Free Lusha Alternatives (2026): Verified Contacts Without Per-Seat Fees

On this pagev

• What Lusha does well
• What a reader searching for a free alternative actually needs
• Why free database credits bounce
• The free options by architecture
• When paying makes sense
• A practical test you can run this week
• Key takeaways

A free contact credit can still produce a bounce, because our probes saw 82 refusals from 617 definitive verdicts (13.3%, 2026-03-19 to 2026-07-05) and 141 catch-all domains from 211 resolved domains (66.8%). If you need a small number of researched contacts each week, the Kustiq free tier gives 3 full profiles per week with a free account and 1 per week without an account, with a live SMTP handshake at delivery. If you reveal contacts inside LinkedIn at high volume, Lusha plan economics can win at 1,000 or more reveals a month (verified on the Lusha public pricing page, 2026-10-20). Free database tiers work as a starting list, and you should treat every address from a database as unverified until a server checks it close to send time.

Full disclosure. We build Kustiq, and this post compares our free tier with other approaches. We link to our own benchmark data where we use our numbers, and we say where another tool fits better than ours.

What Lusha does well

Lusha sells plan-bundled contact credits through a Chrome extension. That design fits a rep who already works inside LinkedIn all day. The rep finds a person in a browser tab. The extension reveals contact details in that same place. No export step interrupts the flow. No separate research tab needs attention. Managers can also reason about usage across the team. Finance can tie spend to monthly credit caps without a custom report.

Plans run $49.90 to $399.90 per month with monthly credit caps, and the self-serve plans cover teams of up to 5 users. The Pro plan costs $69.90 per month for 600 credits. The Premium plan costs $399.90 per month for 3,400 credits. An email costs 1 credit and a phone number costs 5 credits. The free plan gives 40 credits per month. Verification is a database match, rather than a live handshake.

That model has a clear strength at volume. A team that lives in LinkedIn can reveal contacts where the work already happens. Reps keep context from the profile they just read. A phone number matters to some teams more than a company profile does. A plan bundle also matches a sales floor with stable headcount. The honest note is simple. At 1,000 or more reveals a month inside LinkedIn, Lusha plan economics can win. Our Kustiq versus Lusha comparison lays out the unit difference without pricing games.

The search phrase behind this post points at a different job. A founder at a small B2B company rarely needs a seat for every person who touches outbound. That founder may need a handful of good accounts researched well each week. A per-seat minimum is a poor fit for occasional use. A monthly database credit also has a cost that shows up in the bounce report. The rest of this post separates those jobs before it names options.

What a reader searching for a free alternative actually needs

People searching this phrase want two different things. One reader needs volume. Another reader needs certainty on a short list. Those readers should pick different tools. A volume reader builds a list first and writes later. A certainty reader picks named accounts first. The certainty reader then needs the right person at each account. That reader also needs a reason to write that survives a reply.

For the certainty reader, a contact reveal alone leaves work undone. Someone still needs to understand the company. Someone still needs to spot a buying signal worth mentioning. Someone still needs to draft an opener that fits the account. A founder doing their own outbound does that extra work alone. The lookup took seconds on a good day. The research around the lookup took the rest of the hour. A free tool that returns only an address saves the smallest part of the job.

Seat pricing also shapes small team behaviour in quiet ways. A founder shares a login when one person would use a seat only a few times a month. A contractor waits for the founder to run lookups for them. Research batches pile up around one bottleneck person. Tools without per-seat fees remove that friction for mixed teams. Kustiq has no per-seat fees. That detail matters when a part time BD helper joins for a campaign. It matters less when a fixed sales team uses one workflow every day.

Volume changes the answer again. A team building large lists from LinkedIn search pages wants speed on LinkedIn. Extension based reveals match that way of working well. A research tool that starts from a domain would slow that team down. We would rather say that here than win a signup that churns in a week. Match the tool to the way you already work. Then test accuracy on your own accounts before you scale anything.

Why free database credits bounce

A database stores what was true when a record entered the system. People change jobs after that moment. Companies change email providers after that moment. Mailboxes get deleted during reorganisations. A stored record carries no signal from the receiving server today. That gap explains why a free credit can cost sender reputation. The credit cost nothing at lookup time. The bounce arrived at send time.

Our benchmark measures that gap. We recorded 3,508 SMTP probe outcomes across 4,127 processed contacts in the benchmark published at /blog/b2b-catch-all-email-benchmark. Of 617 probes with a definitive server verdict, 502 accepted (81.4%), 82 refused (13.3%), 28 had no MX record left (4.5%), and 5 were catch-all detected at probe (0.8%), all from the sample. A database match cannot see that refusal coming. A live handshake asks the server before you send.

Catch-all domains add a harder problem on top. A catch-all server accepts mail for addresses that may not exist as real mailboxes. Our sample resolved 211 B2B domains and found 141 of them catch-all (66.8%). On those domains a guessed pattern looks deliverable to naive checks. Syntax checks pass. Domain checks pass. The server still tells you nothing about the person. Honest tools label catch-all results instead of counting them as verified. We explain the handshake itself in how to verify an email without sending.

Third party testing points the same way. Sparkle ran a 450-lead test and measured a 13.3% verifier bounce on Apollo database output (Sparkle test, n=450). Sample caveats matter for our data too. Our sample comes from the Kustiq outbound funnel across 4,127 processed contacts, with SMB and mid-market B2B companies overrepresented. Timeouts and proxy outages left 2,860 of 3,508 probe outcomes (82%) without a mailbox verdict. We publish no industry cut from that sample for that reason. The direction still holds across both sources. Stored addresses fail live checks at a rate that hurts small senders fast.

A small sender bears more cost from each bounce than a large sender does. A founder may send from the main company domain. That domain also carries invoices and product mail. Reputation damage spreads beyond the campaign that caused it. A short list with confirmed mailboxes beats a long list with hopeful pattern guesses. That trade sits at the centre of the free question.

The free options by architecture

Kustiq free tier

Paste a B2B domain into Kustiq and you get a 20-field company profile with one SMTP-verified decision-maker contact, a buying signal, and a ready-to-send opener in about 60 seconds at the median. The free tier gives 3 profiles per week with a free account, 1 per week without an account, and no card. The output is the full profile rather than a teaser record. Every email gets a live SMTP RCPT TO handshake at delivery. Catch-all results carry a label and do not count as verified. You can try the no-signup path at /try.

The unit here is a researched profile for a named account. That unit suits target account work and founder led outbound. It suits a BD person who works a short named list each week. It fits poorly when you want to scrape reveals from a LinkedIn results page. We have no browser extension workflow to offer for that way of working. A reader who works that way should keep Lusha on the shortlist. A reader who needs research depth on named accounts gets the research included here.

The manual method

You can do this work by hand with no tool spend at all. Find the company site and confirm what the company sells. Find a named leader on public pages. Work out the address pattern from a published address at that domain. Then run a handshake check before you send anything. Our guide on how to find someones business email walks through that process step by step. The method costs time rather than money. It also teaches you how weak a pattern guess can be.

Pattern guessing deserves its own warning from our data. Of 70 domains with one identifiable email format, first.last@ led at 15.7% and the top three formats together covered about 40% (n=70). Ten formats shared a nearly flat distribution in that same sample of 70 domains. A guess based on the most common pattern misses most of the time on that evidence. On catch-all domains the guess passes basic checks and still tells you nothing about the mailbox. Manual work stays viable for a handful of high value accounts each week. It breaks down as a habit when the list grows past what your calendar allows.

Free database tiers in general

Many vendors in this category offer a monthly credit allowance on a stored database. We describe the category here without naming vendors or prices beyond the Lusha facts. The pattern is familiar across the category. You search people or companies in an interface. You spend a credit to reveal an address. The address comes from records the vendor collected earlier. Freshness depends on when that record last met a live check. Vendors rarely show you that date on the record itself.

Decay is the structural problem for this architecture. People leave roles and mailboxes close behind them. A record that was accurate at collection time drifts every month after. Our refusal share of 82 from 617 definitive verdicts (13.3%) shows what drift looks like at send time. A free monthly allowance still has uses. It can start a list for enrichment work. It can help you map an org chart roughly. You should not send to those addresses without a live check near send time. Readers comparing this category may also want our Hunter io alternatives post for the finder side of the market.

When paying makes sense

Free tiers cover evaluation and light weekly use. Paid tiers start to make sense when the weekly list grows. They also make sense when the research around each contact takes time away from selling. Compare paid units carefully at that point. A profile and a reveal are different units. Price per month alone hides that difference.

Kustiq Insight costs $39 per month for 200 credits and Kustiq Pro costs $119 per month for 800 credits, 1 credit equals 1 profile, and there are no per-seat fees (as of October 2026). Lusha Pro costs $69.90 per month for 600 credits. One Kustiq credit buys a verified 20-field profile with a contact, a signal, and an opener. One Lusha credit buys an email reveal and a phone number costs 5 credits. A team that needs phone numbers inside LinkedIn will read that comparison differently from a founder who needs researched accounts. Both readings can be right for their own way of working.

Deeper Kustiq workflows have their own credit math worth knowing before you buy. The full Targeted Outreach pipeline costs 3.0 credits per prospect and produces a 4-touch email sequence and Kustiq does not send email. Contact Finder takes a company list and returns up to 3 SMTP-verified contacts per company at 1 credit per company with batches up to 1,000 companies. Effective per-profile cost runs $0.15 on Pro at full use to $0.30 on extra credits. Account Watchlist rechecks tracked accounts weekly on the public web with 5 accounts free, 100 on Insight, and 500 on Pro. Full tier detail lives on /pricing.

Providers also differ in ways a credit count hides. Confirmed-deliverable share by platform in our data ran 15.8% for Google Workspace (273 of 1,729), 9.9% for Microsoft 365 (115 of 1,166), 11.2% for Proofpoint (16 of 143), and 3.3% for Mimecast (5 of 154), across all processed contacts. Those shares include infrastructure failures. They show why verification at delivery time matters more than a stored badge. Gateways refuse or drop probes at different rates. A live check close to send time respects that reality.

A practical test you can run this week

Pick accounts you already know well for a fair test. Former customers work well for this. Friendly partners work well too. You know the right people at those companies already. You know which addresses should exist. Run each free option against that known set. Score what comes back against ground truth you trust. A vendor demo list tells you nothing about your market.

Score more than raw hit rate during that test. Note whether catch-all results carry an honest label. Note whether the contact sits at decision-maker level or in a generic inbox. Note how much research came with the address. Note how long you spent after the lookup before you could write. A tool that saves lookup time and leaves research time untouched saves little. A tool that returns a signal you can quote in an opener saves real calendar time.

Then check the addresses again close to a real send. A result from last month may have decayed since. Mailboxes close between research day and send day. Servers change answers as gateways shift. This habit protects a small domain better than any list size trick. It also keeps your test honest over time. Tools earn trust through repeated checks on your accounts. No benchmark post can grant that trust in advance, including ours.

Watch your own behaviour during the trial week as well. Did you stay inside LinkedIn for most prospecting hours? Did you start from a named account list instead? That observation picks the tool type for you. Extension tools serve reps who work in LinkedIn. Domain-first research tools serve named account work. Buying a tool that does not fit how you work wastes money on either side.

Key takeaways

• Free database credits carry bounce risk because 82 of 617 definitive probe verdicts were refusals (13.3%, 2026-03-19 to 2026-07-05).
• Catch-all handling matters because 141 of 211 resolved domains were catch-all (66.8%), and honest tools label those results.
• Low weekly volume with named accounts fits a researched profile unit, while high-volume LinkedIn reveals fit an extension with monthly credit caps.
• Lusha free gives 40 credits per month (verified on the Lusha public pricing page, 2026-10-20), and Lusha economics can win at 1,000 or more LinkedIn reveals a month.
• Test any free option against accounts you already know, and recheck addresses close to send time.

Frequently Asked Questions

What is the best free alternative to Lusha for a few contacts a week?

Kustiq fits low weekly volume because the free tier returns a full researched profile with a live SMTP check. A founder working named accounts gets company research with the contact rather than a bare reveal.

How many free credits does Lusha give?

The Lusha free plan gives 40 credits per month according to the Lusha public pricing page. An email costs 1 credit and a phone number costs 5 credits on the same page.

Why do free database contacts bounce?

Stored records decay after collection and no server has confirmed the mailbox recently. Our benchmark saw 82 refusals from 617 definitive verdicts (13.3%) once live probes asked the receiving servers directly.

Does Kustiq verification work on catch-all domains?

Kustiq labels catch-all results at delivery and does not count them as verified. Our sample found 141 catch-all domains from 211 resolved domains (66.8%), so the label matters often.

When does Lusha beat a free research tool?

Lusha economics can win at 1,000 or more reveals a month inside LinkedIn based on the public pricing page. Extension based reveals match reps who prospect inside LinkedIn search results all day.