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SaaS Distribution in the AI Era: How New Software Gets Users When Building Is Cheap

SaaS distribution in the AI era

TL;DR: In 2026 the scarce part of launching software is getting users, not building it. New App Store apps nearly doubled while downloads grew 2%, and most early-stage founders still get first customers through personal networks. For a new tool, the channels that still work for little money are positioning as “[incumbent] alternative”, a free or open-source edition listed where people look for alternatives, short tool-intent searches (far less exposed to AI summaries than questions), one genuine community presence, and AI referrals judged on signups rather than visits. None of this creates demand; it decides who gets the demand that exists.

Why this page exists

The wiki’s SEO pages (seo/zero-click-strategy, seo/agentic-search-optimization, seo/new-site-ranking) are written for content sites and brands. A product or tool has a different problem: it needs people to try it, and its best search terms are short “I need a tool” queries, not “how do I” questions. This page covers that case.

The supply shock: building got cheap, users didn’t

SignalNumberSource and grade
New App Store apps~560k in H1 2026 vs ~600k in all of 2025Sensor Tower estimates via NYT / 9to5Mac. Independent data, reported secondhand
App Store downloads17.6B in H1 2026, +2%Same
AI-written code at startups62% say more than half their code is AI-writtenSupabase State of Startups 2026, 2,000+ founders. Vendor survey
First customers56% came through personal networksSame
Paid acquisition67% never tried itSame
Who builds78% of respondents technicalSame

The App Store numbers are consumer apps, not SaaS; applying the shape to SaaS is our inference. The reading: when product supply grows far faster than user demand, the average new product gets fewer users, and distribution decides which ones get them.

Counter-evidence worth keeping. “Anyone can build” is overstated. A randomized trial by METR (16 experienced open-source developers, 246 tasks, February–June 2025) found them 19% slower with early-2025 AI tools, while they estimated afterwards that AI had made them 20% faster. Tools improved since, and the sample is small; the point is that building still takes skill. And 78% of the Supabase respondents are technical.

What changed in organic search for tools

AI summaries don’t hit all queries equally. Pew’s browsing data (2025): an AI summary appeared on 8% of one- or two-word searches, 53% of searches with ten or more words, and 60% of question-style searches (who, what, when, why). Short tool-intent searches (“backlink checker”, “invoice parser”) are far less exposed than how-to content. That inference is ours, not Pew’s. The broader click loss is on seo/zero-click-strategy; Google disputes it and says organic clicks are relatively stable overall (an interested party).

Channels that work for a new tool

1. Name it after the incumbent. People search for “[incumbent] alternative” and “[incumbent] alternative open source”. Put the phrase in the site title, repo description and listings, and list the product on alternatives directories.

2. A free or open-source edition as the distribution unit. On GitHub, the trending lists are a discovery surface of their own. See the OpenSEO case below.

3. One community, as a builder. A post where your users already talk, explaining what you built and why, answered comment by comment. Community sites also feed AI answers: Semrush’s study of 230k+ prompts (July–October 2025) found Reddit and LinkedIn among the top five cited domains in ChatGPT, Google AI Mode and Perplexity. Don’t build on one platform: Reddit went from close to 60% of ChatGPT responses citing it in early August 2025 to about 10% by mid-September (Semrush, vendor study).

4. Callable by assistants. An MCP server or a ChatGPT app puts the product inside the conversation instead of behind a search result. Early and unmeasured as a channel; the mechanism is on marketing/ai-interface-layer.

5. AI referrals as their own channel, judged on signups. At Ahrefs, AI search brought 0.5% of visits but 12.1% of signups over 30 days (vendor, one site, and an SEO-tool audience). Segment traffic from chatgpt.com, perplexity.ai and gemini.google.com and check your own conversion before believing anyone’s multiple.

6. Only pages that add something. Google’s spam policy defines scaled content abuse as generating many pages mainly to manipulate rankings, and names “using generative AI tools… to generate many pages without adding value for users” as an example. Programmatic pages carrying unique data are fine; templated rewrites are the target.

Case: OpenSEO’s visible launch path (2026)

OpenSEO (Every App) is an MIT-licensed, self-hostable alternative to Semrush and Ahrefs, built on DataForSEO data, with an MCP server and Agent Skills. A marketing tool that had to market itself. What is publicly visible:

  • Positioning is a search query. The repo description reads “Open source alternative to Semrush and Ahrefs”, and it is the only entry on OpenAlternative’s open-source Semrush alternatives page.
  • GitHub’s own discovery did the lifting. Trendshift records it on GitHub Trending from 25 June to 31 August 2026 (best rank #7), then +10,557 stars in the 61 days to 30 October, reaching ~25.7k. Social coverage followed the stars (posts around the 20k mark in late September), not the other way round.

What isn’t verified: where the very first users came from. Founder posts on Reddit in May 2026 appear in archive snippets, but we couldn’t confirm the subreddit or their effect, so we don’t claim a seeding path. Stars are attention, not customers.

What agent-run SEO research costs

The research side of this work can now run through an agent rather than a suite. The data underneath is the same commodity whichever wrapper you pick:

OptionPrice (checked 30 Oct 2026)
DataForSEO directSERP data from $0.0006 per request; $50 minimum top-up; pay as you go
OpenSEO self-hostedFree software; you pay DataForSEO directly on your own key
OpenSEO hostedDataForSEO’s data plus a flat 28% markup; plans of $10 / $30 / $200 a month include $10 / $36 / $240 of usage
Ahrefs Lite€119 a month (EU price page)
Semrush SEO plan€134 a month, monthly billing

What’s underneath: DataForSEO. OpenSEO has no data of its own. Every keyword, SERP and backlink number comes from DataForSEO, the same pay-as-you-go API many SEO tools resell. So OpenSEO is not a cheaper data source: self-hosted you pay DataForSEO’s price, hosted you pay it plus 28% (the larger plans’ bonus credit offsets part of that: $240 of usage for $200 brings the Agency plan to roughly 7% over the raw data cost).

Where OpenSEO is worth using. It saves the work of building on the raw API:

  • A ready interface. Keyword research, rank tracking, backlinks and site audits in one dashboard, without writing code against DataForSEO’s endpoints.
  • Agent access out of the box. An MCP server plus pre-built Agent Skills, so Claude Code, Codex or Cursor can pull SEO data and run standard workflows the same day.
  • Open source and self-hostable. MIT licence; runs on Docker or your own Cloudflare account, so you can change it, keep the data in your account, and pay no seat fee.
  • A low entry point. No Ahrefs- or Semrush-style subscription; for occasional research the usage-based bill is small.

Where it isn’t. If you or your agent already call DataForSEO directly, OpenSEO adds no data, only an interface and a markup on the hosted plan. Pick it for the convenience, not for the price.

What a wrapper doesn’t do is the analysis, and that is where an agent setup earns its cost: keyword ideas generated in each market’s own language rather than translated, a check of how weak the current first page is (forums, old posts, no dedicated tool), and a scorecard that ranks the opportunities. Our version is the experimental demand-measurement run noted on tools/niche-hunter.

Pay-as-you-go grows with use, and index data is a snapshot, not a live crawl of your site. Check an agent’s numbers against Search Console once before trusting them.

Honest limits

  • This redistributes demand, it doesn’t create it. With downloads +2% and supply nearly doubling, the average product loses whatever the tactics.
  • Most evidence is vendor-sourced (Supabase, Ahrefs, Semrush). Pew, Sensor Tower and METR are the independent anchors.
  • Tactics decay when everyone runs them. “Alternative” pages and skill packs are open to every competitor; what stays scarce is positioning, original data and the judgment about which pages deserve to exist.
  • Not for enterprise sales products, where a few relationships beat search, or for products still validating demand (marketing/discovery-before-scale).

Key Takeaways

  • Building is cheap; the scarce part is distribution. Supply nearly doubled on the App Store while downloads grew 2%.
  • Short tool-intent searches are much less exposed to AI summaries (8% for 1–2 words) than questions (60%).
  • “[Incumbent] alternative” positioning plus an open or free edition is the cheapest visible launch path; OpenSEO rode GitHub Trending to ~25.7k stars.
  • Judge AI referrals on signups, not visits (Ahrefs: 0.5% of visits, 12.1% of signups; one site).
  • Mass AI pages without added value are named in Google’s spam policy.

Sources

By Andrej Ruckij