Adalysis is a diagnostic engine: it watches Google and Microsoft Ads accounts continuously, runs a large battery of audit checks, and flags what is wrong before you asked. AdCopilot is a dialogue: an AI client with 54 read-and-write Google Ads tools (as of v2.16.0) that investigates what you ask and fixes it in the same conversation. One is a monitoring layer, the other an execution layer — and pretending they are direct rivals would misdescribe both.
What Adalysis actually is
Adalysis sells "scale your PPC performance, not your workload" for Google and Microsoft Ads. Its substance is continuous diagnostics: a battery of audit checks it describes as more than a hundred, running against your accounts all the time, plus automated ad testing that monitors creative performance and tells you when a test has a winner. Its homepage cites thousands of agencies and strong review scores. It offers a 30-day free trial; tier pricing is not stated on its homepage.
What deserves plain credit: always-on coverage. Adalysis does not forget to check, does not get busy, and does not need to be prompted. For a practitioner managing many accounts, a system that watches everything while they sleep is not a luxury — it is how nothing falls through.
What we found no public story for: AI-client integration. Adalysis is classic, disciplined automation in its own interface — no MCP, no "use it from Claude" path that we could locate at the time of writing.
What an MCP copilot actually is
AdCopilot connects your AI client — Claude, ChatGPT, Copilot, Gemini CLI, Cursor — to the Google Ads API through a hosted MCP server. Nothing runs until you ask. When you do, the agent reads live data, reasons about it in front of you, and proposes writes your client surfaces for approval: negatives, budget changes, paused campaign builds, asset additions. Deletion is impossible — the four remove tools are never exposed and a REMOVED status is refused server-side, in any letter case.
The strength mirrors Adalysis's inversely: nothing is continuous, everything is contextual. The agent can chase any question, including ones no checklist anticipated, and the investigation ends in an executed fix rather than a flagged finding.
Overlap and non-overlap, mapped honestly
| Job | Adalysis | AdCopilot |
|---|---|---|
| Continuous account monitoring | Yes — always-on checks | No — runs when prompted |
| Automated ad-test monitoring | Yes — its signature strength | No — on-demand analysis only |
| Ad-hoc investigation ("why did CPA jump?") | Limited to what checks surface | Yes — free-form, with live data |
| Executing fixes | Flows within its interface | Yes — 54 read-and-write Google Ads tools (as of v2.16.0), approval on each write |
| Campaign creation | No — not its job | Yes — search and PMax, arriving paused |
| Deletion | — | Cannot delete — remove tools absent, REMOVED refused server-side |
| Platforms | Google + Microsoft Ads | Google Ads, Google Analytics, Search Console, Tag Manager |
| Works inside Claude/ChatGPT | No | Yes — any MCP client |
| Trial and pricing | 30-day trial; prices not on homepage | Free 7-day trial for a new workspace, then a flat monthly or yearly subscription — published pricing |
The overlap is a single band: "find problems in a Google Ads account." Even there the mechanisms differ — a fixed checklist run continuously versus an open investigation run on demand. Everything else is non-overlap.
The audit question: continuous flags vs conversational deep dives
A checklist audit and a conversational audit fail differently. The checklist never misses what it checks for and never sees what it does not; the conversation can chase anything but only what someone thought to ask. In practice the flags make excellent prompts: "Adalysis says this ad group has a losing ad — pull its data, tell me if you agree, and if so draft and create the replacement, paused." That workflow — flag in one system, judgement and execution in the other — is described from the copilot side in what an AI Google Ads audit checks.
Price and fit, verified at publish
Adalysis publishes a 30-day free trial; its tier pricing is not stated on its homepage, so verify current numbers directly. AdCopilot runs a free 7-day trial for a new workspace — the full Pro plan, no card — then drops automatically to the Free plan (one account); a paid plan — published pricing — is there once you want more. Fit splits cleanly. High-account-count practitioners who live in Google and Microsoft Ads get compounding value from always-on checks. Teams whose bottleneck is investigation and execution — not detection — get more from a copilot, because a finding without a fix is still a to-do item.
The stack argument: when running both makes sense
If you are an agency or a heavy in-house team, the two-layer stack is genuinely strong: Adalysis as the smoke detector, AdCopilot as the person who walks over, looks at the fire, and puts it out with your approval. The layers do not duplicate spend because they do not duplicate work.
If you are choosing one: choose by your scarcest resource. Scarce attention across many accounts — the monitor. Scarce hands to actually make changes — the copilot. For the second case, the free trial is the fastest way to find out: connect an account, ask it for the audit, and see how many of the findings are fixed before the week is out.