The 5 best Snorkel AI alternatives in 2025 (For building & applying AI)

So, you’re looking for Snorkel AI alternatives. That probably means you’re facing a big question: do you spend a ton of time and resources building a custom AI model from scratch, or do you grab a ready-to-go AI tool and start solving business problems right now?

Snorkel AI is a fantastic platform for that first path, helping you meticulously get your data ready to train new models. But for many teams, especially in customer support or IT, the goal isn’t to become AI researchers. It’s to automate tedious work and get people answers, fast. This guide covers both sides of the coin, breaking down the top tools for hardcore data labeling alongside a much more direct route to getting results with AI.

What is Snorkel AI?

Snorkel AI started out in the Stanford AI Lab and has since become a major player in AI data development. Its whole deal is something called programmatic data labeling. Instead of a person manually labeling thousands of pieces of data (think: "this email is spam," "this one isn’t"), you write rules or functions to do it automatically. It’s a smart way to handle huge datasets, especially for text.

The catch? It’s seriously technical. This is a platform built for data science and machine learning teams who have the skills to build, train, and maintain their own AI models. If you just want to, say, cut down on support tickets, this "build-it-yourself" approach could mean waiting months or even years to see any real results.

The two paths when considering Snorkel AI alternatives: Building vs. applying

Choosing the right tool really boils down to what you’re trying to accomplish. It’s worth taking a second to figure out which of these two camps you fall into.

• The ‘Build from Scratch’ Path: This is the world of Snorkel AI, Labelbox, and Scale AI. Here, the main job is prepping and labeling enormous amounts of data to train or fine-tune a model. It’s a long-term, expensive, and deeply technical journey that needs a dedicated team of ML engineers and data scientists. The prize at the end is your very own AI model, built for a super specific, complex task.

• The ‘Apply and Go’ Path: This is where a platform like eesel AI comes in. Instead of building anything from the ground up, you take a powerful, pre-trained AI and apply it directly to your business. The focus is on getting started quickly, being easy to use, and seeing a return on your investment almost immediately. You just connect your existing knowledge bases and tools, and the AI is ready to work in minutes, not months. This is perfect for customer support, IT help desks, and internal knowledge sharing where the real goal is just making things run more smoothly.

This guide has the best options for both paths, so you can find what makes sense for your team’s goals, both for today and for the long haul.

Our criteria for the best Snorkel AI alternatives

To put this list together, I looked at these tools from a few different angles, keeping both the tech-heavy and business-focused teams in mind.

• Speed to results: How quickly can you actually get it running and see it making a difference? Are we talking minutes or months?

• Ease of use: Do you need a team of developers on speed dial, or can a non-technical person get things done?

• Focus: Is this a general toolkit for building models, or is it designed to solve a specific business headache, like support automation?

• Control: How much can you tweak the AI’s behavior, the sources it learns from, and how it fits into your workflows?

• Pricing: Is the price tag clear and simple, or are you walking into a minefield of hidden fees and long-term contracts?

Comparison of the top Snorkel AI alternatives for 2025

Here’s a quick side-by-side look at how our top 5 picks compare.

Tool	Best For	Time to Value	Ease of Use	Key Focus	Pricing Model
eesel AI	Applying AI to automate support & IT	Minutes	Self-serve, no-code	Business Automation	Subscription-based, transparent
Labelbox	Building AI with human-in-the-loop data	Weeks to Months	Moderate, for technical users	Data Labeling & Annotation	Custom/Usage-based
SuperAnnotate	Building AI with complex annotation needs	Weeks to Months	Moderate, for technical users	Multimodal Data Annotation	Custom/Usage-based
refinery	Building AI with an open-source framework	Days to Weeks	High, requires developers	Data-centric RAG pipelines	Open-source (self-hosted)
Scale AI	Outsourcing large-scale data labeling	Months	N/A (Managed Service)	Managed Data Labeling	Per-project, high cost

The top 5 Snorkel AI alternatives in 2025

Alright, let’s get into the details of the best alternatives, whether you’re gearing up to build a model or just want to put AI to work today.

1. eesel AI

eesel AI is our top pick for teams who want to skip the whole model-building headache and jump straight to fixing business problems. It’s an AI platform made for customer service, IT, and internal support that plugs right into the tools you already use, like Zendesk, Slack, and Confluence. Instead of labeling data for months, you just connect your knowledge sources. From there, eesel AI builds autonomous agents and copilots that can resolve tickets, answer questions, and give your human agents a hand.

Pros:

• Set up in minutes, literally: This is a true self-serve platform. You can sign up and get an AI agent running without ever talking to a salesperson. The integrations are one-click, so no developers are needed.

• Test before you launch: It has a cool simulation feature that runs the AI on your past tickets. This way, you can see exactly how it would have performed and get a real ROI estimate before you flip the switch for your customers.

A screenshot of the eesel AI simulation feature, a key differentiator among Snorkel AI alternatives, showing how users can test AI performance on past tickets before deployment.

• You’re in the driver’s seat: You get to decide exactly which tickets the AI handles. You can tweak its personality, give it custom tasks (like looking up an order status), and limit what it knows to keep it on topic.

The customization dashboard in eesel AI, where users can set rules and guardrails, showcasing the platform's control features as one of the top Snorkel AI alternatives.

• Clear, simple pricing: The plans are based on usage. No weird per-resolution fees that lead to a surprise bill at the end of the month.

Cons:

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

• It’s laser-focused on support, IT, and internal knowledge use cases, so it’s not the right fit for something like analyzing medical images.

Pricing: eesel AI has a 7-day free trial and straightforward subscription plans.

• Team: $299 per month ($239/mo if you pay annually) gets you up to 3 bots and 1,000 AI interactions, plus the AI Copilot for help desks and Slack.

• Business: $799 per month ($639/mo annually) gives you unlimited bots and 3,000 interactions. This unlocks the full AI Agent, training on past tickets, and more advanced actions.

• Custom: You can talk to their sales team for unlimited interactions and other enterprise-level features.

An image of eesel AI's clear, subscription-based pricing page, highlighting the transparent pricing model that differentiates it from other Snorkel AI alternatives.

2. Labelbox

Labelbox is another big name for teams going down the "build" path and a direct competitor to Snorkel. It’s a whole suite of tools for labeling data, spotting model errors, and keeping your data workflows in order. Where Snorkel leans into automatic labeling, Labelbox is fantastic for projects that need a human touch, letting your team of experts collaborate through a pretty clean interface.

Pros:

• It can handle all sorts of data: images, video, text, and audio.

• Great for projects that need high-quality, nuanced labeling done by actual humans.

• It combines data labeling, management, and model checking all in one place.

Cons:

• There’s a good amount of setup and management involved.

• Their main subscription plan has custom pricing, which can make budgeting a bit of a guessing game.

• The platform is all about prepping data; it doesn’t offer a ready-to-use solution for a business problem.

Pricing: Labelbox’s pricing is a mix of software and services.

• Free Tier: They have a pretty decent free plan for individuals and small teams, which is great for getting your feet wet.

• Subscription Tier: This is their enterprise offering with custom pricing. You’ll need to talk to them to get a quote.

• Services: If you want them to manage the data labeling for you, that’s another custom quote.

3. SuperAnnotate

SuperAnnotate is another strong player in the data annotation world, especially known for how much you can customize it. It’s a great fit for really complex projects, like in computer vision, where you might need to build your own specific annotation tools and quality checks. They offer both the software and managed services to do the labeling for you.

Pros:

• Super flexible platform that you can bend to your will for unique projects.

• Comes with solid tools for managing your projects and making sure the quality is high.

• It’s designed to let your internal teams, outside help, and QA folks all work together in one spot.

Cons:

• It can be a bit overkill for simpler labeling tasks.

• The pricing isn’t public, so you can’t quickly check if it’s in your budget without going through a sales call.

• It’s built for teams that want fine-grained control over the annotation process, not for those who just want to outsource everything.

Pricing: SuperAnnotate keeps its prices under wraps. You have to request a demo to get a quote for any of their plans, which can slow things down if you’re just trying to compare options.

4. refinery

If you’re looking for an open-source option, refinery is your best bet. From the team at Kern AI, it’s designed for developer-led teams who want total control over their data pipelines without being tied to a specific company’s platform. It’s especially good for building more reliable RAG systems, helping you structure your data to prevent those weird AI "hallucinations."

Pros:

• It’s open-source, so it’s flexible and developer-friendly.

• A great choice for building and improving RAG-based apps.

• You can host it yourself, which is a big plus for data security.

Cons:

• You’ll need some serious engineering muscle to get it set up and keep it running.

• It doesn’t have the polished, user-friendly interface you’d get from a commercial product.

• "Free" doesn’t include the cost of the servers and the engineers you’ll need to run it.

Pricing: It’s free and open-source. The real cost is in the time and infrastructure your team will need to dedicate to it. Kern AI does offer a commercial platform for bigger companies, but you’ll have to contact their sales team for that.

5. Scale AI

Scale AI is the big one. If you have a mountain of data and just want to throw money at the problem to make it go away, Scale is an option. They have a massive global workforce of human annotators and can handle the entire process for you. The downside is that it can feel like a "black box" where you don’t have much insight into what’s happening.

Pros:

• Gives you access to a huge workforce for labeling enormous datasets.

• They’ve worked with all the big names, like OpenAI and Microsoft.

Cons:

• The price tag is very high, putting it out of reach for many teams.

• You don’t get a lot of control or visibility into how your data is being labeled.

• Meta recently invested a huge amount in them, which might make some competitors hesitate to use their services.

Pricing: Scale AI’s pricing is almost entirely aimed at large enterprises.

• Enterprise: This is their main offering, and you have to book a demo to get a price.

• Self-Serve: They have a pay-as-you-go option if you bring your own labelers, but it’s clearly not their main focus.

This video from a Snorkel AI engineer explains how to align language models with human preferences, a key concept for teams building their own AI.

How to choose the right platform

Feeling stuck? Just ask yourself one question: "Is my main goal to build a brand-new AI model, or is it to solve a business problem?"

• If your goal is R&D, you have data scientists on staff, and you’ve got the budget to build something proprietary, then a data development platform like Labelbox or SuperAnnotate makes the most sense.

• But if your goal is to lower support costs, make your agents more efficient, or handle more IT tickets this quarter, an application-focused platform is the way to go. A tool like eesel AI delivers value right away by automating the work you’re already doing with the knowledge you already have, all without writing a single line of code.

The smarter path to AI-powered support

Look, building a custom AI model sounds cool on paper. But for most companies, it’s a long, expensive road with no guarantee of a payoff at the end. The faster, more practical move is to use a platform that has already done all that heavy lifting for you.

With a solution like eesel AI, you can get a powerful and secure AI agent up and running in minutes. You get all the perks of AI, like 24/7 support and instant answers, without the massive headache of a full-blown data science project. You can start small, see how it performs, and then scale up when you see the results.

Ready to see how fast you can put AI to work for your business?

Start your free eesel AI trial today or book a demo with our team.