Pick by the kind of data you have, not by the platform's marketing. Tabular business predictions (churn, lead scoring, forecasting) go to Obviously AI, H2O or DataRobot. Images go to Teachable Machine for a quick test and Roboflow or Vertex AI AutoML for anything real. Text classification is increasingly not worth a dedicated platform at all. Two tools that most 2026 roundups still list are no longer sensible picks, and that is where this guide starts.
How this guide was put together
This is a buyer's guide, not a benchmark. Nobody here ran the same dataset through eight platforms and timed them, and any roundup that hands you a star rating for "accuracy" without publishing the dataset is inviting you to trust a number it made up. What follows is drawn from each vendor's current documentation and published pricing, checked in August 2026, plus the thing most lists skip: whether the product still exists.
Where pricing has moved behind a sales call, that is stated rather than guessed at.
Two platforms to cross off your shortlist first
Lobe is gone
Microsoft's drag-and-drop image classifier still appears near the top of most "best no-code ML" lists, usually described as free and beginner-friendly. Development stopped in 2021, its image classification model was deprecated in January 2023, and the desktop apps are no longer distributed. If a guide published this year recommends Lobe, the author did not check.
The replacements are Teachable Machine for a quick test and Roboflow or Google's Vertex AI AutoML for anything you intend to deploy.
Akkio moved upmarket
Akkio was for years the honest answer to "non-technical team, first model, small budget", at a self-serve price around $49 per user per month. As of July 2026 it no longer publishes self-serve pricing: the site is a single Enterprise AI Analytics Platform tier aimed at media agencies, behind a contact-sales form.
It may still be the right tool for you. It is no longer the right first tool for a two-person marketing team, and lists still quoting the $49 tier are describing a product you cannot buy.
What no-code ML does, and where the line is
These platforms automate the modelling, not the thinking. Upload a table, name the column you want predicted, and the platform tries a range of algorithms, tunes them, and hands back the best one with a performance estimate. That genuinely used to take a data scientist a week.
What they do not do:
- Decide whether the question is worth asking. A model that predicts churn with 92 percent accuracy is worthless if nobody has decided what happens when it fires.
- Fix your data. Every one of them will happily train on a leaky column that quietly contains the answer, and report spectacular accuracy that collapses in production.
- Tell you the model is wrong in a way that matters. Overall accuracy hides the case where the model is excellent on the 90 percent of customers who never churn and useless on the 10 percent who do.
That last one is the most common way a no-code ML project quietly fails.
For tabular business predictions
Churn, lead scoring, demand forecasting, fraud flags. This is where most business ML lives and where these tools are genuinely good.
Obviously AI
Connect a data source, state what you want predicted, get a model in minutes. The fastest path from a spreadsheet to a prediction, and the right choice for answering "is there a signal here at all" before anyone commits budget. Less suited to models you intend to run and monitor for a year.
DataRobot
Automated ML with the parts enterprises actually get audited on: model governance, monitoring, drift detection, documentation. The interface is no-code but the concepts are not, so it suits a data analyst more than a marketer. Enterprise pricing, quoted per deal.
H2O AutoML
Open source and free, competitive with commercial AutoML on standard tabular tasks. You need someone comfortable with a notebook or a running server, so it is the strong option for a team with one technical person and no budget rather than for a team with neither.
The platform produces a number. Hannah produces the method around it: which pipeline inputs qualify, what the confidence interval means for the commit, and how to present a forecast leadership will not quietly discount.
View Hannah →For text
The awkward question first
Dedicated text classification platforms were built when custom classifiers were the only way to sort support tickets by intent or tag reviews by topic. A general model can now do that from a written instruction, with no training data at all. For anything under a few thousand documents a month, a well-written prompt run in batches will usually beat the effort of labelling a training set.
The case for a dedicated classifier is still real at high volume, where per-call cost and latency matter, and where you need the same decision made identically every time.
MonkeyLearn, now part of Medallia
MonkeyLearn was acquired by Medallia in 2022 and its technology folded into Medallia's Experience Cloud. If you find it recommended as a standalone self-serve product at a fixed monthly price, treat that as out of date and check what Medallia actually sells today before planning around it.
A predictions export is not an answer. Clara sets the question first, checks whether the data can support it, and turns the file into something a non-technical stakeholder can decide on.
View Clara →For images
Teachable Machine
Google's free browser tool. No account, no install, a working image, sound or pose classifier in about fifteen minutes using your webcam or uploaded files. It is the right way to find out whether your idea is even plausible. It is not a production system and does not pretend to be.
Roboflow
The practical choice once the toy version works: dataset management, labelling, augmentation, training and deployment in one place, built specifically for computer vision. This is where most teams end up after Teachable Machine.
Google Vertex AI AutoML
Custom image classification and object detection with automated architecture search, inside Google Cloud. Worth it if you are already on GCP, have a large labelled dataset and need enterprise deployment. Heavy for anything smaller.
Clarifai
Computer vision with a library of pre-built models for common tasks such as object recognition and content moderation, plus no-code custom training and an API. Strongest when a pre-built model already covers your task, because you skip labelling entirely.
Choosing, in one pass
- Spreadsheet, want a prediction, want it today: Obviously AI.
- Spreadsheet, one technical person, no budget: H2O AutoML.
- Regulated, audited, needs monitoring and documentation: DataRobot.
- Images, testing an idea: Teachable Machine.
- Images, building something real: Roboflow, or Vertex AI AutoML if you are already on Google Cloud.
- Images, and a pre-built model may already cover it: Clarifai.
- Text, moderate volume: try a prompt before you buy a platform.
One rule underneath all of it: pick the smallest tool that answers the question you have now. The cost of outgrowing a free tool is a weekend. The cost of an enterprise contract you did not need is a year.
What DataRobot is really selling is discipline: framing the question, spotting the leaky column, choosing the metric that matches the decision, designing a test you can trust. Leila brings that part.
View Leila →The part no platform solves
Every tool above automates the middle of the job. The expensive mistakes live at both ends: choosing a target variable that does not map to a decision anyone will make, and handing a stakeholder a probability column with no recommendation attached.
If your team has no data function, that gap is the thing to close first, before the platform choice matters at all.
In summary:
Skip Lobe, it no longer exists, and check Akkio's current terms before planning around its old $49 tier. For forecasting, pair your platform with Hannah (Sales Forecasting); to turn an export into a decision, Clara (Data Analyst); for the statistical judgement an enterprise contract is really selling, Leila (Data Scientist). $14.99, $14.99 and $19, all work in Claude, ChatGPT or any AI chat, 30-day money-back guarantee.
Platform status and pricing checked August 2026. Vendors change terms without notice, so confirm current pricing on the vendor's own site before committing budget.
Ready to put this into practice? Browse Tech and Development skills for Claude and ChatGPT, or explore all Claude skills and the prompt library.