Gratefully and Dataro: prediction ranks your file, we answer from it
In one sentence: Dataro ranks your donor file with trained predictive models. Gratefully answers questions about the donors already in it, citing the record each answer came from.
- What Dataro does well:
- Ranked propensity across a large file, four core models plus more than twenty specialist ones, sixteen published integrations, and a large public set of customer results.
- What Gratefully adds:
- Internal relationship context, answers traced back to your own records, donor numbers that are deterministic rather than model-generated, and a $0 starting point with no record limits.
Dataro predicts. Gratefully answers.
Dataro builds trained models on your donor file and returns ranked scores: who is most likely to give next, what they may be worth over time, which channel to use. The output is a prioritised list.
Gratefully builds a knowledge graph from the records, notes and documents your organisation already holds, and answers questions about them with the source record cited. The output is an answer with a reason attached.
If your problem is that you do not know who to call on Monday, Dataro solves it. If your problem is that you know who to call and nobody can remember why they give, that is a different purchase. Plenty of organisations want both, and there is no conflict in running them together.
What Dataro is, who backs it, and what it costs
What does Dataro do?
Dataro sells predictive AI for nonprofit fundraising. It sits on top of the CRM you already run rather than replacing it, which is the same architectural position Gratefully takes.
Four core predictive models come with every plan: Next Gift Likelihood, Recommended Channel, Lifetime Value Forecast and Estimated Age.
On top of those sit specialist models, more than twenty of them by their own count, tiered across the plans at three, six, and all. Named examples include Appeal Response, Lapsed Donor Reactivation, Major Gift Readiness, Mid-Value Readiness, Recurring Donor Churn Risk and Recurring Giving Likelihood.
Their current release, Dataro 3.0, adds four things: Insights, which shows the shape of a programme and then drills to the individual. Donor Research, which assembles a profile covering capacity, affinity, giving history, network and due-diligence checks. Playbooks, which turn predictions into ready-to-run sequences. And Audiences, which build groups from propensity and push them to the channel where you act.
That is a serious product and it is worth describing accurately rather than in outline.
Who owns Dataro?
Dataro is independent and venture-backed. It was founded by Tim Paris, who is CEO, David Lyndon, the CTO, and Chris Paver, the COO.
The company raised a $14.28 million Series A led by Blueprint Equity, announced earlier this year, with the stated purpose of accelerating growth in the United States while continuing to support charities in Canada, the United Kingdom and Australia. Their About page states 300 or more nonprofits across 20 or more countries.
Worth noting because it is the live question buyers ask about this category: Dataro has not been acquired. It is still run by the people who founded it.
How much does Dataro cost?
Dataro publishes a full rate card, and that deserves saying plainly, because almost nobody in this category does. When we priced sixteen nonprofit fundraising tools from vendor pages in August 2026, four published nothing at all. Dataro publishes plans, per-donor rates, a feature comparison and a cost estimator, and it is the more transparent choice for it. Figures below are from dataro.io/pricing, read on 11 September 2026.
| Plan | Annual platform fee | Per active donor | Users | Specialist models | Prospect credits |
|---|---|---|---|---|---|
| Essentials | From $15,000 | $0.10 | 5 | 3 | 300 a year |
| Growth | From $25,000 | $0.12 | 10 | 6 | 1,000 a year |
| Enterprise | Talk to sales | $0.14 | Unlimited | All | 3,000 a year |
An active donor, in their definition, is any individual with at least one gift in the past 24 months, measured as of the end of the previous quarter.
Dataro frames the cost this way on their own pricing page: "At 100,000 active donors, Dataro costs as little as $0.25 per donor per year. That's less than the postage on a single appeal letter." That is a fair framing at that size.
Their pricing page also carries an estimator, and it is the more useful number for most readers, because most organisations are not at 100,000 donors. At 10,000 active donors, their own tool returns this:
| Plan at 10,000 active donors | Platform fee | Donor fee | Total a year | Effective per donor |
|---|---|---|---|---|
| Essentials | $15,000 | $1,000 | $16,000 | $1.60 |
| Growth | $25,000 | $1,200 | $26,200 | $2.62 |
Both figures are true. Which one applies to you depends entirely on your file size, and that is the single most important thing to work out before shortlisting either of us.
Also included across plans: weekly two-way CRM sync, SOC 2 Type II certification, data residency in the US, UK or Australia, Meta and Google Ads connections, and dedicated onboarding. Most organisations are live in four to six weeks. There is no self-serve trial; every demo is scoped to your own data.
Is Dataro worth it?
They publish roughly twenty-five customer stories with named organisations and specific numbers, spanning Australia, Canada, the UK, the US, New Zealand, Switzerland and the Netherlands. A sample, in their words and on their figures:
- Bat Conservation International: appeal revenue up 160% on a smaller file
- The Leprosy Mission Great Britain: 819% ROI on 45% less mail
- Pancreatic Cancer UK: doubled net appeal revenue while mailing 65% fewer
- Save the Children Netherlands: response up 108%, mail file cut 66%
- Child Cancer Foundation: the top 5,000 lapsed donors delivered 87% of revenue
- UNICEF Switzerland and Liechtenstein: identified CHF 700,000 in future legacy income
Two honest notes on reading those. They are the vendor's own published results, not independently audited, which is normal for the category and true of our numbers too. And the pattern in almost all of them is the same: mail fewer people, raise the same or more. If that is the problem you have, the evidence base here is genuinely strong and we would say so.
What Gratefully costs
| Plan | Monthly | Annual | People |
|---|---|---|---|
| Free | $0 forever | - | 1 |
| Essential | $79 | $948 | 1 |
| Professional | $399 | $4,788 | 5 |
| Advanced | $799 | $9,588 | 10 |
Every new account starts on a 14-day trial of Advanced with no card required, and drops to the Free plan when the trial ends rather than locking. There are no record or volume limits on any plan, including Free.
The entry points are genuinely far apart, and it is worth understanding why rather than just noting it. Dataro trains models per organisation, which is real recurring compute and data science work, and it is priced accordingly. Gratefully does retrieval over data you already hold, which costs differently. Neither number is wrong for what it buys.
Where each tool fits
| Capability | Dataro | Gratefully |
|---|---|---|
| Core approach | Trained predictive models on your donor file | Knowledge graph over your records, notes and documents |
| Ranked propensity scoring | Four core models plus 20+ specialist models | Predictive donor scoring, explainable, refreshed nightly |
| Named models per outcome | Yes | No |
| Living donor segments | Not published | Yes, refreshed nightly and after every import |
| Ask a question about a named donor | Not the primary interface | Yes, conversational, grounded in your data |
| What its citations point to | Public sources: news, foundation records, annual reports, wealth databases | Your own records: notes, documents, gift history |
| Prospect research and wealth screening | Yes, US records only | No |
| Are the donor numbers model-generated? | Predictive scores are model outputs | Deterministic and auditable, not language-model generated |
| Staff-turnover handover | Not published | Handover dossiers, viewable and exportable |
| Published pricing | Yes | Yes |
| Entry price | From $15,000 a year plus per-donor fees | $0 |
| Free plan | No | Yes, with no record or volume limits |
| Trial | No self-serve trial | 14 days of Advanced, no card |
| Typical time to live | Four to six weeks | Self-serve signup |
| Published CRM integrations | Sixteen named | Salesforce NPSP and Nonprofit Cloud, Bloomerang, Little Green Light |
| Blackbaud and Raiser's Edge NXT | Yes | No |
| Data residency options | US, UK or AU | Not published |
Read that table honestly and it says something simple. Dataro is the deeper tool if you are buying ranked prediction across a large file, and it has the broader integration surface. Gratefully is the tool if the thing you cannot do today is ask a question about a donor and get an answer you can trace.
One thing we do not do. Dataro bundles prospect research credits through ProspectAI. Gratefully does not do prospect research or wealth screening, and we do not pretend otherwise.
External signals and internal context
It would be easy to claim that we cite our sources and Dataro does not. That is no longer true, and we would rather correct it than lean on it. Dataro 3.0's Donor Research produces cited, explorable profiles, and they describe the output as backed by evidence rather than a black box. Good.
The distinction that actually holds is what the citations point at.
Dataro's research scans public sources: news, foundation relationships, annual reports and wealth databases. That is external capacity, and it is currently available for US-based records only, with UK and Australia described as in progress.
Gratefully cites your own records. The note somebody typed in 2019, the proposal attached to a grant file, the interaction history that moved when a gift officer left.
Here is why that difference matters in practice. A score tells you a donor is likely to lapse. External research tells you what they are worth and who else they fund. Neither one tells you that their giving stopped after the officer who recruited them left, or that they asked a question in 2023 nobody answered, or that their gift was always intended for the scholarship fund rather than general operating.
That information exists in most organisations. It sits in notes fields, old proposals, board minutes and the memory of whoever has been there longest, and it is almost never queryable. It is also what turns a well-timed contact into a conversation that works rather than a well-timed cold call.
Prediction narrows the list. External research sizes the ask. Internal context tells you what to say.
Which to choose
Choose Dataro if
You run high-volume direct response. You need ranked propensity across a large file. You are on Blackbaud or Raiser's Edge NXT. You want named predictive models per outcome, or prospect research credits bundled in. Or your file is large enough that the per-donor economics work in your favour.
Choose Gratefully if
Your team keeps losing relationship history to staff turnover. You need answers you can trace to a source record. You want donor numbers that are deterministic rather than model-generated. You are on Little Green Light. Or you want to start at $0 and see your own data before committing budget.
Run both if
You are a mid-sized shop with a real direct response programme and a major gifts team. Dataro ranks the file, Gratefully carries the relationship context for the names that come back. They read from the same CRM and neither replaces it.
A note on scale, and it is the one thing we would want a buyer to check first. Dataro's pricing makes most sense at volume. Their $0.25 per donor framing assumes 100,000 active donors, while their own estimator puts a 10,000-donor organisation at $1.60 per donor on Essentials.
That is not a criticism of their pricing, it is arithmetic, and it is a reason to run your own file size through their estimator before shortlisting either of us.
A note on Bloomerang. From 1 July 2026, Bloomerang customers can add Dataro inside the Bloomerang Giving Platform, seeing lapse risk and recommended next steps on the donor record. If you are on Bloomerang, both of us are available to you, and the choice is about what you need rather than what connects.
Frequently asked questions
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