Top 7 Together AI alternatives for deploying AI in 2025

Platforms like Together AI are a playground for developers and machine learning engineers. They hand you the keys to a high-performance engine, let you tinker with open-source models, and basically say, "Go build something amazing."

And that’s great. But what if your goal isn’t just to build something amazing, but to solve a pressing business problem, like automating your customer support? What if you don’t have an in-house ML team ready for a six-month development project? What if you want the power of a custom AI solution, but you need it working by next week, not next quarter?

That’s the question I had in mind when I started this deep dive. This list covers the best Together AI alternatives I could find, from platforms that give you the raw parts to build from scratch to purpose-built tools that get you to the finish line in minutes, not months.

What are Together AI alternatives?

At their core, platforms in the same ballpark as Together AI are cloud providers that specialize in GPUs (Graphics Processing Units). Think of them as a version of Amazon Web Services that’s been custom-built for AI work.

Their main purpose is to give developers and data scientists the heavy-duty hardware and software needed to run complex AI models. This usually breaks down into three main jobs:

• Training a model: This is like building a new AI from the ground up using your own data.

• Fine-tuning a model: You take an existing open-source model and teach it new tricks using your specific information.

• Running inference: This is when you actually use the trained model to get answers or generate content. It’s the part your end-users actually interact with.

The person using these platforms is usually pretty technical. They’re comfortable with Python, APIs, and a command-line interface.

How I compared the Together AI alternatives

To make sense of all the options, I judged each platform on a few key things that really matter when you’re trying to get a project off the ground and into the real world.

• Ease of Use: How fast can you get from signing up to having something that actually works?

• Control & Customization: How much can you fiddle with the settings and change how the AI behaves to get exactly what you need?

• Production-Readiness: Is this a tool for tinkering and building prototypes, or is it sturdy enough to handle real customers?

• Pricing: Is the cost straightforward and predictable, or is it a complicated, usage-based model that’s impossible to budget for?

• Who is it really for?: What’s the main job this platform was designed to do?

Together AI alternatives at a glance

Platform	Best For	Pricing Model	Key Differentiator
eesel AI	Teams needing production-ready AI for customer support & ITSM	Predictable monthly/annual fee	Go live in minutes, no ML team needed
Northflank	Deploying full-stack AI products (models, APIs, frontend)	Predictable, container-based	Full CI/CD and DevOps control
Replicate	Quick and easy API access to thousands of public models	Pay-per-second GPU usage	Simplicity and model variety
Modal	Running serverless Python functions and async ML jobs	Usage-based (CPU/GPU time)	Python-native and scales to zero
Fireworks AI	Developers seeking the fastest possible model inference	Per-token usage	Optimized for low-latency inference
Baseten	Building and sharing internal ML-powered tools and demos	Usage-based	Integrated simple app builder
Lambda Labs	Teams needing raw GPU power for large-scale model training	Per-hour GPU rental	Direct access to high-end hardware

The 7 best Together AI alternatives in 2025

Alright, let’s get into the details. Each of these platforms has its own strengths, but they solve very different kinds of problems.

1. eesel AI

Instead of giving you a box of parts and a manual to build a car, eesel AI hands you the keys to a vehicle that’s already built and ready to go. It’s the smart pick for teams who want to solve a specific business problem, like automating customer support, without the huge budget and long timeline that comes with a custom build.

It’s on this list because it focuses on the why behind most AI projects. You could use a platform like Together AI to try and build a support chatbot from scratch. Or, you could use eesel AI to launch a production-ready AI agent in under an hour. It connects right into the tools you already use, like Zendesk and Confluence, learns from your existing knowledge, and starts helping your customers.

Pros:

• Genuinely self-serve: You can sign up, set up your AI, and go live in minutes without having to talk to a salesperson.

• Learns from your data: It automatically reads through your past support tickets and knowledge bases to learn your brand’s voice and how to solve problems correctly.

• You’re in complete control: You get to decide exactly which tickets get automated and what the AI is allowed to do, whether that’s tagging a ticket, escalating to a human, or even checking an order status through an external API.

• Test without risk: A cool simulation mode lets you see how the AI would have handled thousands of your past tickets before you ever turn it on for live customers.

Cons:

• It’s not a general-purpose ML platform. It’s built specifically for customer service, ITSM, and managing internal knowledge.

The eesel AI simulation mode allows users to test the AI's performance on past tickets, providing a risk-free way to evaluate one of the top Together AI alternatives.::

Pricing:

eesel AI has clear, predictable pricing. You know exactly what you’ll pay each month, with no weird fees based on how many tickets it solves or how much time it spends thinking.

Plan	Price (Billed Monthly)	Price (Billed Annually)	Key Features
Team	$299 / month	$239 / month	1,000 AI interactions/mo, up to 3 bots, train on docs, AI Copilot, Slack integration.
Business	$799 / month	$639 / month	3,000 AI interactions/mo, unlimited bots, train on past tickets, AI Agent, AI Actions.
Custom	Contact Sales	Contact Sales	Unlimited interactions, advanced actions, multi-agent orchestration, custom integrations.

2. Northflank

If your project is more than just an AI model, Northflank is a compelling choice. It’s made for teams who need to deploy a whole application: the model, the backend API, the user-facing frontend, and the database. It lets you manage all of these moving parts in one spot, with the kind of control developers appreciate.

Northflank is the right move when you have a team of engineers ready to build and manage a complete product and want one platform to handle the entire deployment pipeline.

Pros:

• Great for building and launching professional-grade applications.

• Has built-in CI/CD pipelines to automate your releases.

• You can run it on your own AWS, GCP, or Azure account if you want.

Cons:

• It has a bit of a learning curve. You’ll need to be comfortable with concepts like containers and modern software development practices.

• You’re still on the hook for building all the application logic yourself.

Pricing:

Northflank’s pricing is based on the resources your application uses, which is more predictable than pay-per-second models. You’re billed for CPU, memory, and GPU usage.

• CPU: Starts at $12.00 / vCPU / month

• Memory: Starts at $6.00 / GB / month

• GPU (NVIDIA H100): Starts at $2.74 / hour

3. Replicate

Replicate is known for one thing: making things easy. It’s one of the quickest ways to get a working API for thousands of popular open-source models, whether you need Llama for text or Stable Diffusion for images. You just find a model, plug in your API key, and you’re off and running.

It’s a fantastic tool for developers who want to add a public AI model into their app without dealing with any of the tedious setup or configuration.

Pros:

• Super easy to use, with a giant library of models ready to go.

• It’s serverless, which means it scales down to zero so you don’t pay a dime when it’s not being used.

• Perfect for prototyping and getting an idea off the ground quickly.

Cons:

• The costs can climb quickly if you have a lot of traffic or your tasks take a long time to run.

• You have less control over the hardware and the environment the model runs in.

Pricing:

Replicate bills you for every second your model is running on a GPU. It’s simple to understand but can be tricky to budget for if your usage spikes.

Hardware	Price per Second	Price per Hour
CPU	$0.000100	$0.36
Nvidia T4 GPU	$0.000225	$0.81
Nvidia A100 (80GB) GPU	$0.001400	$5.04
Nvidia H100 GPU	$0.001525	$5.49

4. Modal

For Python developers who want to run code in the cloud without becoming infrastructure experts, Modal is a game-changer. It’s less about keeping a server online 24/7 and more about running functions on demand. This makes it perfect for things like data processing, running batch predictions, or any other background AI task you can wrap in a Python function.

If your work involves running scheduled jobs or chewing through large amounts of data, Modal is a great fit.

Pros:

• It feels natural for Python developers; you just add simple decorators to your code.

• Scales down to zero automatically, so you only pay for actual usage.

• Excellent for background workflows and data-heavy tasks.

Cons:

• It’s not designed for hosting traditional websites or full-stack applications that need to be always-on.

• The pricing, while fair, needs to be watched for jobs that might run for a long time.

Pricing:

Modal’s pricing is purely usage-based. They have a free starter plan that includes a $30/month credit.

Resource	Price per Second
CPU (Physical Core)	$0.0000131 / core
Nvidia T4 GPU	$0.000164
Nvidia A100 (80GB) GPU	$0.000694
Nvidia H100 GPU	$0.001097

5. Fireworks AI

Fireworks AI is a direct competitor to Together AI, and they are all about one thing: speed. They claim to have one of the fastest platforms out there for getting responses from a model. For teams where every millisecond counts, they are a serious contender.

They offer a library of popular open-source models that have been fine-tuned for fast responses. If your main goal is getting the quickest possible answer from a model like Llama or Mixtral, Fireworks is worth checking out.

Pros:

• Highly optimized for speed and low-latency responses.

• Offers competitive and easy-to-understand pricing based on tokens.

• Supports a good variety of popular open-source models.

Cons:

• Focuses mostly on the "inference" part of the process. It’s less of a platform for training models or deploying full applications.

• The focus is on the tool itself, not necessarily the end-to-end solution.

Pricing:

Fireworks AI uses a simple pay-per-token model.

Model Example	Price per 1M Tokens
Llama 3 8B Instruct	$0.20
Gemma 3 27B Instruct	$0.90
Deepseek R1	$3.00 (Input) / $8.00 (Output)

6. Baseten

Baseten really shines when you not only need to deploy a model but also want to quickly build a simple interface for it. This is perfect for creating internal tools for your business teams, sharing demos with stakeholders, or letting non-technical folks play with your model without having to use an API.

It bundles a solid model deployment platform with a simple app builder, making it a great option for ML teams that need to show off their work quickly.

Pros:

• A really nice experience for developers deploying models.

• The built-in UI builder is a standout feature that makes building internal tools much faster.

• Good for prototypes, demos, and internal apps.

Cons:

• Less ideal for complicated, public-facing applications that have a lot going on in the backend.

• Can be pricier than other options if you aren’t taking advantage of the app-building features.

Pricing:

Baseten has a free tier for individual developers. Paid plans are based on usage and features.

• Developer: Free (for individuals and hobbyists).

• Startup: Starts at $500/month (for teams building and scaling production apps).

• Enterprise: Custom pricing.

7. Lambda Labs

For teams that just want raw power and total control, Lambda Labs is the place to go. Similar to Together AI’s dedicated options, Lambda gives you direct access to high-performance GPU hardware. This is purely an infrastructure provider; you’re renting beefy servers packed with the latest NVIDIA GPUs.

This is the choice for well-funded research teams or large companies with a dedicated MLOps team that needs to train enormous models from the ground up.

Pros:

• Direct access to some of the most powerful NVIDIA GPUs on the market.

• Perfect for heavy-duty, large-scale model training.

• Simple, predictable hourly rental costs.

Cons:

• You are responsible for managing everything yourself, from the operating system to all the ML software.

• You need serious MLOps and DevOps expertise on your team to use this effectively.

Pricing:

Lambda Labs charges a simple hourly fee for their GPU servers.

GPU Instance	Price per Hour
1x NVIDIA H100	$2.49
8x NVIDIA H100	$19.92 ($2.49 each)
8x NVIDIA B200	$23.92 ($2.99 each)

How to choose the right Together AI alternative for you

Picking the right platform really comes down to answering one question.

This is the most important thing to ask yourself. If you’re an ML engineer trying to invent a new model architecture, then a platform like Lambda Labs or Fireworks AI is your sandbox. You need the raw materials.

But if you’re a Head of Support trying to cut down your first-response time and handle 40% of common tickets automatically, building a solution from scratch is the longest, most expensive, and riskiest way to do it. A purpose-built tool like eesel AI gives you a direct path to that goal.

Don’t just look at the per-token price. Think about the total cost. You have to factor in developer salaries, months of research and development, ongoing maintenance, and the cost of waiting to solve the problem. A platform with a predictable monthly fee often ends up being much cheaper than a "pay-per-use" model once you add up all the hidden expenses.

Finally, be realistic about your team’s skills. Choosing a platform that requires deep ML knowledge you don’t have is just a recipe for delays and frustration.

This video explores TurboSeek, an open-source alternative powered by Together AI, offering insights into different platform choices.

The takeaway on Together AI alternatives: Focus on the outcome, not just the tools

The world of AI infrastructure is fascinating, but it’s easy to get lost admiring the tools and forget what you’re trying to build. Together AI and its direct alternatives are fantastic for teams building foundational technology.

But for specific, high-value business challenges like customer service, a solution-focused platform is faster, cheaper, and much less risky. eesel AI is designed for teams that want to use world-class AI today to make their business better, without having to become an AI research company in the process.

Ready to solve your support challenges instead of building more infrastructure? Try eesel AI for free and see how fast you can launch a powerful AI agent that actually gets the job done.