I tried the top 5 Anyscale alternatives and here’s my verdict for 2025

Let’s be upfront: Anyscale is a beast of a platform. If your team is living and breathing the Ray framework for distributed Python, it’s pretty much the go-to. It’s made by the creators of Ray, so you can bet it’s designed to scale those specific workloads better than anything else out there.

But we hit a wall. The thing is, modern AI apps are rarely just a Ray cluster. Our stack needed a REST API, a web front end, a database, and a CI/CD pipeline that didn’t feel like a side project in itself. Anyscale’s intense focus on Ray started feeling less like a specialty and more like a straitjacket. We were spending too much time wrangling infrastructure, getting nervous about vendor lock-in, and trying to guess at costs that were all over the place.

That’s what sent me down this rabbit hole. I decided to find the best Anyscale alternatives for teams like ours, teams that need more flexibility and a simpler, faster way to ship a complete AI product. Here’s what I found.

What is Anyscale and who’s it for?

Anyscale is a managed cloud platform built to take the pain out of developing and deploying apps that use Ray, an open-source framework for distributed computing.

Its main selling points are pretty clear:

• It handles the messy infrastructure parts of managing a Ray cluster, like setup and scaling.

• It’s fine-tuned for huge data processing jobs and machine learning training.

• It has a "bring your own cloud" (BYOC) option, which is a big deal for companies with strict data residency rules.

The perfect Anyscale user is an ML engineer or data science team whose main problem is scaling complicated, Ray-native Python jobs. They’re comfortable managing a fair bit of infrastructure and think in terms of distributed systems. For the rest of us, it can be overkill.

What to look for in Anyscale alternatives

When I started my search, I wasn’t just looking for another place to run Ray. I was looking for a platform that actually gets how real-world applications are put together today. This was the checklist I had in my head:

• Full-stack support: Can I run my whole application here? I’m talking APIs, front ends, and databases, all in one place alongside my AI models. I was tired of gluing five different services together to ship one product.

• Workflows that are ready for production: Does it have CI/CD, Git-based deployments, and preview environments built-in? For any modern team, these aren’t nice-to-haves, they’re essentials.

• A simpler developer experience: The goal is to ship faster, not to become a part-time DevOps engineer. An alternative should actually make my life easier.

• Pricing I can actually predict: I had to get away from confusing, usage-based compute costs. I needed a model that I could forecast without needing a crystal ball.

• A "buy vs. build" reality check: For some of our goals, like building an AI support agent, I started asking if we should even be building it from scratch. A ready-to-go solution could be a massive shortcut.

A quick comparison of the top Anyscale alternatives in 2025

Here’s a bird’s-eye view of how the contenders stack up.

Platform	Best For	Pricing Model	Full-Stack Support?	Key Limitation
eesel AI	Customer service & internal support teams (the "buy" option)	Tiered, per interaction (not per resolution)	Yes (Managed Service)	Focused on CX/ITSM use cases, not general ML
Northflank	Teams building full-stack AI products with CI/CD	Usage-based (compute + storage)	Yes	No specialized performance tuning for models
Modal	Developers needing serverless Python functions & batch jobs	Pay-per-second serverless compute	No	Limited to Python; not for always-on services
Ray on Kubernetes	Teams wanting total control over Ray deployments	Your underlying infrastructure cost	Self-managed	High operational overhead and complexity
Databricks	Enterprises needing a unified data + AI platform	Consumption-based (DBUs)	Yes (within its ecosystem)	Complex and can lead to vendor lock-in

The 5 best Anyscale alternatives we tried for building AI apps in 2025

1. eesel AI

This one was a bit of a curveball. We realized a huge reason we were looking at platforms like Anyscale was to build a custom RAG system for our support team. It turns out, eesel AI offers exactly that as a product you can get running in minutes. If what you really want is a brilliant AI agent, and not the six-month engineering project to build it, this is probably your best bet.

What I liked:

• Genuinely self-serve: I’m not exaggerating, I went from signing up to having a working AI agent trained on our help center in about 15 minutes. There were no mandatory sales demos, which was a huge relief.

• It connects to everything: It plugs right into help desks like Zendesk, wikis like Confluence, and can even learn from your past support tickets. The AI actually sounds like your team right out of the gate.

• You can test it safely: Before you let it talk to a single customer, you can run it against thousands of your old tickets. This gives you a really clear forecast of its automation rate and shows you exactly where your documentation is weak.

• You’re in control: You get a simple but powerful prompt editor to shape the AI’s personality, define what it should know, and decide exactly when it needs to hand off to a human.

What to consider:

• It’s a specialized tool for customer service, IT support, and internal knowledge bases. You wouldn’t use it to train a new language model from scratch.

Pricing:

eesel AI has clear, feature-based plans and a 7-day free trial. The best part is there are no per-resolution fees, so you don’t get penalized for the AI doing its job well. You can pay monthly and cancel whenever.

Plan	Monthly Price	Annual Price (/mo)	Key Features
Team	$299	$239	Up to 1,000 interactions/mo, 3 bots, AI Copilot for helpdesk, Slack integration.
Business	$799	$639	Up to 3,000 interactions/mo, unlimited bots, AI Agent, train on past tickets, AI Actions.
Custom	Contact Sales	Contact Sales	Unlimited interactions, advanced API actions, multi-agent orchestration.

2. Northflank

Northflank is probably the most direct answer for any team feeling stuck in Anyscale’s world. It’s designed to run entire full-stack applications where a Ray cluster is just one component, not the center of the universe.

What I liked:

• You can run your Ray services, FastAPI backends, and React frontends together on one platform. No more duct tape.

• It has Git-based CI/CD and preview environments built right in, which is a massive feature that Anyscale is missing.

• It handles GPUs well and supports "Bring Your Own Cloud" (BYOC), which is great for anyone in a data-sensitive industry.

What to consider:

• Because it’s a general-purpose platform, you don’t get the kind of deep, Ray-specific performance tuning that Anyscale offers.

Pricing:

Northflank has a straightforward, pay-as-you-go model for compute and resources that’s easy to understand. There’s also a free tier for getting your feet wet.

Resource	Example Pricing
Compute Plan (nf-compute-50)	$12.00 / month (0.5 shared vCPU, 1024 MB RAM)
NVIDIA A100 GPU (40GB)	$1.42 / hour
NVIDIA H100 GPU	$2.74 / hour
Networking Egress	$0.15 / GB

3. Modal

Modal is built for the developer who loves writing Python and hates writing YAML. It approaches AI workloads with a serverless, function-as-a-service mindset. If your work involves running batch jobs, data processing scripts, or simple inference APIs instead of managing persistent clusters, Modal is a dream.

What I liked:

• The developer experience is incredibly smooth and feels native to Python. You just add a decorator to a function, and poof, it’s deployed.

• It scales from zero to thousands of containers in seconds, which makes it super affordable for bursty or infrequent jobs.

• It takes care of all the annoying containerization and dependency management behind the scenes.

What to consider:

• It’s not designed for always-on services or full-stack apps that have front ends and databases.

• It’s Python-only, while other platforms let you use whatever language you want.

Pricing:

Modal’s pay-per-second serverless model makes it very cheap for workloads that aren’t running 24/7.

Plan	Monthly Fee	Included Compute	Key Features
Starter	$0	$30 / month credit	Up to 3 seats, 100 containers, limited crons.
Team	$250	$100 / month credit	Unlimited seats, 1000 containers, unlimited crons, custom domains.
Enterprise	Custom	Custom	Volume-based pricing, private Slack support, SSO, HIPAA.

GPU compute is billed by the second, so an NVIDIA A100 (80GB) comes out to around $0.000694/sec.

4. Ray on Kubernetes

For teams with a strong MLOps culture, running Ray on your own Kubernetes cluster (usually with the KubeRay operator) gives you the ultimate control. Going this route means you avoid vendor lock-in completely and can fine-tune every part of your deployment on any cloud or even on-prem hardware.

What I liked:

• You have total control over your infrastructure, software versions, and security.

• There are no platform fees. You just pay for the underlying compute from your cloud provider.

• It’s completely portable. You can move your setup to any Kubernetes environment.

What to consider:

• The operational overhead is huge. You’re suddenly on the hook for everything: cluster setup, autoscaling, monitoring, security patches, and upgrades.

• This is not a side project. It requires a dedicated and experienced DevOps or MLOps team to manage well.

Pricing:

• The cost is whatever your cloud instances run you (like EC2 or GKE), plus any networking and storage fees. It’s completely up to your usage and provider.

5. Databricks

If Anyscale is a specialized tool, Databricks is the entire workshop. It’s the 800-pound gorilla in the enterprise data and AI space. Databricks provides an end-to-end platform that covers the entire data lifecycle, from data engineering with Spark to model training and serving. It’s the right choice for big organizations that want to consolidate their entire data stack in one place.

What I liked:

• It’s a unified platform that can shrink the number of tools your data team has to juggle.

• It has strong, enterprise-level features for governance, security, and collaboration.

• It’s excellent for large-scale data processing and more traditional ML workloads.

What to consider:

• It can be incredibly complex and expensive. The pricing model, based on "Databricks Units" (DBUs), is famously hard to predict.

• Moving to Databricks is a major commitment and can lock you into their ecosystem pretty deeply.

Pricing:

Databricks uses a complicated, consumption-based model that bills per "Databricks Unit" (DBU), and the cost of a DBU changes depending on the workload and cloud provider.

Workload	Starting Price (per DBU)
Data Engineering	$0.15
Data Warehousing	$0.22
Interactive Workloads (Data Science)	$0.40
Artificial Intelligence (Model Serving)	$0.07

How to choose the right Anyscale alternatives for you

For us, the decision came down to one simple question: Are you trying to build a platform or a product?

• Choose a managed solution like eesel AI if: Your main goal is to solve a business problem, like automating customer support. Building all the infrastructure from scratch is just a distraction. You care more about getting to market quickly and having predictable costs than you do about micromanaging compute instances.

• Choose a full-stack platform like Northflank if: Your core product is a custom AI application with different moving parts. You need a real CI/CD pipeline and a smooth developer experience for your entire team.

• Choose a serverless tool like Modal if: Your work is mostly running on-demand Python scripts, batch jobs, or simple inference APIs, and you’d rather not think about servers at all.

• Choose a self-hosted or enterprise platform like Ray on K8s or Databricks if: You have a dedicated MLOps team, strict governance requirements, and you need absolute control over your entire infrastructure.

This demo shows how to build and scale an LLM-based copilot, providing context for teams considering different Anyscale alternatives.

My takeaway on Anyscale alternatives: Look beyond the framework

Anyscale is a great platform for a very specific task: scaling Ray applications. But what we found is that most teams have needs that are much bigger than that. The best Anyscale alternatives aren’t just other ways to run Ray; they’re platforms that are better aligned with the actual goal of shipping real-world AI products.

The choice really depends on what you’re trying to get done. For teams looking to use AI for customer service or internal support, that "build vs. buy" decision is everything. A specialized solution can save you months of engineering headaches and get you a better result, faster.

Ready to skip the infrastructure headache and launch a powerful AI agent for your support team? eesel AI can get you live in minutes, not months. Try it for free or book a quick demo to see how it works.