelling features is that it is designed entirely in Python. You don’t have to write separate model server code in C++ or rely on Java wrappers. Instead, you simply define a Python class, decorate it with @serve.deployment, and deploy it. Everything from orchestration to autoscaling is handled behind the scenes.
This makes it incredibly easy to get started, especially for data scientists and ML engineers who are already familiar with Python. With just a few lines of code, you can deploy a model and expose it as a REST endpoint or gRPC service.
Ray Serve is framework-agnostic, which means you can deploy models written in:
You’re not locked into a specific ecosystem. This is particularly beneficial in real-world environments where teams often work with heterogeneous stacks. Ray Serve supports them all, unifying them into a consistent deployment and management interface.
Modern machine learning systems rarely involve just one model. More often than not, they are pipelines consisting of multiple models or services that need to work in tandem.
For example, you might have:
Ray Serve allows you to compose these individual components into a unified service. Each one can be deployed as a separate Ray Serve deployment, and you can chain them using deployment handles, all with minimal code overhead.
Ray Serve can be deployed in any environment, locally on your laptop, on cloud VMs, or on Kubernetes clusters using KubeRay. For large-scale enterprise deployments, you can also use Anyscale, the managed platform for Ray.
This makes Ray Serve uniquely flexible: you can prototype locally and scale seamlessly to cloud environments without changing your codebase. It’s built for portability.
At its core, Ray Serve is an actor-based serving framework, which means each deployment is backed by a Ray actor that can be scaled horizontally.
You can:
These built-in capabilities make Ray Serve not only developer-friendly but also production-ready out of the box.
To get started, install Ray Serve and the necessary libraries:
pip install "ray[serve]" transformers torch requests
Start a Ray cluster locally:
import ray
ray.init()
Then import the necessary modules:
from ray import serve
from transformers import pipeline
from starlette.requests import Request
Here’s an example of a simple translation service using a HuggingFace t5-small model:
@serve.deployment(num_replicas=2, ray_actor_options={"num_cpus": 0.2, "num_gpus": 0})
class Translator:
def __init__(self):
self.model = pipeline("translation_en_to_fr",model="t5-small")
async def __call__(self, request: Request):
data = await request.json()
text = data.get("text", "")
result = self.model(text)
return result[0]["translation_text"]
serve.start()
Translator.deploy()
Your model is now running at http://localhost:8000/Translator. You can send a POST request with a JSON payload to get translated results in real-time.
This end-to-end deployment takes fewer than 20 lines of code. That’s the power of Ray Serve.
Ray Serve allows you to configure autoscaling for each deployment by setting num_replicas="auto" and defining an autoscaling_config.
You can specify:
This makes your service elastic, scaling up under load and scaling down during idle times, without manual intervention.
Ray Serve includes built-in support for request batching, which is especially useful for deep learning models that perform better with batched inputs.
By using @serve.batch, you can enable this mode and configure:
This ensures you can handle high throughput with low latency, especially for GPU-bound workloads.
With Ray Serve, you can assign fractional GPU or CPU resources to each deployment. For instance, you can run four lightweight models on a single GPU by assigning each deployment num_gpus=0.25.
This increases resource utilization and makes it possible to run multiple models on the same node.
As mentioned earlier, Ray Serve makes it easy to chain multiple models together into a single pipeline.
You can use deployment handles like this:
handle = serve.get_deployment("MyOtherModel").get_handle(sync=True)
result = await handle.remote(input_data)
Each service can be scaled independently, making your pipeline modular, maintainable, and scalable.
Ray Serve exposes detailed metrics and traces to help monitor service health:
You can integrate these metrics with Prometheus, Grafana, or use Ray Dashboard for a visual overview.
Always write async methods (async def __call__) to support concurrent request handling within each replica.
Test different max_batch_size and batch_wait_timeout_s values to find the best tradeoff between latency and throughput.
Use Ray Dashboard or Prometheus to monitor real-time metrics. Use this data to adjust replica counts or batch sizes as needed.
You can use serve.run() or serve build and serve deploy to run your applications directly on virtual machines or dedicated servers.
For enterprise-grade deployments, KubeRay allows you to run Ray Serve on Kubernetes. It provides:
Use RayService custom resource definitions (CRDs) to define production-grade serving clusters.
Anyscale is the enterprise Ray platform that abstracts away infrastructure. It provides features like:
Klaviyo uses Ray Serve to run its real-time inference system for customer segmentation and recommendations. With Ray Serve, they built a fully dynamic runtime that supports multi-tenant model execution with sub-second latency.
Several startups use Ray Serve to host LLM-based services (e.g., summarization, code generation, translation) where latency and composability are critical. Ray Serve helps them chain multiple models while handling burst traffic using autoscaling.
Ray Serve is used in high-resolution image processing tasks, such as object detection and medical image segmentation, where fractional GPU usage and batching are vital to keep inference times low and cost predictable.
Ray Serve is more than just a model server. It’s a developer-friendly, Python-first system for building, scaling, and maintaining machine learning services at production scale.
Its composability, built-in autoscaling, support for batching and GPU optimization, and ability to work across environments make it a perfect choice for any ML engineer looking to scale their model serving architecture without the overhead of managing multiple systems.
Whether you're building a small project or a massive enterprise-grade ML platform, Ray Serve enables rapid iteration, seamless scaling, and total flexibility.
.png)
