micro1 vs Scale AI: Which Is Better for Selling Company Data?

The short answer

micro1 and Scale AI both work with established companies that have years of operational data. They want records that show how employees solve problems, make decisions, and complete work, not just a pile of customer contacts or files.

micro1 has the clearer published route for companies with 30 or more employees and provides a standard data-license agreement that sellers can read. Scale AI prefers companies with 40 or more employees and emphasizes ongoing partnerships, where a business can approve and license new data packages over time. Neither threshold guarantees acceptance, and Scale considers companies case by case.

Neither publishes enough completed-deal evidence to say which consistently pays more. If your company could qualify for both, ask each to price the same historical records, then compare any ongoing work and usage rights separately.

Comparing micro1 with Mercor instead? See micro1 vs Mercor. Comparing micro1 with Handshake AI? See micro1 vs Handshake AI.

micro1 vs Scale AI at a glance

Which businesses are a better fit?

micro1 describes its preferred partner narrowly: at least 30 employees, established documentation, mature operations, and high-quality data that reflects how work gets done. Its demand is strongest for companies in the United States, United Kingdom, and Canada with predominantly English records. Its examples span software, customer service, finance, logistics, legal operations, and sales.

Scale asks for years of usable records, real workflows rather than demos or synthetic examples, and specialized expertise. It also wants a decision-maker inside the business who can approve each data package. Its industry list includes software, manufacturing, insurance, field services, freight, hospitality, professional services, and others. Forty employees is its preferred starting point, not an absolute cutoff.

For a 30-to-39-person operating business, micro1 is the more obvious first application based on the published eligibility criteria. Scale may still be worth asking, especially if the company has unusually deep or specialized records. At 40 employees or more, company size alone does not tell you which is a better fit.

Think in workflows rather than software brands. A plumbing company might have years of service requests linked to diagnosis, quotes, dispatch, completed work, and callbacks. A software company might be able to connect support tickets to internal discussion, code changes, and resolutions. Those sequences are much more informative than a raw export of emails or files, and they are what both programs want.

Which pays more, micro1 or Scale AI?

There is no useful public answer for an individual company. micro1 lists $100K+, $500K+, and $1M+ partnership categories and explains that pricing depends on data quality, uniqueness, volume, and relevance. Its online estimator is explicitly not a binding quote.

Scale displays a $10K–$1M+ illustrative partnership range and ties that value to the frequency of data packages. That figure is not a published standard rate, and it does not say what an ordinary seller earns upfront or annually.

Those headline numbers cannot be compared directly. The key question is what each company wants in return. One proposal might cover a defined historical dataset. Another could require scheduled updates, repeated reviews, additional fields, or broader reuse rights. Before judging the price, establish what is actually being sold.

Is Scale AI better for recurring revenue?

Scale makes ongoing licensing central to its program. Partnerships can start small and expand, with an opportunity for recurring revenue as new packages are approved. That model suits an operating company that continuously generates useful records.

But opportunity is not a payment guarantee. An agreement would still need to specify whether the buyer commits to future purchases, how each package is priced, who pays for preparation, and whether either side can stop the arrangement.

micro1 is not strictly a one-time buyer. Its published program mentions ongoing participation across multiple teams and business functions. Its standard master agreement, however, ties the actual price and payment terms to an individual order form. A series of paid deliveries is possible, but future revenue must be written into the deal rather than assumed.

A fair comparison separates the value of your existing records from the value of future updates. Otherwise a seemingly larger partnership may also involve substantially more work over several years.

When would you get paid?

micro1 has published baseline rules. Under its October 7, 2026 Data License Master Terms, it has 30 business days after delivery to reject records in writing. If it does not do so, the records are treated as accepted. The seller can then invoice, and the signed order form determines how long micro1 has to pay. The 30-business-day review window is not the same thing as a payment deadline.

Scale's public partnership page does not specify a comparable acceptance period or cash-payment deadline. It describes the process and possible revenue, but the date the money is actually due has to come from the seller agreement.

For either company, get the full sequence in writing: delivery, review, rejection or rework, acceptance, invoice, and payment. If the plan involves repeat deliveries, ask whether each package has its own payment clock and whether money depends on Scale or micro1 selling that package to another AI customer.

What happens to your data after the deal?

This is the most significant difference in what the two companies publish.

micro1's master terms say the business keeps ownership of its underlying data. Once a portion is accepted and paid for, however, micro1 receives a worldwide, permanent, non-exclusive license to use and modify it, train and test AI, and sublicense it through multiple parties. The terms impose limits, including restrictions on direct competition with the seller, re-identifying people, and resale of the unchanged data as a standalone product.

The agreement also says deleting supplied records does not require deleting or retraining models or derivatives built from data micro1 accepted and paid for. In other words, ending a relationship does not necessarily undo prior AI training.

Sellers should also examine two obligations: micro1's warranty concerning certain previous licenses of the same data, and a clause that can require repayment if the seller materially misdescribes the dataset. The signed order form can change the baseline terms, so ask to review the complete agreement, not just the website.

Scale publicly promises seller approval of every package, de-identification before the package leaves the seller's environment, and controls over what is shared. Those are important process commitments. They do not establish how long downstream parties can use the processed data, whether a license is exclusive, or what happens to trained models after termination. Only a Scale seller agreement answers those questions.

How do privacy and preparation compare?

Scale describes a process in which its team de-identifies data inside the seller's environment, with on-premises and virtual private cloud options, an audit trail, and approval before each package is released. Identities and raw system credentials do not leave the company. If that approach appeals to you, ask to see its technical access requirements and the written security commitments that would apply to your systems.

micro1 describes an agreed data scope, personal-information removal where relevant, restricted access, isolated processing, and sample review. Its master terms also specify security obligations and categories of records that must be excluded unless the order form expressly permits them, including protected health information, financial account details, and children's data.

Neither company's public assurances mean that your business automatically has the right to license every record it stores. Customer contracts, employee information, privacy law, and third-party intellectual property still need review. A safe first step is a nonconfidential description of what the records contain, not a raw upload.

Which one should you choose?

Start with micro1 if you have 30+ employees, substantial English-language operating history, and want to evaluate a defined dataset against published baseline contract terms.

Start with Scale AI if you have deep, connected workflows and the capacity to review repeat data packages. Its model suits a company that wants a longer relationship, provided the ongoing economics are real.

Approach both if you meet their criteria and have a dataset that could interest either. Ask for comparable pricing on the same historical package first, then compare updates, additional rights, and extra seller work separately.

If neither is willing to put scope, payment and usage rights in writing, there is no reason to hand over your records. And if you cannot establish that your company has the necessary permissions, resolve that before pursuing a deal.

Five questions to ask each company

1. What exact systems, years, records, and exclusions does your price cover?
2. What cash is committed, when is it due, and what allows you to reject or delay payment?
3. Are future updates optional, and how will additional packages be priced and approved?
4. Who can receive or sublicense the data, how long may it be used, and can we license it elsewhere?
5. What must we warrant about customer, employee, and third-party rights, and who bears the risk if a claim arises?

Write down the answers for both companies before comparing the headline amounts. Different scope or rights make even two cash offers difficult to compare.

Already have an offer?

Use the free DataDeals offer-comparison guide and worksheet to line up the money, timing, rights, and unanswered questions. If you want help assessing an offer, request a free offer review. You can start with a summary; don't send raw records or confidential systems access.

About DataDeals: DataDeals connects businesses with AI companies interested in licensing their operational data. Our service is free for sellers. We earn referral fees from participating buyers when successful deals are completed. We disclose these arrangements before making introductions. We never charge sellers a commission or deduct a fee from their payments. Our comparison is based on public company materials, not private offers from these programs.