Top 6 Modular AI alternatives for different needs in 2025

There’s a ton of chatter around Modular AI, and it makes sense. They’re taking on the massive challenge of rebuilding the entire AI software stack to compete with NVIDIA’s CUDA ecosystem. It’s a seriously impressive feat of engineering for anyone working deep in AI development.

But let’s be real: the "best" AI platform is a bit of a myth. It all comes down to what you’re actually trying to do. Are you a systems programmer looking to build the next foundational model from the ground up, or are you trying to use AI to solve a business problem that’s staring you in the face right now?

That’s exactly why I put this list together. We’re going to walk through the top Modular AI alternatives and other powerful AI platforms out there in 2025. Each one is designed for a different job, from the raw infrastructure that gives developers total control, to ready-to-go solutions that can deliver real business value in a matter of minutes.

What is Modular AI?

Before we jump into the alternatives, let’s quickly get on the same page about what Modular AI actually is. In a nutshell, Modular is building a new foundation for AI development that isn’t tied to any specific hardware.

It has a couple of key parts:

• Mojo: A new programming language that aims to give you the easy syntax of Python with the performance of something hardcore like C++ or Rust.

• MAX Platform: A unified AI engine designed for running trained models (inference) incredibly quickly and efficiently.

• The Goal: Modular’s big ambition is to free the AI world from being locked into one company’s hardware and software, namely NVIDIA’s CUDA. The idea is to write your code once and have it run at peak performance on any GPU or AI chip, whether it’s from NVIDIA, AMD, or another player.

It’s a huge project aimed at AI researchers, systems programmers, and MLOps engineers, the folks who need to get their hands on the nuts and bolts of AI systems.

This video provides a first look at Mojo, one of the core components of Modular AI's platform and a fast alternative to Python.

How I picked these alternatives

Comparing other platforms to Modular isn’t straightforward because it operates at such a deep, fundamental level of the AI stack. So, instead of trying to find direct copies, I’ve evaluated platforms based on what they help you achieve.

Here’s what I considered for each one:

• Primary Use Case: What problem is it really built to solve? Is it for training models, running them, managing the whole machine learning process (MLOps), or automating a business function?

• Target Audience: Who’s it for? AI researchers, data scientists, DevOps engineers, or business teams who wouldn’t know where to start with code?

• Ease of Implementation: How much of a headache is it to get started and see a real return on your time?

• Key Differentiator: What’s its secret sauce? Is it that it works on any hardware, its no-code builder, or just how ridiculously simple it is to set up?

A quick comparison of the best alternatives

Here’s a quick-glance table to see how these tools stack up against each other before we get into the gritty.

Feature	eesel AI	Vertex AI	RunPod	Together AI	OctoAI	NVIDIA Triton
Primary Use Case	Customer Service & ITSM Automation	End-to-end MLOps	GPU Cloud Infrastructure	Open-Source Model Inference	GenAI Inference Platform	Inference Serving
Target Audience	Support & IT Teams	Data Scientists, MLOps	Developers, Researchers	Developers	Enterprise Developers	MLOps Engineers
Ease of Use	Radically Self-Serve	Requires Expertise	Developer-Focused	API-based	Requires Expertise	Requires Expertise
Key Differentiator	Go live in minutes, trains on past tickets	Fully managed Google Cloud suite	Low-cost GPU rental	Optimized open-source models	Acquired by NVIDIA	High-performance, multi-framework
Pricing Model	Tiered (not per-resolution)	Pay-as-you-go	Per-hour GPU rental	Per-token	Enterprise	Open Source (Free)

The top 6 Modular AI alternatives for developers and businesses

This list covers a lot of ground, from tools that let you tackle business challenges without writing a single line of code to pure infrastructure that gives developers all the control they could want.

1. eesel AI

While Modular is focused on building the AI engine, eesel AI hands you a finished product that’s ready to solve a very specific, high-value problem: automating customer service and internal support. Instead of asking you to write low-level code, eesel AI connects directly to your existing help desks (like Zendesk or Freshdesk) and knowledge bases (like Confluence, Google Docs, and even your past tickets) to start automating support right away.

A look at the eesel AI dashboard, showcasing how it integrates with various help desks and knowledge bases to automate support. Among Modular AI alternatives, this platform is focused on immediate application.::

• Pros:

  • Go live in minutes, not months: It’s genuinely self-serve. You can connect your help desk, train the AI, and deploy an agent without ever needing to talk to a salesperson.

  • Fits right in: It integrates with the tools you’re already using, so you don’t have to overhaul your current workflows.

  • Trains on your actual data: It learns from thousands of your past support chats and tickets to automatically match your brand’s voice and accurately solve issues from day one.

  • You’re in control: The prompt editor and custom actions let you set clear rules for what the AI should and shouldn’t do, making sure it stays on-brand and knows when to pass a tricky issue to a human.

• Cons:

  • It’s not a tool for building your own AI models from the ground up.

  • It’s hyper-focused on customer service, ITSM, and internal help desk use cases.

• Pricing: eesel AI has transparent, tiered plans that don’t charge per ticket resolved, so your bill won’t suddenly jump during a busy month.

  • Team Plan: $239/month (billed annually) for up to 1,000 AI interactions.

  • Business Plan: $639/month (billed annually) for up to 3,000 AI interactions, plus advanced features like training on past tickets and AI actions.

  • Custom Plan: For unlimited interactions and other enterprise needs.

• Why it’s on the list: This is for teams that want the benefits of generative AI without the massive engineering headache. If your goal is to cut down on ticket volume or help your support agents work more efficiently, eesel AI is a much faster and more direct way to get there than building a custom solution on a low-level platform.

This workflow illustrates the simple, self-serve implementation process of eesel AI, a key feature for businesses looking at Modular AI alternatives for rapid deployment.::

2. Google Vertex AI

Vertex AI is Google’s massive, unified platform for the entire machine learning lifecycle. It gives you tools for everything from data prep and model building to training, deployment, and monitoring. In a way, it competes with Modular’s vision of a complete stack, but it keeps you firmly inside the Google Cloud world.

• Pros:

  • A massive suite of tools covering every stage of the ML workflow.

  • Plays nicely with other Google Cloud services like BigQuery and Cloud Storage.

  • Offers scalable infrastructure for training and serving really big models.

• Cons:

  • It pretty much locks you into the Google Cloud ecosystem.

  • The huge number of features can be overwhelming, especially for smaller teams.

  • The pricing is famously complex and can be incredibly difficult to predict.

• Pricing: Vertex AI uses a pay-as-you-go model where you get billed for usage across dozens of different services. For example, generating text with one model costs a fraction of a cent per 1,000 characters, while training a custom model on a specific machine costs about $0.22 per hour, plus extra charges for any GPUs you use. This level of detail makes it really tough to forecast your costs.

• Why it’s on the list: For teams already deep into Google Cloud, it’s one of the most mature and complete Modular AI alternatives if you need a fully managed MLOps platform.

3. RunPod

RunPod is a cloud platform that gives you access to on-demand GPU instances without the sticker shock of the major cloud providers. It’s for developers who need to deploy and scale AI workloads on a budget. You can think of it as an alternative to the raw infrastructure layer that a platform like Modular would sit on top of.

• Pros:

  • Way cheaper for GPU power than AWS, GCP, or Azure.

  • A massive selection of NVIDIA GPUs, from consumer cards like the RTX 4090 to data center beasts like the H100.

  • Has a Serverless option for inference, which makes deployment simpler for workloads that come in bursts.

• Cons:

  • It’s more of an infrastructure provider than an all-in-one platform.

  • You’ll need some solid DevOps and MLOps skills to manage everything yourself.

• Pricing: RunPod’s pricing is billed by the hour based on the GPU you pick, and the rates are great. For example, you can rent an NVIDIA RTX A6000 for as little as $0.79/hour. Their Serverless option charges by the second, with rates like $0.00076/second for an A100 GPU.

• Why it’s on the list: It’s a great choice for developers and startups who need raw, affordable GPU power for training or running models and don’t mind managing the software stack themselves.

4. Together AI

Together AI offers a super-fast cloud platform made specifically for running, fine-tuning, and training open-source generative AI models. It provides a simple API for inference that’s often faster and cheaper than trying to host the models on your own.

• Pros:

  • Highly optimized for top open-source models like Llama 3.1 and Mixtral.

  • A simple, developer-friendly API that’s compatible with OpenAI’s standards.

  • Often delivers better performance and lower inference costs compared to other providers.

• Cons:

  • Mainly focused on serving existing models, not building new model architectures from scratch.

  • You have less direct control over the hardware compared to a platform like RunPod.

• Pricing: Together AI uses a pay-per-token model. For instance, running Llama 3.1 70B Instruct Turbo costs $0.88 per million tokens. Fine-tuning is also priced per token, which keeps things predictable.

• Why it’s on the list: It’s an excellent option for developers who want to tap into the best open-source models through a simple, high-performance API and skip the infrastructure headaches.

5. OctoAI

OctoAI, which was recently acquired by NVIDIA, offered a high-performance service for running, tuning, and scaling generative AI models. It was built by the original creators of Apache TVM, a popular open-source machine learning compiler, so the engineering behind it was top-notch.

• Pros:

  • Engineered for fast, high-volume inference at scale.

  • Had a strong focus on enterprise-level performance and reliability.

  • Supported a wide range of popular generative AI models.

• Cons:

  • The NVIDIA acquisition throws its future as a multi-vendor platform into question. Its tech is now being folded into NVIDIA’s own software.

  • It was always less focused on the model development and training side of things.

• Pricing: Before the acquisition, OctoAI had a per-token or per-second pricing model. Now, its technology and pricing will likely be rolled into NVIDIA’s broader enterprise packages, which aren’t publicly listed.

• Why it’s on the list: It represents a successful take on solving the AI inference puzzle, making it a relevant conceptual competitor to Modular’s MAX engine, even though it’s no longer an independent company.

6. NVIDIA Triton Inference Server

NVIDIA Triton is an open-source inference server that lets teams deploy trained AI models from just about any framework (TensorFlow, PyTorch, TensorRT, you name it) on any GPU- or CPU-based setup.

• Pros:

  • It’s completely open-source and free to use.

  • Supports running multiple models at once, which helps you get the most out of your hardware.

  • Incredibly flexible and works with almost every major ML framework.

• Cons:

  • It takes a good amount of technical skill to set up, configure, and manage properly.

  • While it can run on CPUs, it’s, not surprisingly, heavily optimized for NVIDIA’s hardware.

• Pricing: The software itself is free. But if you want enterprise-level support, security, and management tools, you’ll need the paid NVIDIA AI Enterprise software suite, and for that, you’ll have to talk to their sales team.

• Why it’s on the list: For teams that want total control and are comfortable managing their own infrastructure, Triton is a powerful, direct alternative to the serving part of Modular’s stack.

How to choose the right AI platform

Feeling a bit lost in all the options? That’s totally normal. Here’s a simpler way to break it down.

• Start with your end goal. What are you actually trying to achieve? If you’re doing deep R&D on new AI architectures, then a platform like Modular or raw infrastructure from RunPod is your best bet. But if you’re trying to solve a business problem today, a solution like eesel AI delivers value almost immediately. It hides all the underlying complexity so you can focus on results, like how to cut down on ticket volume.

• Look at your team’s skills. Do you have a crew of MLOps engineers who live and breathe infrastructure management? If not, a fully managed platform like Vertex AI or an application-focused tool like eesel AI will save you a world of trouble.

• Think about vendor lock-in. Modular’s whole reason for being is to break free from vendor lock-in. If that’s important to you, be cautious of single-vendor ecosystems. On the flip side, if you’re already all-in on a cloud provider, their native solution might be the easiest path forward.

• Don’t overlook the user experience. For any AI tool that your team has to use, the setup process matters. A lot. Platforms like eesel AI are built to be radically self-serve. That means you can sign up, connect your tools, and see it working without having to sit through a sales call or mandatory demo, which can be a huge bottleneck with other enterprise AI platforms.

From building the engine to driving the car

The AI world is massive, and "Modular AI alternatives" can mean very different things to different people. The right platform for you boils down to where in the AI stack you want to spend your time.

Modular is for the brilliant folks building the AI "engine," a powerful but incredibly difficult job. Platforms like Vertex AI give you a whole garage of MLOps tools, while infrastructure providers like RunPod just sell you the raw horsepower.

But for most businesses, the goal isn’t to build an engine; it’s to get from point A to point B. That’s where a solution like eesel AI fits in. It gives you the fastest, most direct path to getting real results, like automated ticket resolutions and happier customers.

Ready to see how easy it can be to put AI to work for your support team? Get started with eesel AI for free, and you can have your first AI agent live in under five minutes.