Top Anyscale alternatives for AI/ML model deployment
You chose Anyscale because you wanted to scale Python, not become a distributed systems engineer.
And for a while, it worked.
You could deploy Ray Serve DAGs in minutes, scale actors across GPUs, and skip the Kubernetes rabbit hole entirely.
But now the cracks are showing.
- Want to run a FastAPI service next to your Ray cluster? Too bad.
- Need better logs, metrics, or runtime debugging? Good luck.
- Trying to control cloud costs, reuse GPUs, or add CI/CD? Not built in.
Anyscale is great if your entire stack lives inside Ray. But the moment you step outside that boundary, even slightly, the abstractions start to fight you.
If you’ve hit that wall, you’re not alone.
In this guide, we’ll break down the best Anyscale alternatives for teams running LLMs, Ray pipelines, and real-time inference at scale without giving up control, observability, or flexibility.
You’ll learn:
- What Anyscale gets right (and where it breaks down)
- What features truly matter when replacing it
- And which platforms actually support modern AI workloads and not just Ray
If you're short on time, here’s a snapshot of the top Anyscale alternatives. Each tool has its strengths, but they solve different problems, and some are better suited for real-world production than others.
| Platform | Best For | Notes |
|---|---|---|
| Northflank | Full-stack ML apps with Ray, APIs, GPUs, and CI/CD | Run Ray clusters, inference jobs, REST APIs, and web services in one platform container-native and GPU-ready |
| Ray OSS (self-managed) | Full control of Ray on your infra | Kubernetes, Docker, or VM-based Ray clusters |
| Modal | Function-as-a-service with Python and simple parallelism | Doesn’t use Ray, but great for async parallel compute |
| RunPod | Cheap, custom GPU workloads with full Docker control | Great for teams that want to run Ray manually at low cost |
| AWS SageMaker | End-to-end enterprise ML workflows | Doesn’t support Ray natively, but comparable for some use cases |
| Vertex AI | AutoML and pipelines for GCP-native stacks | For teams already on GCP looking to replicate training/inference pipelines |
Looking for a single platform to run Ray, APIs, CI/CD, and GPU workloads all in one? Northflank is the only one that covers the full stack, not just training scripts.
Let's give Anyscale its props. The creators of Ray, which is a distributed computing framework for scaling Python applications, built Anyscale. It’s often the top choice for teams who want the power of Ray without the infrastructure headache. Here’s why it stands out:
You don’t need to configure EC2, Kubernetes, or networking. Anyscale handles provisioning, scaling, and the entire cluster lifecycle so teams can stay focused on building.
It makes deploying complex inference pipelines simple. You can route traffic between models, scale components independently, and keep boilerplate code to a minimum.
Anyscale supports hot-start LLMs and actor-based serving, making it ideal for use cases like prompt-based inference, retrieval-augmented generation, and agent systems.
For teams with security, compliance, or data residency requirements, Anyscale supports deployments on your own cloud account, so you can keep control without losing the platform benefits.
Multiple users can manage jobs, environments, and projects through a shared dashboard that supports real-time coordination.
In short, Anyscale is a great choice for teams that are all-in on Ray and want a smooth path to production without managing infrastructure.
That said, not every team wants to be tightly coupled to Ray or locked into a single platform. Let’s explore some alternatives.
Anyscale takes a lot off your plate, but it doesn’t solve everything. As your team matures and your needs grow, some sharp edges start to show.
If it doesn’t fit into the Ray runtime, it doesn’t fit into Anyscale. That means your APIs, frontends, cron jobs, and anything not actor-based need to live somewhere else. You end up stitching together multiple platforms to ship one product.
Platforms like Northflank solve this by running Ray and non-Ray services side by side from APIs and inference pipelines to UIs and batch jobs on the same stack.
Distributed systems break in weird ways. But when actors silently crash or memory runs out across nodes, the Anyscale UI often leaves you guessing. You spend hours digging through logs just to figure out what went wrong.
It helps to have unified logs, metrics, and tracing across both Ray and non-Ray workloads. Northflank, for example, ships this out-of-the-box, so debugging doesn’t require jumping between systems.
There’s no native support for pull request previews, deploy pipelines, or automatic rollbacks. You have to script everything manually, which defeats the simplicity promise that drew you to the platform in the first place.
By contrast, platforms like Northflank offer Git-based workflows, automated deploy previews, and rollback-safe promotions, with no glue code or custom CI needed.
You don’t always know how pricing scales with usage. You spin up a few extra nodes, maybe run a large job overnight, and suddenly the bill spikes. Without fine-grained cost visibility, budgeting becomes guesswork.
Anyscale is built for trusted team environments, but it doesn’t offer secure runtime isolation for executing untrusted or third-party code. There’s no built-in sandboxing, syscall filtering, or container-level hardening. If you're running workloads from different tenants or just want extra guarantees around runtime isolation, you’ll need to engineer those protections yourself.
By contrast, Northflank containers run in secure, hardened sandboxes with configurable network and resource isolation, making it easier to host untrusted or multitenant workloads out of the box safely.
When replacing Anyscale, you're not just looking for a generic hosting platform; you need something that:
If you're using Ray, you need control over worker orchestration, memory settings, actor lifecycles, and scaling rules. For example, Northflank supports this while giving you flexibility to mix in other runtimes.
You should be able to bring your own Ray version, Torch stack, and Python environment, no one-size-fits-all runtime. Container-native platforms like Northflank make this straightforward by letting you build from any Dockerfile or image.
Especially for LLMs, you need GPU affinity, persistent containers, and options for model sharding or batching. Look for platforms that support fine-grained GPU allocation and persistent deployment patterns. Northflank includes built-in support for GPU scheduling and scale-to-zero for idle inference endpoints.
You want repeatable, automated deploys from GitHub, with promotion flows and rollback. Northflank tightly integrates with Git, enabling pull request previews, automatic deploys, and promotion between environments without external CI scripting.
If you're building full-stack AI products with APIs, UIs, and agents, you need to orchestrate more than just Ray clusters. Northflank is designed to run sidecar services, background workers, and scheduled jobs alongside Ray, on a unified networking layer.
If you’re running AI agents, plugins, or user-submitted code, you need runtime isolation. Look for container sandboxing, syscall filtering, and strict resource boundaries features that Northflank includes by default, with hardened multi-tenant security policies.
Here is a list of the best Anyscale alternatives you can find. In this section, we talk about each platform in depth, its top features, Pros, and Cons.
Northflank isn’t just a model hosting tool; it’s a production-grade platform for deploying and scaling real AI products. It combines the flexibility of containerized infrastructure with GPU orchestration, Git-based CI/CD, and full-stack app support.
Whether you're serving a fine-tuned LLM, hosting a Jupyter notebook, or deploying a full product with both frontend and backend, Northflank gives you everything you need, with none of the platform lock-in.
Key features:
- Bring your own Docker image and full runtime control
- GPU-enabled services with autoscaling and lifecycle management
- Multi-cloud and Bring Your Own Cloud (BYOC) support
- Git-based CI/CD, preview environments, and full-stack deployment
- Secure runtime for untrusted AI workloads
- SOC 2 readiness and enterprise security (RBAC, SAML, audit logs)
Pros:
- No platform lock-in – full container control with BYOC or managed infrastructure
- Transparent, predictable pricing – usage-based and easy to forecast at scale
- Great developer experience – Git-based deploys, CI/CD, preview environments
- Optimized for latency-sensitive workloads – fast startup, GPU autoscaling, low-latency networking
- Supports AI-specific workloads – Ray, LLMs, Jupyter, fine-tuning, inference APIs
- Built-in cost management – real-time usage tracking, budget caps, and optimization tools
Cons:
- No special infrastructure tuning for model performance.
Verdict: If you're building real AI products, not just prototypes, Northflank gives you the flexibility to run anything from Ray clusters to full-stack apps in one place. With built-in CI/CD, GPU orchestration, and secure multi-cloud support, it's the only platform designed for teams who need speed and control without getting locked in.
Ray OSS gives you full control of the Ray ecosystem without Anyscale. Great for teams that want flexibility and are comfortable managing infra.
Key features:
- Native support for training, tuning, and serving
- Works on Kubernetes, EC2, Northflank, or bare-metal
- Integrates with MLflow, Prometheus, and W&B
Pros:
- Full flexibility and no lock-in
- Scalable and production-capable
- Rich ecosystem of AI tools
Cons:
- Infra setup required
- No built-in CI/CD or frontend support
- Steeper learning curve
Verdict:
Powerful option for infra-savvy teams. Production-ready, but high effort to maintain.
Modal makes Python deployment effortless. Just write Python code, and it handles scaling, packaging, and serving — perfect for workflows and batch jobs.
Key features:
- Python-native infrastructure
- Serverless GPU and CPU runtimes
- Auto-scaling and scale-to-zero
- Built-in task orchestration
Pros:
- Super simple for Python developers
- Ideal for workflows and jobs
- Fast to iterate and deploy
Cons:
- Limited runtime customization
- Not designed for full-stack apps or frontend support
- Pricing grows with always-on usage
Verdict:
A great choice for async Python tasks and lightweight inference. Less suited for full production systems.
RunPod gives you raw access to GPU compute with full Docker control. Great for cost-sensitive teams running custom inference workloads.
Key features:
- GPU server marketplace
- BYO Docker containers
- REST APIs and volumes
- Real-time and batch options
Pros:
- Lowest GPU cost per hour
- Full control of runtime
- Good for experiments or heavy inference
Cons:
- No CI/CD or Git integration
- Lacks frontend or full-stack support
- Manual infra setup required
Verdict:
Great if you want cheap GPU power and don’t mind handling infra yourself. Not plug-and-play.
SageMaker is Amazon’s heavyweight MLOps platform, covering everything from training to deployment, pipelines, and monitoring.
Key features:
- End-to-end ML lifecycle
- AutoML, tuning, and pipelines
- Deep AWS integration (IAM, VPC, etc.)
- Managed endpoints and batch jobs
Pros:
- Enterprise-grade compliance
- Mature ecosystem
- Powerful if you’re already on AWS
Cons:
- Complex to set up and manage
- Pricing can spiral
- Heavy DevOps lift
Verdict:
Ideal for large orgs with AWS infra and compliance needs. Overkill for smaller teams or solo devs.
Vertex AI is Google Cloud’s managed ML platform for training, tuning, and deploying models at scale.
Key features:
- AutoML and custom model support
- Built-in pipelines and notebooks
- Tight GCP integration (BigQuery, GCS, etc.)
Pros:
- Easy to scale with managed services
- Enterprise security and IAM
- Great for GCP-based teams
Cons:
- Locked into the GCP ecosystem
- Pricing can be unpredictable
- Less flexible for hybrid/cloud-native setups
Verdict:
Best for GCP users who want a full-featured ML platform without managing infra.
| What You Need | Best Fit | Why It Works |
|---|---|---|
| Ray + APIs + CI/CD + GPU in one place | Northflank | Run Ray, FastAPI, LLMs, and batch jobs side by side with Git-based deploys |
| Total control over Ray and infra | Ray OSS | Full flexibility, but high DevOps overhead |
| Fastest path to deploy async Python | Modal | Simple, serverless compute for Python workflows |
| Raw GPU power on a budget | RunPod | Cheapest GPUs with full container control |
| Deep enterprise cloud integration | SageMaker or Vertex AI | Great if you're already locked into AWS |
Most platforms force you into trade-offs. Anyscale locks you into Ray. Modal strips out customization. RunPod leaves you wiring everything together by hand.
Northflank is different. It gives you full control without the platform baggage, whether you’re serving LLMs, running Ray jobs, or deploying full-stack apps.
Only Northflank lets you:
- Run Ray and non-Ray workloads together, inference APIs, async jobs, web apps, and agents in one place
- Use Git-based CI/CD with PR previews, auto-deploys, and rollback workflows
- Deploy to your cloud, your way, BYOC with full container-level control
- Get built-in GPU autoscaling and cost tracking so usage never surprises you
- Move from prototype to production without switching platforms or re-architecting
If you’re hitting the limits of Anyscale or stitching together half a dozen tools just to ship, it’s time for a better foundation.
Northflank is built for real ML products, not just demos. Start for free and scale when you're ready.
Anyscale is a solid choice for teams who are fully bought into the Ray ecosystem, but it’s not the only way to run distributed ML workloads, and for many teams, it’s not the best long-term fit.
Whether you're scaling LLM inference, orchestrating batch jobs, or building full-stack AI products, platforms like Northflank offer more control, broader runtime support, and better observability without sacrificing simplicity.
Modern ML infra should be composable, transparent, and infra-agnostic.
And it’s finally possible to build that without locking into a single runtime.
Deploy your ML workloads with real CI/CD, BYOC, and GPU auto-scaling on Northflank. Start free and scale when you're ready.
Not exactly, but it’s a close comparison. Coiled is the managed cloud platform for Dask, just like Anyscale is for Ray. Both help you scale Python workloads without managing infrastructure, but they support different underlying ecosystems.
- Choose Dask + Coiled if your workloads involve dataframes, ETL pipelines, or heavy pandas usage.
- Choose Ray + Anyscale for LLM inference, reinforcement learning, or actor-based parallel compute.
Dask is designed around dataframe-like operations. Ray is more task-oriented and better suited for modern AI workloads.
Yes. Both are open source and fully self-hostable. However, setting up production-grade clusters, handling autoscaling, and integrating CI/CD can be complex. This is where platforms like Anyscale, Coiled, or Northflank add real value.
Northflank is a strong alternative. It lets you run open-source Ray, custom APIs, CI/CD pipelines, and GPU workloads in one place without being locked into a single runtime or cloud vendor.
