Databox alternative

Databox has the more generous free tier. Here is the axis where that stops being the question.

Databox is a KPI dashboard platform with an AI analyst (Genie) and an MCP server — and its free plan includes three data sources, one user and 50 AI credits a month. As of September 30, 2026, paid plans run Analyst at $71 a month billed annually (five sources, 150 AI credits, the MCP server and API), Team at $199 (ten sources, three users, 500 credits) and a larger Team tier at $319, with white-labeling sold as an $80-a-month add-on.

Worth saying plainly at the top: on free-tier generosity Databox wins. Three sources against TableBI's one is not close. The axis where the comparison turns is what happens to a question the dashboard was not built for — because in Databox that question costs credits and returns an answer inside their data model, and in TableBI it is read-only SQL over the untouched raw tables with no meter attached.

Side by side

Databox figures read from the Databox pricing page on September 30, 2026; vendors change pricing, so check before deciding. TableBI figures come from its own pricing page and docs.

What a credit pool means as you actually use it

Fifty credits a month on free and 150 on the $71 Analyst plan are not large numbers once an agent is in the loop. Generating insights, building a dashboard and asking a question all draw from the same pool, and Databox sells a $200-a-month top-up precisely because teams run out.

This is not a criticism of the pricing — hosted inference costs real money and metering it is honest. It is a structural difference. TableBI hosts no model at all, so the number of questions you ask has no effect on what you pay us; the inference runs on your own Claude Code subscription or API key. If your usage pattern is "a handful of questions a month", the meter never bites and this difference is theoretical. If it is "my agent interrogates the data all day", it is the whole comparison.

A curated dataset against the raw table underneath

Databox models your sources into datasets so the KPI tiles work. TableBI keeps two altitudes: the same unified metrics — roas(), cpa(), ctr() and the rest — plus search_console_raw, ga4_raw, google_ads_raw and meta_ads_raw with every dimension the platform returned.

The practical difference shows up on the awkward question. "Which queries lost impressions on mobile in Germany after the August update, and did those pages also lose GA4 sessions" is one SQL statement against raw tables. Against a curated dataset it is either a supported cut or it is not.

Where TableBI is simply behind

Being fair about this is the point of the page. Databox connects far more sources than TableBI's four. It has an MCP server; TableBI has one too since CLI 0.4.0, but only as a local stdio server inside the CLI — there is no hosted TableBI endpoint. It has alerting, forecasting, sub-accounts and a white-label path. It has a free tier with three sources where TableBI gates at one.

What TableBI has is narrower and sharper: SQL over raw, no credit meter, per-answer freshness metadata, and a self-host option. Pick on which of those lists describes your actual week.

Databox pricing (September 2026)

Databox pricing starts free and moves to $71 a month billed annually for Analyst; every paid plan is metered by data sources, users and a monthly AI credit pool. Read from the Databox pricing page on September 30, 2026:

Add-ons: an AI credit top-up at $200 a month and branding and white-labeling at $80 a month. Two things changed since this page first quoted Databox on September 5: the Analyst plan went from $64 to $71 and from 500 to 150 AI credits, and the MCP server and API moved off the free plan. The credit pool is the number to model — at 150 a month, an agent asking questions every working day spends it inside a few weeks.

What TableBI actually is, and is not

TableBI is a marketing-data backend for coding agents: it connects Search Console, GA4, Google Ads and Meta Ads (beta) into one queryable store, answers in read-only SQL over both a unified cross-channel view and the lossless per-platform raw tables, and pins any answer to a live public dashboard URL that refreshes itself.

The shortest way to see whether that shape fits is to connect one source on the free tier and ask it something:

# install, connect one source, ask
npm i -g @tablebi/cli
tablebi login
tablebi install          # plants a skill so Claude Code knows the commands
tablebi connect google_ads

tablebi ask "WITH w AS (SELECT MAX(date) AS anchor FROM metrics)
   SELECT platform,
          roas(SUM(revenue), SUM(cost)) AS roas,
          cpa(SUM(cost), SUM(conversions))       AS cpa
   FROM metrics, w WHERE date > w.anchor - 30
   GROUP BY platform ORDER BY roas DESC"

# pin the answer to a URL that refreshes itself
tablebi pin --title "Cross-channel efficiency"
→ https://you.tablebi.com/d/dsh_…

Every answer carries a trust block: per-source freshness, the rows it scanned, and explicit caveats — Search Console runs two to three days behind and applies a privacy threshold to rare queries; GA4 backfills for a day or two; GSC clicks and GA4 sessions are different populations and are not supposed to match. Here is a pinned dashboard running on real data.

Which to pick

Questions people ask

Other comparisons

Working the numbers rather than the tools? The free marketing calculators do ROAS, CPA, CTR and the rest in the browser, and the report templates are widget sets you can pin.