Gratefully vs Dataro: Which AI Fundraising Tool
Dataro predicts who will give and starts at $15,000 a year plus $0.10 per active donor, earning its cost on large files with volume appeals. Gratefully prioritises a portfolio and preserves donor knowledge at $4,800 a year for five seats. Different jobs, not competing products.
These two products are compared constantly and they do different jobs. Working out which job you need takes about five minutes and saves a great deal of money, because the entry prices differ by more than three times.
The short version: Dataro predicts who will give. Gratefully remembers why they gave and tells a person what to do about it today.
The essential difference
| Gratefully | Dataro | |
|---|---|---|
| Core job | Prioritisation and organizational recall | Predictive scoring |
| Main output | A ranked daily portfolio with reasoning, and answers about donors | Propensity scores pushed into your CRM |
| Who acts on it | A gift officer, one donor at a time | Whoever builds your appeal segments |
| Published entry price | $4,800 a year, 5 seats | From $15,000 a year, plus $0.10 per active donor |
| Best fitted to | Small development teams, major gifts, moves management | Large files with volume appeal programmes |
| Free trial | Four weeks, no card | Not published |
What Dataro does well
Dataro models your database and scores every record for likelihood to give, to upgrade, to lapse and to leave a bequest. Those scores go back into your CRM so you can segment on them.
The value is concentrated in appeal selection, and it is easy to measure. If you mail 60,000 people and modelling lets you mail 40,000 for the same income, the saving is immediate and appears in the next campaign report. That is an unusually clean return for software.
Bloomerang has announced a partnership bringing Dataro’s predictive modelling into its platform from July 2026, which will make this the path of least resistance for many Bloomerang users.
Where it does not fit: the price floor. At $15,000 a year plus $0.10 per active donor, a 50,000 record file costs $20,000 before you have done anything. It also needs volume to be actionable, because a propensity score is only useful when you are choosing who to include in something. If you are not running appeals at scale, you are buying a ranking you will not use.
What Gratefully does well
Gratefully builds a knowledge layer over your existing CRM, reading Bloomerang, Salesforce, Raiser’s Edge NXT, Mailchimp and Google Workspace, plus documents, notes and email. It then answers questions about donors with citations back to the source record, and produces a ranked list each morning of who needs attention and why.
The differentiated part is not the ranking, which several products do. It is that the context behind a relationship becomes searchable and stays with the organization when the person holding it leaves. Given median tenure in a fundraising job of two years against major gift relationships that take longer to mature, that is a structural problem rather than an occasional one.
Where it does not fit: organizations without a populated CRM, without defined portfolios, or under roughly $1m where $4,800 is a programme line. It also has no published independent security certification, which a board may reasonably ask about. Our full review sets out the fit and the limits in detail.
Choosing between them
| If this describes you | Look at |
|---|---|
| Tens of thousands of records, regular mail or email appeals | Dataro |
| One to five people doing donor work, portfolios, major gifts | Gratefully |
| Your problem is who to include in the next appeal | Dataro |
| Your problem is that nobody knows why a donor lapsed | Gratefully |
| Staff turnover keeps resetting relationships | Gratefully |
| You want a measurable lift on a defined campaign | Dataro |
| Budget under about $10,000 a year for this | Gratefully, or neither |
| No CRM history, or under a few hundred donors | Neither, yet |
What each one asks of you
Cost is the visible difference. What the product requires from your organization is the one that decides whether it works, and it is rarely discussed.
| Requirement | Gratefully | Dataro |
|---|---|---|
| Minimum data history | Two years of consistently entered records, plus documents | Enough gift history to model, which means a substantial file |
| Who has to change their habits | Gift officers, daily | Whoever builds appeal segments, per campaign |
| Failure mode if ignored | A ranked list nobody opens | Scores sitting unused in a CRM field |
| How you know it worked | Harder. Relationship outcomes are slow and attribution is contested. | Easier. Compare campaign response against previous selection. |
| Effort to prove value in 90 days | Moderate | Low, given a campaign in that window |
The last two rows deserve weight in a board decision. Dataro’s benefit is measurable in a way Gratefully’s largely is not, because a campaign gives you a clean before and after and a portfolio does not. That is a genuine advantage for Dataro when you have to justify the spend to trustees, and it is separate from which product is more useful.
The common failure for both is the same and it is not technical. Software that produces a recommendation nobody is required to act on produces nothing. Decide who opens it, when in their week, and what they do with it, before you sign anything.
The third option most organizations should consider
Neither. A general purpose assistant costs nothing at the entry tier or about $20 a month and covers drafting, summarising and rewriting, which sector survey data suggests is where roughly two thirds of nonprofit AI use actually sits.
That is not a consolation prize. For an organization with a few hundred donors, no defined portfolios, or a database with gaps in it, it is the correct answer, and the money not spent on a five figure platform funds something that will do more. Both products here read your records. Neither can invent the history nobody entered.
They are not mutually exclusive
A large organization could reasonably run both, because scoring a file and briefing a gift officer are genuinely different tasks. Most organizations reading this cannot justify either at first, and the honest sequence is worth stating.
Get the CRM current and consistently used. Decide who owns donor relationships. Use a general purpose assistant for drafting, which costs nothing and covers most of the realistic AI benefit. Then, if you have a development function and a database with real history, look at this category.
Both tools are trying to answer the same underlying question, which is who to contact next and why. The discipline that question belongs to, software aside, is moves management.
What to test, whichever you trial
Run these on your own data, not a demo set.
Check something you already know. Ask about a donor whose history you know in detail and verify the answer, then follow the citation to the record. A tool that is confidently wrong about a case you can check is confidently wrong about the ones you cannot.
Reconcile one number by hand. Take a reported total and check it against your CRM.
Give it your worst data. Performance on well documented donors tells you little. What it does with thin records tells you everything.
Ask what happens at the end. Can you export everything, what is retained after cancellation, and what happens to any derived layer. Get it in writing.
Establish the exit before the entry. The two products leave you in different positions if you stop paying, and this is worth settling in writing at the start rather than discovering at renewal. Dataro writes scores into your CRM, so what it produced is in a system you control. Gratefully builds a derived knowledge layer, and the question of whether an export includes that layer or only the records you supplied is a materially different answer. The vendor states data can be exported at any time with no lock in. Get the specifics: the format, whether the derived layer is included, and the retention period after cancellation.
For the wider category, including the general purpose assistants that are the right answer for most small organizations, see AI tools for nonprofit fundraising.
Questions people ask
What is the difference between Gratefully and Dataro?
They solve different problems despite both being sold as AI fundraising tools.
Dataro is a prediction engine. It scores your database for likelihood to give, upgrade, lapse or leave a bequest, and pushes those scores into your CRM so you can segment appeals on them. It is built for volume.
Gratefully is prioritisation and recall. It builds a knowledge layer over your CRM and documents, answers questions about donors with citations, and produces a ranked daily list for a gift officer with reasoning attached. It is built for relationship work.
Volume appeals point to Dataro. Portfolios and major gifts point to Gratefully.
Which is cheaper, Gratefully or Dataro?
Gratefully, by a considerable margin at the entry point. It publishes $400 a month billed annually, which is $4,800 a year including five seats, or $500 monthly.
Dataro publishes a floor of $15,000 a year for its Essentials tier plus $0.10 per active donor, and $25,000 for Growth plus $0.12 per donor. On a 50,000 record file that is $20,000 before anything else.
Both figures were confirmed against each vendor's own pricing page on the review date shown on this page. Verify before budgeting, because pricing in this category changes without announcement.
Can you use Gratefully and Dataro together?
Yes, and for a large organization it is a defensible combination, because scoring a file and briefing a gift officer are different tasks that do not overlap much.
For most organizations it is not a realistic question. The combined cost is above $20,000 a year, and an organization that can absorb that generally has an advancement operation with other priorities to fund first.
If you are choosing one, choose by which problem is actually costing you money: appeal inefficiency, or donor knowledge that keeps walking out of the door.
Does Dataro work with Bloomerang?
Yes, and the connection is getting closer. Bloomerang announced a partnership bringing Dataro's predictive modelling into the Bloomerang Giving Platform from July 2026, which makes it the path of least resistance for Bloomerang users considering scoring.
Convenience is a legitimate factor and it is not the whole decision. The price floor still applies, and the underlying question of whether you run appeals at enough volume for propensity scores to be actionable does not change because the integration is easier.
Which is better for a small nonprofit?
Between the two, Gratefully, on price and on fit. Dataro's published floor of $15,000 a year rules it out for most organizations under a few million in revenue, and its value depends on volume appeals that small organizations are usually not running.
The more useful answer is that many small nonprofits should buy neither yet. If your CRM does not hold two years of consistently entered history, or nobody owns donor relationships as a defined part of their job, both products will produce confident output from thin data.
Start with a general purpose assistant for drafting at a free tier or about $20 a month, and revisit this when there is something for it to work on.
Do either of them replace a CRM?
No. Both sit on top of a donor database you already run and neither is a system of record.
Dataro pushes scores back into your CRM. Gratefully reads your CRM along with documents and email and states that it does not migrate or duplicate your data. In both cases your donor records stay where they are.
If you are shopping for something to hold gift history, receipts and contact records, you are shopping for a CRM, and that decision comes first and should be lived with for a couple of years before adding a layer like either of these.
How accurate are AI donor predictions?
Accuracy depends far more on your data than on the vendor. Both products read what your organization has actually recorded, and neither can recover history that was never entered.
Judge it empirically during a trial rather than from published case studies. Ask about donors whose history you know in detail, verify the answers, and follow the citations to the underlying records. Separately, reconcile one reported total by hand.
Pay particular attention to what happens with thin records. Performance on well documented donors tells you very little, because those are the ones you did not need help with.
What should we ask before buying either?
Five things, in writing. Can we export everything, and in what format. What happens to any derived layer if we stop paying. Is our data used to train models, and can we opt out. Where does each answer's citation point, so a claim can be verified against the source record. And is there an independent security audit such as SOC 2, or a timeline for one.
That last question matters more than it used to and much of this category cannot yet answer it with a certification. A stated timeline is reasonable for a young product. No answer at all is not.
This is reference information, not legal or tax advice. Rules vary by state and change over time. For a decision that carries real consequences, check the current text at irs.gov or your state registry, and talk to a nonprofit attorney or CPA.