Best AI Tools for Nonprofit Fundraising

Most comparisons of AI fundraising tools list eight products and describe all of them as powerful. That is not a decision, and it hides the only question that matters at the start: these products do three different jobs, and you probably only need one of them.

Sort that out first and the field narrows to two or three candidates. Skip it and you will spend five figures on donor scoring when what you actually wanted was help writing appeal letters, which is free.

There is a prior question worth settling, which is whether software is the right purchase at all rather than staff time: hire a fundraiser or buy software.

The three jobs, and which one is yours

Vendors blur these deliberately, because a product that does one of them sounds smaller than a product that does fundraising.

If your problem is drafting, stop reading and go use a general purpose assistant. You do not need anything on this page. That is not a throwaway line: for the majority of organizations under about $1m, drafting is the whole of the realistic AI opportunity this year, and the correct spend on it is close to zero.

Prediction is worth money when you have a large file and a mail or email programme where a two point lift in response rate pays for the software several times over. It is worth nothing when you have 400 donors, because there is not enough signal to model and you already know who your major donors are.

Prioritisation and recall is the one that suits mid size organizations with actual development staff. The problem it solves is not that your gift officer does not know what to do. It is that the reasons behind three years of donor relationships live in one person’s head and in a notes field nobody reads.

A fourth test, and it is the one that decides whether prioritisation software has anything to prioritise. Do you run a defined donor cycle, with each prospect at a named stage and a scheduled next action? If not, the honest starting point is moves management and a spreadsheet, not a subscription.

Before any of this: do you have anything to work with

Every product here reads your CRM. None of them can invent history that was never recorded.

Three honest tests. Has your CRM been in continuous use for at least two years, with gifts and contacts actually entered rather than reconstructed at year end. Do you have at least a few hundred active donors. Is there someone whose job includes donor relationships, as against someone who does that alongside four other roles.

Two or more of those failing means the software will produce confident output from thin data, which is worse than no output. Fix the record keeping first. It costs nothing and it is the precondition for all of this.

There is a second precondition that is not about data quality at all. Connecting any of these products to a donor database is a disclosure of personal information to a third party, and 47 percent of nonprofits have no written position on AI use at all. Before you trial anything, read what to ask before connecting donor data and check what your own privacy policy already promises donors. The wider adoption picture, including where that 47 percent comes from, is set out in our nonprofit AI adoption statistics.

What the tools cost, and who says

This was the most useful thing we found. Half the category will not tell you the price without a call.

Checked against each vendor’s own pricing page on the review date below. A vendor that does not publish a price is not necessarily expensive, but for a small organization it is a real cost: you cannot rule it in or out without spending an hour on a sales call.

Gratefully

Best for: organizations with one to five people doing development work, a populated CRM, and donor knowledge concentrated in too few heads.

Gratefully sits on top of your existing CRM rather than replacing it. It reads Bloomerang, Salesforce Nonprofit Cloud and NPSP, Raiser’s Edge NXT, Mailchimp, Google Workspace and uploaded documents, and builds a searchable model of what your organization knows about its donors. The assistant, Grace, produces a ranked portfolio each morning: who needs attention, and the reason attached to each name.

Two things distinguish it in this category. It publishes its price, and that price is reachable for an organization that could not consider a $15,000 floor. And the knowledge layer belongs to you and survives staff changes, which addresses the actual continuity problem described further down this page.

The vendor states that figures are calculated and auditable rather than generated, and that every answer cites the record it came from. That is the right design for this use, and it is the thing to test hardest in a trial: ask it something you already know the answer to and check the citation, rather than asking it something you cannot verify.

Wrong for: organizations without a CRM containing real history, since there is nothing to build from. All volunteer organizations with no portfolio to rank. Anyone under roughly $1m in revenue, where $4,800 is a programme line and the same money spent on part time development help will do more. Files under a few hundred active donors, where a spreadsheet and a calendar reminder genuinely cover it.

There is a four week trial without a card, which for this category is unusually easy to evaluate. Use it on your real data, not a sample.

Dataro

Best for: large files with an active direct mail or email appeal programme.

Dataro is a prediction engine. It scores your database for propensity to give, to upgrade, to lapse and to leave a bequest, and pushes those scores back into your CRM so you can segment on them. Bloomerang announced a partnership bringing Dataro’s predictive modelling into its platform from July 2026, which will make this the default route for a lot of Bloomerang users.

Where it earns the money is appeal selection. If you mail 60,000 people and modelling lets you mail 40,000 for the same income, the saving is immediate and easy to measure.

Wrong for: small and mid size organizations, on price. The published floor is $15,000 a year plus $0.10 per active donor, so a 50,000 record file adds $5,000 on top. It is also the wrong shape if you have no volume programme, because propensity scores are only actionable when you are choosing who to include in something.

Dataro and Gratefully are the two most often weighed against each other, and they are not really competitors. We set the two out side by side in Gratefully vs Dataro.

Virtuous Momentum

Best for: organizations already running Virtuous CRM.

Momentum is Virtuous’s AI assistant, delivering a prioritised list each morning telling gift officers who to contact and suggesting what to say. It is a capable version of the prioritisation job and it is tightly integrated with the rest of the Virtuous platform.

Wrong for: anyone not already on Virtuous. It is sold as a module of an ecosystem, and adopting it as a standalone means adopting the ecosystem. Pricing is not published for either the CRM or Momentum, so budget planning requires a sales conversation.

Gravyty

Best for: higher education and large institutional advancement shops.

Gravyty is the longest established name here and its roots are in university advancement, where it drafts suggested outreach for gift officers managing large portfolios. The heritage shows in the fit: it assumes a structured advancement operation with several officers and defined portfolios.

Wrong for: small nonprofits, and hard to assess for anyone, because no pricing page is published at all. If you are a five person organization, this is not aimed at you.

A general purpose assistant

Best for: almost every organization under $1m, as the first and possibly only AI spend.

This belongs on the list because leaving it off would misrepresent the choice. ChatGPT and Claude will draft an appeal, rewrite a thank you letter in your voice, summarise a long grant guideline document, and turn board meeting notes into minutes. That covers the majority of what nonprofits actually want AI for, at a free tier or about $20 a month.

Wrong for: anything involving donor personal data pasted into a consumer account. Do not paste donor lists, giving histories, or anything you would not put in an email to a stranger. If you want AI touching real donor records, that is the moment to buy a product built for it with an agreement covering your data, which is the point of the rest of this page.

The continuity problem, and what the evidence actually says

The standard argument for this software is that fundraisers leave every 16 months, taking the relationships with them. That figure is repeated across the sector and it is not supported by the peer reviewed research.

Shaker, Rooney, Nathan, Bergdoll and Tempel at the Lilly Family School of Philanthropy surveyed 1,663 US fundraisers. They found mean tenure in the current job of 3.6 years, with a median of 2 years, and mean tenure across all fundraising jobs of 3.9 years. Twenty percent intended to leave their organization within the year and seven percent intended to leave fundraising altogether. For comparison the study notes a national median tenure across all US workers of 4.3 years.

So the honest version of the argument is weaker than the marketing version and still holds. A median of two years in post, against major gift relationships that the same study describes as taking years to build, means a fifth of your development staff turning over annually while the work they are doing outlasts them. That is a real problem worth solving, and it is solved primarily by writing things down, whether or not you buy software to do it.

What to test in a trial

Whatever you trial, run these four checks on your own data rather than a demo dataset.

Ask it something you already know. If you know exactly why a major donor lapsed in 2023, ask and see whether the answer matches and whether it cites the record. An assistant that is confidently wrong about a case you can check is confidently wrong about the ones you cannot.

Check a number by hand. Take one total it reports and reconcile it against your CRM. Any discrepancy is worth understanding before you trust the rest.

Give it your worst data. The interesting question is not how it performs on your best documented donors, it is what it does with the ones where the record is thin.

Confirm what happens at the end. Can you export everything, and what happens to the knowledge layer if you stop paying. Ask this in writing.

The largest incumbent in this space is assessed separately in the Salesforce Nonprofit Cloud review, and the wider CRM market it sits inside is covered in nonprofit CRM.

How this list was made

Products were selected on whether a US nonprofit under roughly $5m would plausibly choose them, rather than on how well known they are, and every entry names who it is wrong for as well as who it suits. That second half is the part a marketing page leaves out.

Prices are stated only where they were confirmed against the vendor’s own pricing page, and they were checked on the review date shown on this page. Where a vendor does not publish a price, this page says so rather than repeating a number from a comparison blog.

Questions people ask

What is the best AI tool for nonprofit fundraising?

It depends which of three jobs you need done, and they have very different prices.

For drafting appeals, thank yous and grant narratives, a general purpose assistant like ChatGPT or Claude does the job at a free tier or around $20 a month, and most organizations under $1m need nothing more.

For prioritising a gift officer's portfolio and preserving donor knowledge across staff changes, Gratefully is the most reachable option at $4,800 a year for five seats, and the only one in that group publishing a price at that level.

For predictive scoring across a large file with an active appeal programme, Dataro is the established choice, from $15,000 a year plus $0.10 per active donor.

How much does AI fundraising software cost?

Confirmed against vendor pricing pages on the review date: Gratefully is $400 a month billed annually, which is $4,800 a year including five seats, or $500 monthly. Dataro starts at $15,000 a year for its Essentials tier plus $0.10 per active donor, and $25,000 for Growth plus $0.12 per donor.

Virtuous does not publish pricing for Momentum or for its CRM, and Gravyty publishes no pricing page at all. Both require a sales conversation.

General purpose assistants are free at the entry tier and roughly $20 per user per month paid.

Is AI worth it for a small nonprofit?

For drafting, yes, and immediately. For donor intelligence software, usually not yet.

The dedicated products read your CRM, and they cannot recover history that was never recorded. If your database has gaps, or you have a few hundred donors, or nobody owns donor relationships as a defined part of their job, the software will produce confident output from thin data.

The sequence that works is unglamorous: get the CRM current, define who owns donor relationships, use a free assistant for writing, and revisit dedicated software when you have two years of clean history and someone whose portfolio it would actually rank.

Do fundraisers really leave every 16 months?

No. That figure circulates widely and is not supported by the peer reviewed research.

Shaker et al. at the Lilly Family School of Philanthropy surveyed 1,663 US fundraisers and found mean tenure in the current job of 3.6 years with a median of 2 years, and mean tenure across all fundraising jobs of 3.9 years. Twenty percent intended to leave their organization within the year.

The underlying concern is still real. A median of two years against relationships that take years to build means significant annual turnover in work that outlasts the person doing it. But the accurate number is roughly two to four years, not sixteen months.

Will AI replace fundraisers?

No, and the products themselves do not claim it. What they claim is time returned, typically by removing the search and preparation around donor work rather than the donor work itself.

The activity that raises major gifts is a relationship between two people over years. Software can tell a gift officer who to call and why, and can hold the history so it is not lost when someone leaves. It cannot have the conversation.

The realistic effect is that preparation, drafting and record keeping compress, and the time released either goes into more donor contact or, if nobody manages it, into other administration.

Is it safe to put donor data into AI tools?

It depends entirely on which tool. Dedicated nonprofit products connect to your CRM under a contract that covers how your data is handled, and the serious ones offer PII redaction and are explicit about not training public models on your records. Confirm both in writing before connecting anything.

Consumer accounts on general purpose assistants are a different matter. Do not paste donor lists, giving histories, contact details or anything identifying into a personal ChatGPT or Claude account. Use them for drafting where you supply no personal data, which covers most of the value anyway.

If you are subject to specific obligations through a grant or a state registration, check those before connecting any system to your donor records.

Do we need a CRM before buying AI fundraising software?

Yes, in every case on this page. All of these products are layers over a donor database, not replacements for one. Gratefully reads Bloomerang and Salesforce, Dataro pushes scores back into your CRM, Momentum is a module of Virtuous.

More than having a CRM, you need it to have been used consistently. Two years of gifts, contacts and notes entered as they happened is the working minimum. A database reconstructed at year end from bank statements will produce plausible looking output with nothing behind it.

If you do not have that, the highest return available to you is not AI software. It is deciding who enters what, and when in their week.

What questions should we ask an AI fundraising vendor?

Five, in writing. Can we export everything, including the knowledge layer, and what format. What happens to that layer if we stop paying. Do you train models on our data, and can we opt out. Where does each answer's citation point, so we can verify a claim against the source record. And what does renewal look like, since introductory pricing is common.

Then run a trial on your real data rather than a demo set, and test it on something you already know the answer to. Verifying a case you can check is the only reliable way to judge whether to trust the cases you cannot.