5 practical Scale AI alternatives for ML teams in 2025

When Meta dropped a cool $15 billion on Scale AI, it definitely got people talking. Almost immediately, big players like Google and OpenAI, who are in a head-to-head race with Meta, started backing away and looking for partners who weren’t so… conflicted.

This big move has left a lot of ML teams wondering who they can trust with their most sensitive AI projects. But it also brings up a bigger question: is there a better, faster way to get value from AI in the first place?

This guide breaks down the top Scale AI alternatives. We’ll look at the classic data labeling platforms for teams building new models from the ground up. But we’ll also explore a smarter option for businesses that want to use AI to solve real problems, like a swamped customer support team, without the massive upfront investment.

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

Scale AI is a company that helps other businesses train their AI models by providing high-quality data labeling and annotation. They’re famous for helping build huge large language models (LLMs) using techniques like Reinforcement Learning from Human Feedback (RLHF), which needs an enormous amount of data.

While Scale AI is powerful, more and more teams are starting to look elsewhere for a few pretty solid reasons:

• Vendor neutrality: With Meta holding a 49% stake, it’s easy to see why competitors like Google and Microsoft feel a bit uneasy. Handing over sensitive model data to a platform that’s half-owned by your biggest rival is a tough pill to swallow.

• The price tag: Let’s be honest, Scale AI is expensive. Projects can easily climb into the hundreds of thousands of dollars, which is simply out of reach for a lot of companies.

• The "black box" problem: Many users feel like they’re just sending data into a void. You don’t get much insight into how it’s being handled, and that lack of control can feel risky when you’re working on a long-term AI project.

• The bigger picture: For many, the problem isn’t just Scale AI itself. It’s the whole process of building custom AI models from scratch, which is incredibly time-consuming, expensive, and complex. This has opened the door for a different kind of alternative: applied AI platforms that are designed to solve specific business problems right out of the box.

Our criteria for choosing the best Scale AI alternatives

When we sized up these Scale AI alternatives, we looked beyond just their data labeling features. We focused on what really matters when it comes to getting a return on your AI investment.

• Time to value: How quickly can you go from signing up to actually seeing an impact on your business?

• Ease of use & setup: Can you get it up and running on your own, or are you stuck in a never-ending cycle of sales calls and implementation meetings?

• Business impact: Does the platform solve a real, immediate business need (like cutting down on support tickets) or is it just another tool for a long-term R&D project?

• Cost predictability: Is the pricing straightforward, or are you going to get a nasty surprise on your bill after a busy month?

• Vendor neutrality: Is the company an independent player in the AI space?

Comparison of the top Scale AI alternatives

This table gives you a quick look at the platforms we’re about to cover. It highlights the main differences between the traditional data labelers and the applied AI solutions that are all about getting you quick results.

Feature	eesel AI	SuperAnnotate	Labelbox	Encord	CVAT
Primary Use Case	Customer Support Automation	Data Annotation	Data Annotation	Data Annotation	Data Annotation
Time to Go Live	Minutes	Weeks to Months	Weeks to Months	Weeks to Months	Days to Weeks
Self-Serve Setup?	Yes	No, requires demo	No, requires demo	No, requires demo	Yes (self-hosted)
Best For	Automating support workflows	Customizable labeling projects	Enterprise collaboration	Complex video/medical data	Technical teams on a budget
Pricing Model	Flat monthly fee	Per user / Per hour	Tiered / Custom	Custom	Free (Open-Source)

5 best Scale AI alternatives for different needs in 2025

Here are our top picks. We’re starting with a different kind of alternative, one that focuses on applying AI for immediate impact, before diving into the direct competitors for data annotation.

1. eesel AI (The applied AI alternative for immediate business impact)

Instead of spending months and a small fortune on data labeling just to build a support bot, eesel AI lets you launch one in minutes. It’s the ideal alternative for teams that want to solve real business problems, like crushing ticket queues and answering the same questions over and over again, right now, not next year. It connects directly to the tools you already use, like Zendesk, Slack, and Confluence, and learns from your past tickets and knowledge bases to give accurate, helpful answers from day one.

Key Features:

• Go live in minutes: This is a truly self-serve platform. You can sign up, connect your tools, and get an AI agent running without having to talk to a single salesperson.

• Risk-free simulation: You can test the AI on thousands of your past tickets to see exactly how it would have performed. This lets you calculate your ROI before the bot ever talks to a real customer.

• Unifies all your knowledge: It pulls information from your helpdesk, Confluence, Google Docs, and more, so it learns from everything your team already knows.

• Total workflow control: You get to decide exactly which tickets the AI handles and what it can do, whether that’s escalating an issue to a human agent or looking up order info in Shopify.

Pros: You get value incredibly quickly, the pricing is predictable with no weird per-resolution fees, and it fits right into your current workflow without a massive migration project.

Cons: It’s an applied AI solution specifically for support and internal knowledge, not a general tool for annotating data to build brand new models from scratch.

Pricing: Plans start at $299/month with a simple, interaction-based model. You can even choose a month-to-month plan and cancel whenever you want.

2. SuperAnnotate (For customizable data labeling workflows)

If you’re set on going down the data labeling path, SuperAnnotate is a strong contender. It’s known for its highly customizable platform that gives you a ton of control over your annotation workflows. This makes it a great fit for large, complex AI projects that involve different types of data.

Key Features: It has a fully customizable platform, supports all sorts of data (images, video, text, 3D), and has great tools for managing quality control and your team of annotators.

Pros: It’s super flexible, works well for complicated enterprise projects, and gives you a clear view of your entire data pipeline.

Cons: All that complexity means it’s probably not the best option for small teams or simple, one-off projects. It’s really built for teams that want deep, hands-on control over the process.

Pricing: SuperAnnotate has a 14-day free trial, and paid plans start at around $62 per user per month.

3. Labelbox (For collaborative enterprise annotation)

Labelbox is another solid competitor to Scale AI that positions itself as a central hub for enterprise AI teams. Its main strengths are its collaboration features, workflow management tools, and its use of AI to help speed up the annotation process.

Key Features: It offers model-assisted labeling, a clean interface, customizable workflows, and connects well with major machine learning pipelines.

Pros: It’s great for large teams that need to work together on huge datasets, and its user-friendly design makes it fairly easy to get started.

Cons: The price can be a bit steep for smaller teams, and it doesn’t support 3D annotation as well as some of the more specialized tools out there.

Pricing: Labelbox uses a tiered pricing model that depends on your project. You’ll need to schedule a demo to get a quote.

4. Encord (For complex video and medical data)

Encord has found its sweet spot by becoming an expert in tricky areas like medical imaging (DICOM) and high-quality video annotation. If your project involves complex visual data that needs specialized, precise tools, Encord is a fantastic choice.

Key Features: It has best-in-class tools for video, including object tracking within videos, and offers AI-assisted labeling with great support for medical and scientific data formats.

Pros: It’s a leader in the medical and video annotation space, with advanced tools that are trusted by top research institutions like Stanford Medicine.

Cons: It isn’t built for total customization across all data types, so it’s less flexible for general GenAI projects that fall outside its core specialties.

Pricing: Encord doesn’t list its prices publicly, so you’ll have to get in touch with them for a custom quote.

5. CVAT (The open-source option for technical teams)

For teams that have the engineering talent in-house and want full control without the software costs, the Computer Vision Annotation Tool (CVAT) is the go-to open-source option. It’s a powerful tool for annotating images and video without getting locked into a vendor.

Key Features: It’s totally free and open-source, highly customizable, supports different types of annotation like object detection and segmentation, and lets multiple users collaborate.

Pros: It’s free, and it gives you complete control over your data and workflows.

Cons: You’ll need some serious technical know-how to set up, host, and maintain it. There’s a steep learning curve, and since it’s open-source, you won’t have a dedicated customer support team to call when you get stuck.

Pricing: Free.

Data labeling platforms vs. applied AI: which path is right for you when choosing Scale AI alternatives?

This whole Scale AI situation is a good time to step back and ask a bigger question: should you be investing in tools to build AI, or in platforms that let you apply AI?

You should probably choose a data labeling platform if:

• You’re building a totally new, proprietary AI model from the ground up.

• You have a dedicated ML research team and a budget that spans multiple years.

• Your main goal is to create a massive, high-quality labeled dataset for training.

• Think: SuperAnnotate, Labelbox, or Encord.

You should probably choose an applied AI platform like eesel AI if:

• You’re trying to solve a specific, pressing business problem (like lowering support costs or making your agents more efficient).

• You want to see a return on your investment in days or weeks, not years.

• You need something that works with the software you already have (your helpdesk, your internal wiki) without a painful migration process.

• You’d rather have a predictable, flat-fee pricing model that doesn’t punish you for having a busy month.

Final thoughts on Scale AI alternatives: it’s time to rethink your AI strategy

Looking for Scale AI alternatives is about more than just finding a new data labeler. It’s a chance to rethink how you’re approaching AI altogether. While building a custom model from scratch is a worthy long-term goal for some, the fastest way to get real business value is often by applying proven AI solutions to your biggest headaches.

For customer support teams, that means getting out of the slow and expensive cycle of data annotation and model training. It’s about deploying an intelligent agent that already understands your business on day one.

Discover mind-blowing AI tools that can help scale your business and solve real-world problems, moving beyond traditional data labeling.

Get started with one of the best Scale AI alternatives in minutes

If you’re ready to see how an applied AI solution can change your customer support, you don’t have to wait around for a demo or a sales call.

With eesel AI, you can sign up and build your first AI agent in less than 5 minutes. Connect your knowledge sources, see how it would have performed on your real ticket history, and find out just how much time and money you can save.

Start your free trial or book a demo today and deploy an AI agent that works for you.