Profound alternative: what you actually get for the money
By Vladimir Boldyrev · LLMBrandScan · Published August 10, 2026
The short verdict
Buy Profound if you are running AI visibility as an ongoing program: a named budget line, a dashboard several people log into, weekly prompt-set changes, and someone whose job includes watching the trend. That is what an enterprise contract buys, and Profound has the scale to back it — as of August 2026 it reported a $96M Series C at a $1B valuation, 700+ enterprise customers, and roughly 10% of the Fortune 500 as clients.
Buy an LLMBrandScan scan if your question has an end. You want to know today whether AI models name your brand when someone asks your category's buying question, which competitor takes the slot when they don't, and which sources the models leaned on. That is a diagnosis, not a subscription — $19 for 90 sampled answers, $249 for 720. If the answer turns out to require continuous tracking, you will have paid $19 to find that out instead of committing to a year.
Price and coverage, side by side
Profound's figures below are its published plans as recorded in our July 2026 competitive review; they are a snapshot of that date, not a live quote. LLMBrandScan's figures are read directly from the pricing table this site charges from.
| What | Profound | LLMBrandScan |
|---|---|---|
| Entry price | $99/mo Starter (July 2026) | $19 one-time, 1 credit |
| Engines at entry price | ChatGPT only (July 2026) | 3 of 6, your choice |
| Mid tier | $399/mo Growth — 3 engines, 100 prompts (July 2026) | $149 one-time — 25 questions, 6 engines |
| Top self-serve tier | Enterprise, quoted; reviews cite $2,000-$5,000+/mo (July 2026) | $249 one-time — 720 answers, then $0 |
| Billing shape | Recurring subscription | Prepaid credits; optional monitoring on top |
Where Profound is genuinely stronger
Saying otherwise would be easy to disprove, so here it is plainly. Profound has the deepest dataset in the category and the operating scale to keep it that way: $55M raised as of our July 2026 review, restated as a $96M Series C at a $1B valuation in the August 2026 follow-up, with 700+ enterprise customers. That funds historical depth, prompt-set breadth (up to 10 engines on Enterprise as of July 2026), integrations, an account team, and a procurement process an enterprise buyer can actually put through legal.
It also buys distribution. Profound ranks organically for the category's own head terms, which is how most people reading this page found the comparison in the first place. A one-person product does not out-resource any of that, and pretending otherwise would be the kind of claim this page exists to avoid. The published criticism of Profound is narrower and worth knowing: agency reviews from the same July 2026 period describe the $99 Starter tier as a funnel step rather than a working plan, and the recurring complaint across reviews is "insights without execution" — the tool tells you where you stand, then you still need content and PR work to move it.
What LLMBrandScan does differently
You pay once, and the meter stops
Credits are prepaid and spent per scan: 1 credit for a Light scan, 3 for a Standard, 5 for a Deep. Nothing renews on its own. A $249 Deep pack is 7 credits — one Deep scan plus two Light re-scans after you have made changes.
Six engines on every tier, Claude included — no upsell
An API-grounded ChatGPT proxy, an API-grounded Claude proxy, Perplexity Sonar, grounded Gemini, Grok with live search, and Google AI Mode. A Light scan runs three of the six; Standard and Deep run all six. Engine access is not a pricing lever here — where Profound gates Claude behind Enterprise, it ships in every LLMBrandScan tier at the same price.
A failed scan refunds itself
Credits are reserved at launch and released as calls complete. If every call in a scan fails, the full reservation is refunded automatically — you are never charged for a scan that produced no answers. Partial failures are reported per engine rather than quietly averaged away.
You get the raw answer corpus, not only a dashboard
Every sampled answer, the sources each model cited, and the per-engine breakdown export to CSV, XLSX and PDF. If you want to re-analyse the run yourself or hand it to a client, the underlying text is yours.
A metered MCP surface
Three tools — check_brand_visibility, compare_with_competitor, get_citation_sources — billed per call, so an agent workflow can pull a visibility check without a seat or a subscription.
One honest caveat about all of these numbers, ours included: AI answers are non-deterministic, so the same question asked twice can return different brands. That is why every scan here samples each question 3 times per engine and reports the observed range with a Strong / Emerging / Noisy label rather than a single confident percentage. The methodology behind that is set out in what AI visibility is and how it is measured and in the AI share of voice formula.
The comparison in one paragraph
Profound and LLMBrandScan answer the same question at opposite ends of the market. As of August 2026 Profound had raised a $96M Series C at a $1B valuation and served more than 700 enterprise customers, roughly ten percent of the Fortune 500. Its published entry plan as of July 2026 was $99 per month covering ChatGPT only and 50 prompts, with a $399 per month Growth tier adding three engines and 100 prompts, and enterprise agreements that reviewers placed between $2,000 and $5,000 per month. LLMBrandScan sells no subscription as its core product: a $19 sample buys one Light scan of 90 sampled answers across three engines, and a $249 Deep Audit buys 720 answers across six — Claude included, no upsell. The real decision is not which tool is better in the abstract — it is whether you are buying a continuous monitoring program or a single dated diagnosis you can act on once.
Also worth comparing
Profound sits at the top of the market. If your shortlist is really about mid-market subscription pricing, the closer comparison is Peec AI, whose entry plan is roughly a fifth of Profound's Growth tier. And if you are still deciding which discipline you are even buying — AEO, GEO, or plain LLM SEO — start with AEO vs GEO before you compare vendors.
Frequently asked
- How much does Profound cost in 2026?
- As of July 2026 Profound published a $99/month Starter plan covering ChatGPT only with 50 tracked prompts, and a $399/month Growth plan covering three engines with 100 prompts. Enterprise pricing is quoted per customer; published agency reviews from the same period cite $2,000-$5,000+ per month. Prices are the vendor's published figures at that date, not a live quote.
- Is there a cheaper alternative to Profound for a one-time audit?
- LLMBrandScan is priced per audit rather than per month: $19 buys one Light sample (10 category questions x 3 engines x 3 samples = 90 answers), $149 buys a full Audit (450 answers across 6 engines), and $249 buys a Deep Audit (720 answers) plus two Light re-scans. There is no recurring charge unless you deliberately add monitoring to a completed scan.
- Do I need a subscription to check my brand's AI visibility?
- No. A subscription is the right purchase when you need a trend line and alerting month after month. If your question is one-time - 'do AI models name us in our category, and who takes our slot when they don't' - a single dated scan answers it, and you can re-run the identical scan later to compare.
- Which AI engines does each tool cover?
- As of July 2026 Profound advertised up to 10 engines, with Claude, Gemini, Copilot, Grok and DeepSeek available only on Enterprise; its $99 Starter tier covered ChatGPT alone. LLMBrandScan runs six engines on every paid tier, Claude included at no extra charge: an API-grounded ChatGPT proxy, an API-grounded Claude proxy, Perplexity Sonar, grounded Gemini, Grok with live search, and Google AI Mode.
See where you actually stand — not a guess.
LLMBrandScan runs your category's questions live across up to 6 AI engines and reports the observed range across samples, with a Strong / Emerging / Noisy signal label — not a single lucky answer, and no promise about where you'll rank.
$19 = 90 live answers across 3 engines · pay per scan · ranges, not fake precision
More AI-visibility guides.
- What Generative Engine Optimization (GEO) Actually MeansThe definition, how it differs from SEO, and why 'optimizing for AI answers' is a different job than optimizing for a ranked list.
- Answer Engine Optimization (AEO): Myths vs. FactsAEO, GEO, and LLM SEO get used interchangeably online. Here's what's actually true about each — and where the myths come from.
- How to Create an llms.txt File (Step by Step)A working llms.txt template, what belongs in it, and an honest read on what it does and doesn't do for AI visibility.
- AEO vs. GEO: A Head-to-Head ComparisonThe two acronyms compared axis by axis — origin, scope, mechanics, what each one measures — and a decision rule for which one your work actually is.
- GEO vs. AEO vs. LLM SEO: What's the Actual Difference?Three overlapping terms for three overlapping-but-distinct jobs. A disambiguation guide so you stop guessing which one you need.
- What Is AI Visibility, and How Is It Actually Measured?The metrics the category has converged on — Visibility Score, Share of Voice, first-mention — explained from first principles.
- How to Improve Your Odds of Being Recommended by ChatGPTSeven concrete, testable levers — and three things that don't work no matter how confidently they're sold to you.
- How Perplexity Chooses Sources to CiteThe mechanics of Perplexity's citation model, what makes a page citable, and how to track whether you're being cited at all.
- Optimizing for Google AI OverviewsWhat triggers an AI Overview, how it differs from classic SGE and organic ranking, and a practical checklist to work through.
- AI Brand Monitoring: Why a One-Time Check Isn't EnoughAI answers drift month to month. What ongoing monitoring actually requires, and the DIY-vs-tool tradeoffs honestly laid out.
- AI Share of Voice: The Formula and a Worked ExampleThe exact formula, a worked numeric example with real arithmetic, and how it maps onto the classic marketing metric it's borrowed from.