I reviewed 7 top Scale AI alternatives for 2025 to find a better way to build AI

So, you're looking for Scale AI alternatives. It seems like a lot of people are these days. Between the Meta acquisition and the usual worries about high costs and lack of control, plenty of teams are shopping around.

But here’s a thought: what if the best alternative isn't just another data labeling company? I dug into 7 different options, from the usual suspects to AI automation tools, to find a path that gets you to the finish line, not just the starting block of raw data.

What is Scale AI and why are people looking for Scale AI alternatives?

Scale AI is basically a service that provides human-labeled data to train machine learning models. You hand over your raw data, images, text, whatever you've got, and Scale’s mix of software and people annotates it so your AI models can make sense of it.

It's a powerful service, but it's not always the right fit. Here’s why many teams are starting to look elsewhere.

First, the cost can be a real headache. Pricing is often unpredictable, making it tough for anyone without a massive budget to get on board.

We got a quote for our project, and it was astronomical. The per-task pricing felt completely unpredictable, making it impossible to budget effectively. We had to find other options.

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Then there’s the “black box” feeling. Handing over your most important data for labeling can feel like you’re just tossing it over a wall and hoping for the best. You don't get much say or visibility into how the work gets done, which makes quality control a bit of a guessing game.

Meta’s recent investment in Scale AI also raised a few eyebrows. If you're competing with Meta, you might not feel great about them being so close to your data vendor.

And maybe the biggest reason: for a lot of jobs, like customer support, data labeling is just a long, expensive detour. The goal is a working AI that solves a problem, not a perfectly labeled dataset.

My criteria for choosing the best Scale AI alternatives

To make this list actually useful, I didn't just round up a bunch of Scale AI copycats. I looked at these tools from the perspective of what a team really needs to get an AI project up and running.

• What’s it for? Is it a general-purpose tool for labeling any kind of data, or is it built for a specific job like customer support?

• How hard is it to start? Can you just sign up and get going, or do you need to book a sales call and assemble an engineering team?

• Can you trust the quality? How much control do you have over the process and the final output?

• Is the pricing clear? Do you know what you’re paying for, or are there surprise fees waiting for you?

Scale AI alternatives comparison for 2025

Here's a quick side-by-side look at the tools that made the cut.

The 7 best Scale AI alternatives in 2025

Alright, let's get into the details. Here’s the breakdown of my top picks, starting with the option that lets you skip the labeling part completely.

1. eesel AI

If your goal is to automate customer support, eesel AI takes a completely different approach. Instead of spending a fortune and waiting months to label data for a custom support bot, eesel lets you launch an AI agent that learns directly from the knowledge you already have, like past tickets, help center articles, and internal docs.

You can get it running in minutes. It connects to helpdesks like Zendesk or Freshdesk in one click, and you can set it all up yourself. The best part is a simulation mode that lets you see how it would have handled past tickets, so you can test it out with zero risk before turning it on. It gets you straight to the outcome you want: solving customer issues faster.

A screenshot of the eesel AI simulation mode, which shows how the AI would have handled past tickets, demonstrating a risk-free way to test automation among Scale AI alternatives.

• Pros: Incredibly simple to set up yourself, learns from your existing knowledge automatically, and has a great simulation mode for risk-free testing. The pricing is also refreshingly clear.

• Cons: It's built specifically for customer service, ITSM, and internal support, so it's not for general model training.

• Pricing: eesel AI has transparent, flat-rate pricing. The Team plan is $299/month, and the Business plan is $799/month, with discounts for paying annually. No hidden per-resolution fees.

2. Labelbox

Labelbox is a big name in this space and a solid direct alternative to Scale AI, particularly if you want more hands-on control. It gives you a whole toolkit for annotating data, managing your datasets, and checking on your model's performance. It’s a software-first solution, meaning it empowers your own team (or their on-demand workers) to manage the labeling process.

• Pros: A powerful platform with good automation and quality control tools. It’s great for team collaboration and fits nicely into existing MLOps pipelines.

• Cons: It can get pricey for smaller teams, and there’s a bit of a learning curve because it does so much. The pricing isn't public.

• Pricing: Labelbox has a free plan for individuals, but it's limited. For actual business use, you have to get on a sales call for a custom quote, which makes it impossible to budget for without talking to them first.

3. SuperAnnotate

SuperAnnotate is built for the big leagues. It's an enterprise-level platform that’s known for letting you tweak just about every part of the annotation process. It’s designed for teams working on complicated computer vision and NLP projects where having fine-grained control is a must. It handles all sorts of data (images, video, text, LiDAR) and has solid tools for quality checks and project management.

• Pros: Very flexible and customizable, great for projects using different types of data, and offers top-notch quality control features.

• Cons: It's really geared toward large companies, so it can be overkill for small teams. The initial setup can be complex.

• Pricing: SuperAnnotate’s pricing page shows different tiers, but no actual prices. You have to book a demo or contact sales to get any numbers, which again, adds a hurdle to the evaluation process.

4. Encord

Encord is another heavy-hitter for creating quality training data, with a real focus on AI-assisted labeling. It’s especially good with tricky video and medical imaging annotation. Encord uses "micro-models" to help automate parts of the annotation work, which can save a lot of time and keep things consistent. It's a great option if you want to speed things up without sacrificing quality.

• Pros: Excellent AI-assisted labeling tools, great support for video and medical data (like DICOM), and solid security features.

• Cons: Might be more than you need for simple annotation jobs. And like the others, pricing is kept under wraps.

• Pricing: Encord's pricing is also hidden behind a "Get started" or "Contact sales" button. You won't find any numbers on their site.

5. V7

V7 is all about handling complex visual data, especially for computer vision projects. People really like its clean interface and smart automation features, like model-assisted labeling and automated quality checks. V7 is a favorite among teams in healthcare and life sciences because it handles DICOM and other medical image formats so well.

• Pros: Easy-to-use interface, fantastic for medical and scientific images, and has strong AI-powered automation.

• Cons: The price can be a roadblock for smaller projects, and it’s very focused on visual data.

• Pricing: V7's pricing is completely custom. They calculate a quote based on a platform fee, how many users you have, and how much data you process. You have to book a 30-minute call to get a price.

6. Label Your Data

Label Your Data takes a "best of both worlds" approach. They offer both a platform you can use yourself and a fully managed service where they do the labeling for you. This is great for teams that want to outsource a big project but still have the ability to handle smaller tasks in-house. They also have a good reputation for being secure and high-quality.

• Pros: A flexible hybrid model, strong security, and a free pilot to test them out. Their pricing is also very clear.

• Cons: The self-serve platform is mainly for computer vision; you'll need to use their managed service for NLP and audio.

• Pricing: It's refreshing to see a clear model. Label Your Data lists their prices right on their site. They charge per object (like $0.02 for a bounding box) or per hour ($6/hour). The free pilot is a nice touch.

7. CVAT

For teams with the technical skills and not much budget, CVAT is the DIY, open-source option. Intel originally developed it, and it's a powerful and free tool for annotating images and video. Since it's open-source, you have total control. You can customize it however you want and host it on your own servers, keeping your data private.

• Pros: It’s completely free to use, highly customizable, and supports a ton of computer vision tasks.

• Cons: The catch? You're the IT department. You need the technical know-how to set it up, host it, and keep it running. There's no customer support to call if you get stuck.

• Pricing: CVAT is free to download and use. The real "cost" is in the time and engineering resources you'll spend managing it yourself.

How to choose among Scale AI alternatives: Data labeling platform vs. AI automation

So, how do you pick the right path? It really comes down to what you're trying to accomplish.

You should probably stick with a data labeling platform (like Labelbox or CVAT) if:

• You're building something brand new, like an AI model to spot rare manufacturing defects.

• You have an MLOps team and the budget for a long-term project of training and tweaking a custom model.

• The main thing you need at the end of the day is a highly accurate, custom-trained model.

You should look at an AI automation platform (like eesel AI) if:

• You're trying to solve a common business problem, like getting through your customer support queue or internal IT tickets faster.

• Your main goal is to get a working AI solution live as quickly as possible.

• You already have a ton of useful information (old tickets, help articles) that an AI could learn from without you having to label anything by hand.

A workflow diagram comparing traditional data labeling with a direct AI automation approach, helping readers choose between different types of Scale AI alternatives.

For most businesses, that second path is going to get you results much, much faster.

This video offers a look at different AI tools available for research and data analysis, which is relevant for teams evaluating Scale AI alternatives.

Scale AI alternatives: Stop labeling, start delivering

While there are plenty of good Scale AI alternatives for data labeling, it’s worth asking if data labeling is the problem you actually need to solve. The whole process, annotating data, training a model, deploying it, is long, complicated, and expensive.

For important business functions like customer support, tools like eesel AI show there’s another way. You don't always need a massive data labeling project to get a great AI agent. By using the knowledge you already have, you can launch a smart, self-serve AI in minutes and see an impact right away. It's about shifting your focus from building AI from scratch to configuring a smart system that just works.

If your goal is to resolve issues faster and make customers happier, maybe it's time to skip the labeling queue.

Ready to see what an AI agent can do with your existing knowledge? Start your free eesel AI trial today and see how fast you can automate your support.