Free AI rank tracking: what is actually free

Yes — free AI rank tracking exists, in three forms: a manual method you can run today for nothing, free checkers published by SEO vendors, and no-card trials of paid trackers. None of them give you repetition, and repetition is the whole point of rank tracking. This page sets out exactly what each free route measures, what it cannot measure, and the point at which paying starts to be the honest answer.

§Is there really free AI rank tracking?

There is, as long as you are precise about which part is free. Three routes cost nothing at the point of use:

• The manual method. A fixed prompt set, asked by hand in each assistant, with the answers logged in a spreadsheet. Free forever, no account, no vendor. It is described step by step in the four sections below, one per surface.
• Domain-level AI visibility checkers. Semrush has a free AI Search Visibility Checker that scans a domain across ChatGPT, Gemini, AI Mode, and AI Overviews and returns a visibility score, top cited pages, and a competitor comparison. Checked 31 July 2026, it states you can “use the tool three times per day for free, without registering or creating an account.” Good for a fast baseline before a deeper project.
• No-card trials of paid trackers. SimplyRank sits here rather than in the free-tier bucket: a 14-day trial, no card, with your own prompts and competitors across four models. The honest limits of that trial are set out below.

What none of them give you is the same prompts re-run on a schedule, stored as a comparable history. That is the line every vendor charges across, and it is the line this page keeps drawing: free answers are we named today? Paid answers did last month’s work change the answer?

§Free ChatGPT rank tracking: the manual method

ChatGPT answers in prose, so there is no numbered results page to read and no rank to copy down. What you record instead is four fields per answer — and you can record all four by hand, for nothing:

1. Inclusion. Was your brand named at all? This is the floor; every other field is a refinement of a mention that already exists.
2. First-mention position. Not a rank — an offset. Where in the reply does your brand first appear: opening recommendation, mid-list option, or a hedge near the end? Write down which third of the answer it landed in. A tracker does the same thing by converting the character offset of the first mention into ordered bands so it can be charted; by hand, thirds are close enough to see movement.
3. Competitor set. Which other brands were named, and in what order? This is the field that turns brand monitoring into a market-share reading.
4. Cited domains. If the answer attaches links or a reference list, log the domain of each one and mark it as yours, a competitor’s, or a third party’s. Third-party pages that cite three rivals and not you are the most actionable output of the whole exercise.

Three rules that make a free check trustworthy

Most manual checks fail not because they are manual but because the conditions move between runs. Hold these still:

• New chat, memory and personalisation off. Otherwise you are measuring your own account history, and your brand will look far more visible than it is to a stranger.
• A fixed prompt library. Write the questions once, in the words a buyer would use, and stop editing them. A drifting prompt list makes every comparison meaningless — you can no longer tell a change in the model from a change in your own question.
• One run per prompt per day, dated. Answers vary between runs. Fix the day and the prompt so that later comparisons are like for like; a tracker does this by keeping one canonical scan session per brand per day.

That sheet is a real ChatGPT rank tracker. It costs an hour a week and nothing else. When an hour a week stops being available, the same fields are what the ChatGPT rank tracker panel records automatically.

§Free AI Overviews and AI Mode rank tracking

Google’s answer layer is a separate surface from the chat assistants and has to be logged on its own — presence in an AI Overview says nothing about what ChatGPT replies, and the reverse is just as true. The free manual method here is short:

1. Search logged out. Use a fresh private window for each commercial query so personalisation and location history do not colour the result.
2. Record presence first. Did an AI Overview or AI Mode answer appear at all for this query? Absence is a data point, not a failed check — a query that never triggers an answer box is one you should stop chasing at this layer.
3. Record naming and linking separately. Being named in the text and being cited as a source are different outcomes with different fixes. Log both.
4. Log the cited domains. The list of sources Google chose is your target list. Screenshot it — the panel changes and you will want the receipt.

Several SEO vendors publish free tools for this surface, which is the most genuinely free corner of the market:

• AI Overviews-specific free tools. Advanced Web Ranking lists a free Google AI Overview tool that “tracks the evolution of AI Overviews over time in Google search.” SE Ranking offers a free 5-keyword check on its AI Overviews tracker plus a no-card trial, and Seobility's free Basic plan shows only whether an AI Overview appeared for a monitored keyword, with the full report — the AI answer, cited sources, and their order — behind its 14-day trial or a Premium plan. All three checked 31 July 2026. These are strongest when Google SERP visibility is the main question.

The shared ceiling is keyword count and history: free tiers cap how many queries you can monitor and rarely keep a comparable archive. For the deeper version of this surface — per-query citation logs, absences recorded as absences, competitor source overlap — read the AI Overviews tracker guide, which covers it properly rather than in a section.

§What is free LLM rank tracking?

Same job, named after the model class instead of a brand. “LLM rank tracking” means measuring how large language models answer your category questions: whether you are named, how early, beside whom, and on what evidence. People search it that way when they think of the surface as a class of models rather than as ChatGPT.

The free version is the manual spreadsheet, widened by one column. Keep the prompt set fixed, ask every prompt in each model, and add a model column so the same question can be compared across them. That comparison is the reason to bother with the class rather than one assistant — the models disagree, and the disagreement is the finding.

• Named everywhere. Your category presence is solid; work on position and sentiment rather than inclusion.
• Named by the live-web models, absent from the others. Your evidence is recent and thin. It exists on the web but has not settled into the models’ learned picture of the category.
• Named by the trained models, absent from the live-web ones. The opposite problem: an older reputation with nothing current for a citation-led engine to pick up.
• Absent everywhere. A category-evidence gap, not a tracking problem. No tool fixes this; third-party coverage does.

For reference when you build the sheet, SimplyRank scans four models — ChatGPT (gpt-4o), Claude (claude-sonnet-5), Perplexity (sonar-pro) and Gemini (gemini-2.5-flash) — with model IDs read from pricing.ts, checked 28 Aug 2026. Free or paid, name the exact model in your log: “we were named in Claude” ages badly once the version behind it changes.

§Free Perplexity visibility checks

Perplexity answers from live sources and shows its citations on screen, which makes it the cheapest surface to check by hand: the evidence is already listed. Ask your fixed prompts, then log the numbered sources rather than the prose — which domains were cited, in what order, and whether any of them are yours. Because the citation list is visible, a free Perplexity check gives you a ready-made outreach target list in a way the other assistants do not. The ongoing per-model panel lives on the Perplexity rank tracker page, and the same manual logic applies to Claude and Gemini.

§What free tools quietly cannot do

Free checkers are good products. The problem is what they leave unsaid, and it is the same three gaps almost everywhere — including in our own trial, which is why they are worth naming plainly:

1. No fixed prompt set of your own. Most free checkers generate the prompts for you from your domain. That is genuinely useful for discovery, and useless for measurement: you cannot compare two runs when the questions changed, and you are not measuring the buying questions you already know matter.
2. No history. A free run is stored as a result, not as a dated series. Without a comparable archive you cannot answer the only question that justifies the work — did the page we rewrote last month change the answer? Note that a stored history is only comparable if the measurement method behind it stayed the same, which is why scan results are stamped with a benchmark version and older runs are charted separately rather than silently blended.
3. No cross-model comparison. Free tools tend to cover one surface well. A Google AI Overview tool tells you nothing about Claude; a single-assistant checker tells you nothing about whether the disagreement between models is the real story.

§SimplyRank's free trial, with its limits

SimplyRank has no free tier. It has a 14-day trial with no credit card, and it is a sample of the full method rather than a smaller version of it. Here is exactly what it includes:

• 10 tracked prompts, written by you, in your buyers’ words — the fixed prompt set the manual method above asks you to build.
• Four models on every prompt. ChatGPT, Claude, Perplexity and Gemini are asked the same question in the same run, so the model-by-model disagreement is visible in one view rather than four browser tabs.
• One sample scan. It is queued the moment signup finishes and picked up by the next hourly scan run, so you see real dashboard data — first-mention position, sentiment, competitor set, parsed citation domains — on your own prompts.
• No credit card, cancel anytime. At the end of the 14-day window you pick a plan or the account goes read-only. Nothing charges automatically.

And what it does not include

• No scheduled scans. Weekly scans start with the first paid billing cycle. The trial gives you one scan, which means it can show you the method and the data quality — it cannot show you a trend line, because a trend needs a second run.
• No history to chart. For the same reason. If someone shows you a movement chart from a free trial, ask what the second data point was.
• Ten prompts is a sample, not a programme. Most categories need more than ten questions to cover the buying journey properly.

Where it goes afterwards is public on the pricing page, and the ladder matters when you are judging whether free is enough. Read from pricing.ts, checked 28 Aug 2026: Starter Lite is $25/mo for 25 prompts, weekly scans only, no CSV export. Starter is $75/mo for 50 prompts with daily and weekly scans plus CSV export (pricing.ts, checked 28 Aug 2026). Advanced covers 300 prompts, per the same pricing.ts read on 28 Aug 2026.

§When free is enough, when it isn't

Free is genuinely enough when:

• You want a one-off read on whether your brand is named in AI answers at all.
• You are building a vendor shortlist and need to see what each tool surfaces.
• You only care about the Google answer layer, not the chat assistants.
• You need a proof point for an internal conversation before buying software.
• You have a handful of prompts and someone willing to re-run them by hand each week.

Free stops being enough when:

• You need a comparable history rather than a snapshot.
• You have more commercial prompts than a person will re-run by hand.
• You need first-mention position, sentiment, competitor set and cited domains in one place.
• You need exports and reporting someone else will read.
• You need to prove a content change moved the answer, not assert it.

The honest split: use the free routes to prove the surface matters in your category, and pay only at the point where repetition, history and export become the bottleneck. If you get there, the cross-model workflow is described on the AI rank tracker page.