I tested 6 of the best Labelf alternatives in 2025 (here’s what I found)

After spending a few weeks buried in different NLP tools, I’ve got some notes to share. Specifically, I was looking for the best Labelf alternatives out there in 2025.

Labelf AI is a solid low-code platform for building your own text classification models. But what if that’s not quite what you need? Maybe you’re looking for different features, better connections with your support software, or just a quicker path to getting an AI up and running without having to build a model yourself. If that sounds familiar, this list should help. I’ve broken down six top alternatives, each with its own thing going for it, to help you figure out what works for your team.

What are text classification and no-code AI platforms?

Basically, text classification platforms teach a computer how to read and sort text. Imagine you have a giant, messy pile of customer emails. These tools help you sort them into neat piles like "Bug Reports," "Feature Requests," or "Billing Questions." That sorting process is called data labeling, and it’s how you start training a custom AI model.

The whole "no-code" part just means these platforms are built for regular folks, not just data scientists, with simple interfaces for labeling data and training a model. But there’s a big difference to keep in mind: some tools give you the toolbox to build an AI from scratch, while others hand you a ready-to-go AI application. It’s the difference between being the architect and just moving into the house.

What I looked for in these Labelf alternatives

To put this list together, I wasn’t just ticking boxes on a feature list. I focused on what actually matters for a business. I wanted to know how fast a team without a data scientist could get up and running. I also made sure every tool was genuinely good at handling text, from support tickets to internal docs.

A big one for me was how quickly you could see a real return, how long does it take to go from setup to an AI that’s actually helping? And finally, I looked at how well they play with the software you already use, like your helpdesk or knowledge base.

The best Labelf alternatives at a glance

Tool	Best For	Key Feature	Pricing Model
eesel AI	Customer support automation	Automatically trains on past tickets to create an autonomous agent	Flat monthly fee (no per-resolution costs)
MonkeyLearn	General text analysis	Pre-built models and a no-code model builder	Custom Plans
Lang.ai	CX ticket tagging & routing	AI-powered topic detection for support workflows	Custom Plans
Label Studio	DIY data labeling projects	Open-source and highly configurable annotation interface	Free (Open Source) / Enterprise Plan
Snorkel AI	Enterprise data science teams	Programmatic data labeling for large-scale datasets	Custom Enterprise Plans
DeepOpinion	Document-heavy workflows	No-code automation for processing invoices, forms, etc.	Custom Plans

A closer look at the 6 best Labelf alternatives

Here’s a breakdown of each platform, including what I liked, what to consider, and who it’s really for.

1. eesel AI: A solution-first Labelf alternative

While most tools on this list give you the parts to build an AI model, eesel AI is different, it gives you a ready-to-go AI agent. It’s built for teams who want the result (automated ticket resolution, instant answers) without spending months labeling data and training a model from scratch. You just connect it to your helpdesk and knowledge sources, it learns from your past conversations on its own, and you can have it live in minutes.

What I liked: It’s incredibly hands-off. You can seriously go from signing up to having a working AI agent in about 10 minutes, no sales call required. It automatically learns from all your existing knowledge, past tickets, help centers, Google Docs, and Confluence, so its answers are actually accurate and relevant. My favorite part is the risk-free simulation; you can test the AI on your old tickets to see exactly how it would have performed and calculate your ROI before it ever talks to a real customer. You also get full control over what tickets the AI handles and what it can do.

What to consider: It’s very focused on customer service, ITSM, and internal support. If you’re looking for a general-purpose tool to build random NLP models for other things, this isn’t it.

Pricing: Starts at $299/month for the Team plan, with predictable, transparent pricing that doesn’t charge you per resolution.

2. MonkeyLearn: A versatile Labelf alternative for text analysis

MonkeyLearn is a really versatile, no-code text analysis platform. It lets you build your own machine learning models for things like figuring out customer sentiment, classifying topics, or pulling out keywords. It’s a solid option if your team has a bunch of different text analysis projects and not just one specific goal.

What I liked: The interface is clean and easy to figure out, even if you’re new to this. They also have a library of pre-built models which can give you a head start. It connects nicely with tools like Google Sheets, Zapier, and Zendesk.

What to consider: You still have to do the work of gathering, cleaning, and manually labeling data to train your models. It feels more like a toolkit for various jobs rather than a complete solution for one specific problem like automating support.

Pricing: You’ll need to contact them for custom pricing.

3. Lang.ai: A Labelf alternative for CX automation

Lang.ai is another strong player in the customer support world. It’s a no-code AI platform that helps you dig up insights from support conversations and automate parts of your workflow. Its real strength is accurately tagging and routing tickets based on what the customer wants, which can save agents a lot of manual effort.

What I liked: It has tight integrations with big helpdesks like Zendesk and Salesforce. It’s really good at automatically adding tags to new tickets, which helps a lot with routing and analytics. It helps you see the "why" behind why customers are reaching out.

What to consider: It’s more geared towards helping agents by tagging and routing tickets, rather than being a fully autonomous agent that closes tickets on its own. The setup can also be a bit more work than a plug-and-play tool.

Pricing: Not listed publicly; you have to book a demo.

4. Label Studio: An open-source Labelf alternative

For teams with developers who want full control, Label Studio is a great pick. It’s a popular open-source tool for data labeling that’s incredibly flexible and works with all sorts of data, including text. Think of it as the ultimate DIY alternative; it gives you a solid framework to handle the labeling part of your AI project.

What I liked: It’s free and open-source, which means you can do pretty much anything you want with it. The interface is super flexible, so you can set it up for even the most complicated labeling jobs. Plus, it has a big community behind it.

What to consider: This is a full-on DIY project. You have to host it yourself and have the technical skills to set it up and keep it running. And remember, it only does the labeling part. You’re still on the hook for building, training, and deploying the actual model, which is a huge job.

Pricing: Free. They also have a paid Enterprise version with more features and support.

5. Snorkel AI: An enterprise-grade Labelf alternative

Snorkel AI has a unique take on data labeling. Instead of having people manually tag everything, it uses programmatic labeling, where data scientists write code to label huge datasets automatically. It’s a seriously powerful platform for enterprise teams that are building advanced AI applications on their own data.

What I liked: It can label massive datasets incredibly fast. This allows teams to build some really complex and accurate AI models. It’s definitely built for serious, large-scale AI work.

What to consider: The learning curve is steep. It’s made for data scientists and ML engineers, not your average business team. It’s not a low-code tool you can just pick up and use for a simple task.

Pricing: Available through custom enterprise plans.

6. DeepOpinion: A Labelf alternative for document automation

DeepOpinion is a no-code AI platform built to automate workflows that deal with a lot of text and documents. It’s great at jobs like pulling information from invoices, sorting contracts, or processing forms. If your main headache is dealing with structured documents instead of conversational text, DeepOpinion is worth a look.

What I liked: It’s really strong when it comes to document processing and pulling out data. The no-code interface lets business users train models for specific document types without needing to code. It’s great for getting rid of repetitive back-office work.

What to consider: It’s not really built for conversational AI, like support chatbots or ticket automation. Its main thing is internal processes, not so much customer-facing interactions.

Pricing: Not publicly available; you have to reach out to their sales team.

So, how do you pick the right Labelf alternative?

The best tool really just depends on your goal. You need to ask yourself one question: "Am I trying to build a custom AI model, or am I trying to solve a business problem?"

If you’re on a data science team and need total control for building a model from the ground up, something open-source like Label Studio or an enterprise tool like Snorkel AI makes sense. If you’re a business team that needs a flexible toolkit for different text projects, MonkeyLearn strikes a nice balance.

But if you’re on a support or IT team, and your main goal is to cut down on tickets and make things more efficient right now, you don’t really need a model-building kit. You need a solution that works out of the box. That’s where a tool like eesel AI really stands out. It skips the whole manual building process and just gives you a working AI agent that learns from your existing data.

The fastest way to get automated support with Labelf alternatives

While Labelf and many of the tools on this list give you the raw materials to build an AI, they leave the hard work to you: labeling tons of data, training a model, and plugging it into your systems. That whole process can take months and usually requires someone with very specific skills.

For teams that just want results, there’s a more direct way. Instead of building from scratch, you can use a solution that’s already built to do the job. eesel AI was designed for exactly that. It takes all the knowledge you already have, in past support tickets, your help center, your internal docs, and turns it into an AI agent that can resolve issues and answer questions, freeing up your team for more important work.

If you’re curious to see how quickly you can get an AI working for your business, you can try eesel AI for free and see it for yourself in just a few minutes.