Compare us
Work out whether this is the right shape for you.
There are several sensible ways to solve a data problem, and a bespoke build is only one of them. These comparisons describe categories of option rather than particular companies, so you can place us against whatever you are already considering.
Versus a shared community data platform
Subscription products built for the community sector, where many organisations use the same underlying system.
| Area | Shared SaaS platform | DataBytes AI build |
|---|---|---|
| Where data lives | A shared, multi tenant vendor cloud under the vendor's terms of service | Your own dedicated infrastructure, in an account only your organisation controls |
| Data sovereignty | Access and sharing settings inside the vendor's platform | Ownership and control at the infrastructure level, not just the permissions level |
| Ongoing cost | Recurring per seat or per tier subscription | A one off or staged build, with no required ongoing licence |
| Time to first value | Fast. Sign up and start today | Slower. A build takes weeks |
| Support model | Possibly a dedicated support person or team, and a knowledge base | Direct access to the person who built it, on agreed terms |
| Fit to your organisation | A fixed feature set shared across every customer | Tailored to your existing systems and ways of working |
| Leaving later | Data and reporting may be tied to the vendor's product | Built on open, widely used tools, so you are free to move it |
Choose the platform if you want something running today and are comfortable being one tenant on a shared roadmap. Choose a build if ownership, fit and the absence of a recurring licence matter more than speed to start.
Versus a Microsoft or Power BI consultancy
Consultancies delivering reporting and analytics on the Microsoft stack, typically Power BI, Fabric, SharePoint and Azure.
| Area | Microsoft-stack consultancy | DataBytes AI build |
|---|---|---|
| Best when | You already run Microsoft 365 across the organisation and want reporting to sit natively inside it | You want a self contained system you own outright, independent of any one vendor's licensing |
| Licence exposure | Per user licences that scale with headcount, and can change with vendor pricing | Open and source-available tooling with no per seat licence, so cost tracks infrastructure not headcount |
| Adding more people who just need to look | Viewing a report generally needs a licence too, so every extra board member, program worker or funder adds recurring cost | Adding viewers costs nothing. The number of people who can see a dashboard is a governance decision, not a budget one |
| Sharing outside your organisation | Sharing with funders, partners or community members typically means guest access, additional licences, or publishing to the open web | Controlled external access without a per head licence, and without making anything public |
| Familiarity for staff | Often already familiar to staff using Microsoft tools daily | New interfaces, which is why training is built into every engagement |
| Offline field collection | Not typically part of the stack | A core capability, designed for areas with no coverage |
| AI search over your own documents | Available, but as a separate product with its own per user licence, and your content is processed by the vendor | Part of the same system, with a fully self hosted option where nothing leaves your infrastructure |
| Ecosystem depth | A very large partner and contractor market to draw on later | A smaller pool, though the tools used are widely adopted and well documented |
| Data residency and control | Configurable, but within the vendor's cloud and commercial terms | Infrastructure in an account your organisation holds |
| What you hold if the engagement ends | Reports that keep working only while the licences keep being paid | The entire system, running in your own account, with no payment to anyone required to keep using it |
If your organisation is already deeply invested in Microsoft and happy there, a Microsoft specialist is very likely the better call. We are the better fit where independence from vendor licensing is itself the requirement.
Versus an off the shelf survey or field data tool
Mobile form and survey products used to collect data in the field.
| Area | Off the shelf survey tool | DataBytes AI build |
|---|---|---|
| Collecting data | Good. Many handle offline capture well and are quick to set up | Comparable, since we build on the same class of open source form tooling |
| Setup effort | Low. Build a form yourself in an afternoon | Higher upfront, because the collection layer is wired into everything else |
| What happens next | Data usually needs exporting to somewhere else to be analysed or reported on | Flows directly into your database, dashboards and reporting with no export step |
| Reporting depth | Usually summary views of the responses themselves, then export to a spreadsheet for anything beyond that | Full dashboards and scheduled board or funder reports generated from live data |
| Joining to other data | Limited. It is a collection tool, not a data platform | Field data sits alongside your other organisational data in one governed structure |
| Access control and audit | Typically simple roles at the form or project level | Role based access down to the individual record, with an audit trail of who created, changed or deleted a record |
| Hosting | Usually the vendor's servers, sometimes offshore | Your own infrastructure, in Australia |
| Cost at small scale | Often free or very cheap for a handful of forms | A build cost, which only makes sense if the data needs to go somewhere |
| Cost as it grows | Pricing commonly scales with submissions, users or storage, so collecting more data costs more every year | Cost tracks infrastructure, not how much you collect or how many people use it |
| What it can become later | It stays a survey tool. Everything past collection is somebody else's problem | Collection is one layer of a system that also does reporting, automation and private AI search across your documents |
If all you need is to collect responses and read them, a survey tool is genuinely the right answer and we will say so. The case for a build starts when that data has to feed reporting, mapping or decisions.
Versus hiring a data analyst
Bringing the capability in house with a permanent staff member.
| Area | Hiring an analyst | DataBytes AI build |
|---|---|---|
| Ongoing cost | A recurring salary, on cost and recruitment spend | A defined project cost, with optional light touch support after |
| Time until it helps | Advertising, shortlisting, interviewing and a notice period. Often months before anyone starts | Work starts when the engagement does, and discovery produces something useful in about a week |
| What they need to be effective | A system to work in. Without one, a good analyst spends most of their week hand cleaning spreadsheets rather than analysing anything | The system is the deliverable, which is the thing an analyst would otherwise need before they could add value |
| Institutional knowledge | Deep understanding of your organisation over time | Less context initially, which discovery exists to close |
| Key person risk | High if the system lives in one person's head and they leave | Documented, version controlled infrastructure with written runbooks |
| Infrastructure skills | An analyst is not usually an infrastructure or security engineer | Database, identity, hosting and security are part of the build |
| Breadth | One person, one skill set, and whatever they do not cover stays uncovered | Database, access control, reporting, automation and AI search in one engagement |
| Retention in a small organisation | A capable analyst in a small team often has nowhere to progress to, and gets recruited away | Capability is built into your systems and documentation, so it does not resign |
| Day to day responsiveness | Someone in the building who can answer a question that morning | Slower for ad hoc questions once the engagement ends |
| Realistic for a small team | Often not. A good analyst is expensive and hard to retain part time | A build plus training may give a small team most of the benefit |
These are not mutually exclusive. A common pattern is a build that establishes the foundation, with training so an existing staff member can grow into the data role rather than the organisation hiring for it.
Still weighing it up?
Tell us what you are comparing against.
If you are looking at a specific product or approach, describe it and we will give you an honest read on whether a build is actually the better option for your situation. Sometimes it is not, and we would rather say so early.
Email hello@databytesai.com.au