Which kind of tool do you actually need?

Not “which vendor wins every row”. That table gets fact-checked and dismissed, and rightly. Here are the five kinds of tool people buy instead of Kamino, named, priced where the vendor publishes a price, each with the row it genuinely wins. Competitor prices are as published on each vendor's own pricing page, August 2026. Check them there; we would rather you did.

An agent, pointed at your booksNot compared

ChatGPT · Claude · Copilot · the assistant already inside QuickBooks or Bill.comWhy not point an assistant at my systems, or just use the one already inside each of them?

Spreadsheet pipesNot published

LiveFlowDo I just need my books inside the model I already have?

Report packsFathom ~$50/mo · Syft ~$23/mo

Fathom · Syft · Reach · G-AcconDo I need a monthly pack that looks finished?

FP&A platformsJirav $250–700/mo

Jirav · DatarailsDo I need driver-based modelling and someone to run it?

Generic BIPower BI $10/user · Tableau $75/user

Power BI · Tableau · LookerDo I need to draw anything at all?

An agent, pointed at your books

ChatGPT · Claude · Copilot · the assistant already inside QuickBooks or Bill.com

Not compared

Why not point an assistant at my systems, or just use the one already inside each of them?

Where they win

Wins on breadth, and on the day you need something nobody built a feature for. It will read the contract, draft the email and summarise the board pack. Kamino will not, and does not pretend to.

Where Kamino wins

Because the number a services company runs on does not live in any one system, and neither shape of this can reach it. The assistant inside QuickBooks answers from the ledger: it cannot see the hours in your time tracker, the burden in payroll, or the vendor bill that posted late in accounts payable. Point one at each platform instead and you have five assistants each confidently right about its own system, five answers to the same question, and nothing that reconciles them. Point a single agent at all of them raw and it is handed your records, infers what a direct cost is from the account names that afternoon, and reconciles nothing, because reconciling is the work. Kamino stitches the sources first and answers from the reconciled set, which is why the same question at 07:15 and 16:40 returns the same figure unless a record moved.

Who it actually serves

Whoever holds the keys, at whatever depth those keys open. There is no per-role ceiling in a chat window: an agent that can reach payroll can be asked about payroll by anyone who can reach the agent, and the answer arrives with no record of what it was allowed to see.

What its assistant can reach

One system each, or all of them raw, and both fail the same way. The records go to the model, the semantic layer is whatever it infers that afternoon, nothing it returns can be traced to the rows behind it, and nothing reconciles the systems the answer actually spans.

Spreadsheet pipes

LiveFlow

Not published

Do I just need my books inside the model I already have?

Where they win

Wins when the model already lives in Sheets and works. If your spreadsheet is the answer and it only needs feeding, feed it.

Where Kamino wins

Kamino is the other choice: you stop maintaining the model. The scoping, the lineage and the aggregation are the product, not something you rebuild each month.

Who it actually serves

Whoever maintains the model, which in practice is one person. Everyone else is sent a tab.

What its assistant can reach

Whatever is in the sheet. An assistant over a spreadsheet inherits that spreadsheet's scope and its blind spots, because the sheet IS the data model: it can only reach what somebody already fed it and still maintains.

Report packs

Fathom · Syft · Reach · G-Accon

Fathom ~$50/mo · Syft ~$23/mo

Do I need a monthly pack that looks finished?

Where they win

Fathom wins on polished board packs: hand it over and it reads well. Syft wins on entry price at ~$23/mo.

Where Kamino wins

Kamino wins on operator scoping, drill-to-record, and multi-entity economics. Fathom is ~$106/mo at two entities and ~$280 at ten; a pack still cannot tell you which project lost money.

Who it actually serves

Finance, and the board it hands the pack to. A pack is either shared whole or not shared, because there is nothing in it but the general ledger.

What its assistant can reach

The general ledger, because that is all a pack holds. An assistant on top of one answers ledger questions well and still cannot say which project lost money, for exactly the reason the pack cannot.

FP&A platforms

Jirav · Datarails

Jirav $250–700/mo

Do I need driver-based modelling and someone to run it?

Where they win

Jirav wins on driver-based 3-statement modelling. Datarails wins for teams that genuinely live in Excel and will not leave.

Where Kamino wins

Kamino wins because you do not have an FP&A analyst, and they are priced assuming you do. Jirav runs $250–700/mo before anyone has touched a driver.

Who it actually serves

The finance team. Everyone in the room is already finance, so the question of who may see a cost rate never comes up.

What its assistant can reach

The plan someone built. An assistant over a driver-based model is only as current as the drivers, and keeping the drivers true is a person's job rather than the tool's.

Generic BI

Power BI · Tableau · Looker

Power BI $10/user · Tableau $75/user

Do I need to draw anything at all?

Where they win

They win on drawing anything. Any chart, any shape, any source, no ceiling.

Where Kamino wins

Kamino wins because they need a third-party connector to reach QuickBooks, then DAX and Power Query, and YOU build the semantic layer. That layer is the part that decides whether the number is right.

Who it actually serves

Anyone, once someone builds the model and the row-level security under it. That is a modelling project with a data engineer attached.

What its assistant can reach

Whatever the semantic layer exposes. That layer is the part that decides whether the number is right, and you built it, so the assistant inherits the model and its mistakes together.

Power BI will draw anything you ask it to. It won’t tell you the number is right.

And who Kamino serves

Five roles at once, because a drill-down ceiling per role per data domain is the only way stitched payroll, margin and time data can be handed to producers and resourcing at all.

How the ceiling works

Everything above has an assistant. That is not the axis.

The question is not whether a tool has AI in it. By now they all do. The question is what the assistant is handed, and what it can reach once it has it.

The model receives your question and the shape of your data: table and field names, the scope, the period. It writes the query; Kamino runs it against your records itself. To write the read-out under an answer, the model is then given a summary of the result: the totals, and the largest and smallest entries by name and amount. It never receives the underlying rows. That is a fact about how it is built rather than a promise about what we keep, which is why it is the one row here that does not move when somebody ships a new model.

How the assistant works How the ceilings work

If your model works, keep it

Kamino is for operators who never wanted to build the model in the first place, who want profit by project, client and department out of the books they already keep, and want to be able to open the invoice behind any figure. If that is not the problem you have, one of the tools above is a better buy, and we would rather say so here than in month three.

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