The Best osint.industries Alternatives in 2026

We picked one username and ran it through every serious username search platform on the same afternoon. The username was richard. Revealer.US returned 488 profiles from 751 sources checked. fingerprint.to returned 483 from 620. osint.industries, on the premium module set, returned 301 from 396. infobreach.net returned 231 from 332. Those four numbers are the headline. The rest is why they differ that much, what each osint.industries alternative is actually good at, and the extraction test most of the field failed.

Disclosure up front: we build Revealer.US. Assume bias and check our work. Every count below is a real run from a single day, not a marketing estimate. Competitor sections are written to be fair, because that is what makes a benchmark worth reading.

How we tested

The rules were simple. We followed them even when a number came out unflattering.

One username, chosen to hurt. richard is a common English given name that thousands of people use as a handle. A common handle punishes lazy detection logic, because soft 404s, parked profiles, and surname collisions all show up at once. A rare handle makes any tool look accurate.

Same day, consecutive runs. We used paid tiers wherever the platform sells one. No reruns to shop for a better number. No handle shopping. Whatever the tool reported is the number in this article.

Counts as each tool reports them. Every platform shows its own version of "sources checked" and "profiles found." We recorded both. Module pools are not comparable across vendors, so every hit rate below is a tool measured against its own pool, never against another tool's. For Revealer.US and fingerprint.to, the headline counts come from JSON exports. For osint.industries and infobreach.net, they come from the results page, because those two offer no export.

One run each. This is a snapshot, not a lab study. Module lists change weekly. Run your own test on a handle where you know the ground truth before you spend money.

Run-to-run drift. An hour after the export session we ran the two leaders again. fingerprint.to returned 483. Revealer returned 478. The lead flipped. Counts move by a handful of profiles between runs, so treat the single-run snapshots in this article as directional. The re-run is a drift check only; every headline number is the first-pass export.

Free checkers and CLI, same window. The same afternoon we also ran the no-install web checkers and a fresh install of Maigret, Sherlock, and Blackbird. Other hosted free UIs that completed a scan (WhatsMyName.io, OSINT UI, Name Checker, UsernameCheckr, Namevine, and AliaScan) sit on the ranked chart with them. whatsmyname.app is not WhatsMyName.io: the former was blocked by an AWS WAF captcha; the latter completed.

The scoreboard

Profiles found for the username richard, one run each:

Raw reach, same runs. How many sources each platform actually attempted:

Results found for the username richard, ranked across every platform that completed a scan: hosted paid, hosted free, and self-hosted CLI on one axis:

CLI counts are one pass each on a fresh install (Maigret 319 on its default ~500-site subset, not the full 3,300-site DB; Sherlock 236/413; Blackbird 281/716). Web counts are each tool's own reported total: WhatsMyName.io 228/483, OSINT UI 91/244, Name Checker 69/194, UsernameCheckr 31/42, Namevine 21/21, AliaScan 15 (teaser).

Hit rate versus reach, and why the difference matters

fingerprint.to posted the best hit rate among the four paid platforms at 77.9 percent. It did that with a raw count within five profiles of the leader, on a pool 17 percent smaller than Revealer's 751 sources. osint.industries posted the second-best paid rate at 76.0 percent, on a 396-module pool, and still finished 187 profiles behind the leader (488 minus 301). Both are true. The second one matters more.

Hit rate measures how often a tool agrees with its own module list. Reach measures how big that list is. A high hit rate on a 396-module pool still misses the 187 extra profiles the larger pool found. When your case turns on the account the subject forgot they had, you do not get partial credit for the hits you never attempted.

fingerprint.to is the sharpest detector in this run and nearly tied on raw count. osint.industries' detection is also sharp, but its pool is small relative to the leaders. A high hit rate on a small pool is still limited reach. Revealer's 65.0 percent on the largest pool shows the extra reach was not bought with sloppy matching. On a handle as collision-prone as richard, holding two-thirds of a 751-source pool without drowning in false positives is the harder job.

Duplicates and data quality: what a result row is worth

A result count is only as useful as the rows behind it. We exported the two leaders and counted unique profile URLs.

Revealer.US exported 488 rows. After dedup that is 486 unique profile URLs: two duplicate rows (0.4 percent), with hackaday.io and namemc.com each listed twice via different modules. fingerprint.to exported 483 rows, 481 unique profile URLs, with zero duplicate URL groups (0.0 percent). The two-row gap is two URL-less summary rows, not duplicates. Both exports are effectively clean. osint.industries is a different story. In our browser session the raw results page showed roughly 20 percent duplicate rows (same platform or same profile listed twice) with thin enrichment. That figure is a manual observation, not a computed one: they offer no export, so we could not verify it programmatically.

Duplicates cost an analyst real time: inflated counts, double-clicked profiles, wasted triage. A tool reporting 301 rows where a fifth are repeats is really reporting about 240 unique profiles.

On flat per-row fields the two leaders are close. Around 90 percent of rows carry at least one enrichment field (Revealer 90.0 percent, fingerprint 89.0 percent). fingerprint's export schema exposes granular fields: follower counts, post counts, account creation dates, even game stats. Revealer rows carry display names, bios, locations, and follower counts. The bigger quality gap is what hangs off the whole report. The same Revealer export carried 500 breach-database hits and 503 stealer-log hits, and the stealer search surfaced 129 email addresses. The fingerprint export ran with its deep search and data search toggles off, so its breach array came back empty; we did not re-run with them on. Username-keyed breach and stealer hits are leads for a common name like richard, not proof of one identity.

Cross-tool overlap on the same handle, unique profile URLs:

Neither leader alone sees the whole footprint. Between them the same handle yielded 662 distinct profile URLs. That is the case for running two engines on important work.

1. Revealer.US: most sources, most profiles, and the deepest result rows

We build it, so grade this section yourself against the raw numbers above. 488 profiles from 751 sources (486 unique after dedup): the widest reach and the highest count in the export session.

Three things separate a Revealer result row from a bare URL. All three showed up in this run.

Former usernames. People change handles and forget the old ones. Breach corpora remember. A Revealer search returns the historical handles attached to an identifier, and those old names routinely unlock accounts the current handle does not match anymore. In a missing-person case or a fraud pattern where the subject rebrands every few months, the former username is often the only durable link between personas.

Enriched extraction. Every hit comes back parsed, not just detected: display names, locations, bios, linked accounts, and follower counts where exposed. The guns.lol test below is the cleanest example.

Breach and stealer-log correlation in the same result set. The search runs account enumeration, breach datasets, and infostealer logs together, so the pivot from a profile hit to an exposed credential happens inside one report. AI Deep Search automates the pivot loop: an identifier found in round one becomes a query in round two until the graph stops growing.

Start on the free tier; paid self-serve runs from $12.99/mo on pricing, with an API for pipelines. Entry points: username search, email lookup, data breach lookup, stealer logs.

Revealer is not a consumer reporting agency, and its data may not be used for employment, tenant, credit, or insurance eligibility decisions.

2. fingerprint.to: the closest thing to a peer

If you strip our own product out of the test, fingerprint.to wins the day. 483 profiles from 620 sources, a 77.9 percent hit rate, and result rows with real enrichment: names, profile names, and locations. It also passed the guns.lol extraction test, one of only two platforms that did.

Revealer led the export session by five profiles while scanning 131 more sources, and on the re-run fingerprint.to edged ahead 483 to 478. That is a tight race on enumeration alone. If your workflow is enumeration-first and you want a second opinion on every handle, this is the one to pair with.

3. osint.industries: sharp detection, small pool, and one real superpower

osint.industries is a good product with the second-best paid hit rate in this test. The premium run checked 396 modules and confirmed 301 accounts, 76.0 percent agreement with its own pool. Its email-first workflows are mature and the modules are solid.

Its superpower showed up outside the raw counts. Feed osint.industries an identifier and its email-provider module set tells you whether that identifier is registered across Microsoft's consumer domains, Outlook, and a set of other mail providers. That registration-existence check is hard to do at scale without tripping abuse controls, and in this test nobody else matched its coverage. If you need to know whether a subject holds a Microsoft account, that module set alone can justify the subscription.

Where the run fell short: reach, duplicates, and extraction. 396 modules is a small pool in 2026 next to Revealer's 751 and fingerprint.to's 620, and 187 profiles fewer than the leader on the same handle. Duplicate rows on the results page (see Duplicates and data quality) padded the 301 count; a fifth of those rows were repeats, so the unique total is closer to 240. There is no former-username layer, and in the guns.lol test below its module confirmed the page existed without returning the contact handles sitting inside it. Detection without extraction turns leads back into manual work.

4. infobreach.net: small pool, honest numbers

231 profiles from 332 sources at a 69.6 percent hit rate. A solid mid-tier showing. The pool is roughly half Revealer's 751 sources, so think of it as a fast triage layer rather than a primary sweep. Useful when you want a second aggregator's opinion without paying for a second full seat.

5. The free checkers: what no-install actually gets you

We ran richard through the free no-install checkers the same afternoon. They answer a narrower question than the paid tier: does this handle exist somewhere?

The same handle, free tier against paid, for perspective:

usersearch.org ran the cleanest free scan of the day: 138 profiles from a 315-site complete sweep, no captcha, no account, one stable total. The cards are bare existence checks, a favicon and a profile URL, with a premium upsell on every hit. Fine for triage. It confirms a footprint exists and leaves the rest to you.

idcrawl.com checked ~36 networks and returned the richest free cards of the no-install group: display names, bios, photos, locations, and follower counts. An Instagram hit came back as Richard Yang, 2,984 followers, with a bio. A Twitter hit carried a location and a job title. The deeper people reports and guessed emails behind those cards route to Spokeo and BeenVerified CTAs. Small pool, real enrichment.

instantusername.com, namecheckup.com, and whatsmyname.app. instantusername.com and namecheckup.com are availability checkers (taken versus available), not ownership evidence. instantusername.com scanned 85 sites and marked 63 taken, 5 available, 16 unknown, with follower counts on TikTok and X only. namecheckup.com scanned 63 properties (27 social plus 36 domains) and marked 22 usernames taken, shown as green/red tiles plus profile URLs. whatsmyname.app is normally the best free accuracy pick, a curated community dataset. An AWS WAF captcha blocked our run, so it gets no number today. WhatsMyName.io is a different host; it completed at 228/483 and is on the ranked chart above.

social-searcher.com is not an existence checker. It runs a Google Programmable Search across nine networks and returns indexed web hits for the keyword: "about 112,000,000 results" on the Facebook tab for richard. That is a content-monitoring tool, not a handle checker. It cannot tell you whether an account exists, only what the index has seen.

epieos.com keeps its username module behind a sign-in wall. The public free tools are the email and phone reverse lookups the platform is known for, and those are genuinely good; we cover them properly in our Epieos alternatives piece. For a no-login username sweep, it was not in this test.

namechk.com and knowem.com both failed to produce a scan: namechk's availability checker died on a captcha in our window, and knowem.com would not resolve at all across two attempts from two different networks. No numbers, no verdict.

Free checkers confirm existence. Paid platforms identify. 138 found versus 488 on the same handle is the coverage gap. A favicon and a URL versus a parsed row carrying names, locations, linked Telegram handles, and breach history is the workflow gap. And free checkers count differently than paid ones (existence cards against parsed profiles), so treat any cross-tier comparison as directional.

Below the web checkers sits the CLI stack: Maigret, Sherlock, and Blackbird. Free, self-hosted, auditable, and consumed as datasets by half the tools above them. This time we ran them too, one fresh install and one pass each on richard. Maigret returned 319 accounts from its default ~500-site subset, Sherlock 236 from 413 sites, and Blackbird 281 from 716 sites. Their site lists overlap the hosted pools unevenly, so treat those numbers as a self-hosted axis rather than a like-for-like match with the hosted tools above. They still belong in a serious stack. Our Sherlock alternatives post covers where each one earns its slot.

The guns.lol test: detection versus extraction

This test separates a hit counter from an investigation tool.

One of the richard hits in this run was a guns.lol page. If you have not seen it: guns.lol is a profile-card service out of the Discord scene. Users get a single fast-loading page with a background image, music, badges, and whatever links they want to pin. Threat actors adopted the format heavily, because the page looks like a harmless aesthetic flex while it holds every contact point they operate.

Every platform in the paid tier has a guns.lol module, and every one of them found the page. Finding it is a status code. Reading it is the product. Exactly two platforms, Revealer and fingerprint.to, returned the contact handles buried in the page body. Every other paid platform in the test, osint.industries included, reported the profile and stopped.

Here is a trimmed version of what a parsed hit looks like (Discord ID partially redacted):

{
  "account_created": 1717026322,
  "badges": ["premium", "gifter", "christmas_2024", "christmas_2025"],
  "custom_metadata": {
    "description": "t.me/imlostt4words",
    "title": "richard"
  },
  "discord": {
    "id": "4042034437******",
    "username": "lacieismine",
    "user_badges": ["HypeSquad Bravery", "Discord Nitro"]
  },
  "page_views": 556,
  "second_tab": {
    "discord": "discord.gg/rayhitta",
    "github": "github.com/lostwordss"
  },
  "typewriter": ["t.me/cybrcriminal", "cybersec", "dms off"]
}

Count the new identifiers in that one hit. Two Telegram handles, one sitting in the typewriter rotation and one in the page metadata where almost nobody looks. A Discord account with its ID, username, and badge list, which is a fingerprint you can age and correlate. A GitHub. An invite server. The creation timestamp and view count for timing analysis. The full parse also carries the audio track list and cursor assets, which matter less, and the background gradient colors, which matter when you are linking aesthetic reuse across personas.

One of those Telegram handles resolved to an active threat actor channel. A platform that returned the page without the handles returned none of that. That is the argument for extraction over detection, in one result row.

Where osint.industries still wins

Mail-provider signup checks. The Microsoft and Outlook registration-existence checks remain the best in this test. If the question is "does this identifier have a mailbox," they answer it better than anyone here.

Email-first module flows. Their per-module email checks are mature and fast. For teams whose subjects arrive as email addresses rather than handles, the workflow is smooth.

Detection precision. 76.0 percent agreement with its own pool, second among the paid platforms to fingerprint.to's 77.9 percent. Their checks are conservative, which means fewer false positives to kill by hand.

If osint.industries is already in your stack, the case for a second tool is not that it is bad. Its pool stopped at 396 modules while Revealer found 488 accounts on the same handle, and its guns.lol row came back without the Telegram handles. Keep it for the mailbox checks. Add reach and extraction next to it.

How to choose

Two rules held up. Never build a conclusion on one tool's negative result, because a miss is usually a module difference, not a contradiction. And budget for the second tool from day one, because almost nobody in this field runs a single aggregator for long.

For the wider field, including the email OSINT side of the house, see our best username OSINT tools rundown, Epieos alternatives, and Sherlock alternatives.

Frequently asked questions

Which osint.industries alternative found the most profiles? Revealer.US, with 488 profiles from 751 sources checked for the username richard. fingerprint.to was second at 483 from 620. Same handle, same day, one run each.

Is there a free osint.industries alternative? Yes. WhatsMyName.io completed a 228/483 scan in this test. whatsmyname.app, a different host, is normally the curated community dataset. An AWS WAF captcha blocked our run, so it has no number here. The CLI stack of Sherlock, Maigret, and Blackbird is free and self-hosted. None of them carry breach data or the enrichment layer of a paid platform, which is the trade you make.

Does osint.industries check Microsoft accounts? Its email-provider module set, checking registration across Microsoft consumer domains, Outlook, and other mail providers, was the strongest in this test. It is the single best reason to keep it in a stack.

Why did the platforms return such different counts for the same username? Different module pools and different detection rules. A tool that checks 396 modules cannot return more hits than it attempts, and one tool's "found" can be another's "unverified" depending on how it reads the response. That is why this article reports sources checked and profiles found side by side.

What is the best free way to check a username across sites? WhatsMyName.io for a no-install web check that actually completed in this test (228/483). Maigret if you can run Python and want the deepest free sweep (319 on the default ~500-site subset). Verify anything you plan to report, because free checkers lean on methods that soft-404 sites fool.

Can I use these tools for background checks? Not Revealer. It is not a consumer reporting agency, and its data may not be used for employment, tenant, credit, or insurance eligibility decisions. Use an FCRA-compliant provider for those purposes.