AgencyAnalytics alternative

What the per-client price actually buys — and the case where you want a different layer entirely.

AgencyAnalytics is a client-reporting platform priced at $20 per client per month, billed annually, with every feature unlocked on the single plan. That covers 85+ integrations, white-label branding, a client portal, report scheduling, goals and alerts, API access and MCP access for ChatGPT and Claude. Rank tracking and AI brand tracking are separate add-ons, priced per 500 keywords and per 250 credits.

Most "AgencyAnalytics alternative" lists answer with a cheaper reporting platform. This page answers a different question first: whether you want a reporting platform at all. If your job is delivering branded monthly reports to non-technical clients, the honest answer is that AgencyAnalytics does that well and TableBI cannot do it — there is no white-label, no client portal and no scheduled delivery here. If your job is asking arbitrary questions of your own numbers and getting answers you can verify, that is a different layer, and the rest of this page is about the difference.

Side by side

AgencyAnalytics figures read from the AgencyAnalytics pricing page on September 5, 2026; vendors change pricing, so check before deciding. TableBI figures come from its own pricing page and docs.

The per-client model is the real decision

At $20 per client per month, twelve clients is $240 a month and forty is $800 — a cost that scales exactly with the thing you are already billing for, which is why agencies rarely object to it. The model only hurts in two shapes: a portfolio of very small retainers where $20 is a visible slice of the margin, and an in-house team with one "client" that pays per-client pricing for a structure it does not have.

TableBI prices the workspace instead, which is cheaper for the in-house case and has no answer at all for the agency case — there is no per-client isolation, no per-client quota and no client login. That gap is real and it is the reason to stay on AgencyAnalytics if you run an agency.

Ask AI against SQL: the same question, two different guarantees

Both products let you ask a question in plain language. What differs is what stands behind the answer.

AgencyAnalytics answers inside its own data model, and the answer costs credits from a monthly pool. TableBI has no model of its own in the loop: your agent writes read-only SQL, the SQL runs on a deterministic engine, and the answer comes back with a trust block — per-source freshness, the row count it scanned, and explicit caveats such as the Search Console privacy threshold or the GA4 backfill window. You can re-run the query and get the same number.

Neither is strictly better. A metered assistant inside a curated model is faster for a routine question; SQL plus your own agent is the one that survives a question nobody anticipated, and it does not get more expensive the more you ask.

Two altitudes, and why the raw tables matter

Reporting platforms normalise everything into one schema so the charts line up. That is the product. The cost is that when a number looks wrong, you cannot get underneath it.

TableBI keeps both altitudes on purpose. The unified view gives you roas(), ctr(), cpc(), cpa(), cvr(), aov() computed the same way across platforms; underneath it, search_console_raw, ga4_raw, google_ads_raw and meta_ads_raw keep every dimension the platform gave you, unaggregated. When the blended figure disagrees with a platform dashboard, you drop an altitude and find out why instead of filing a support ticket.

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.