Recruitment · head to head
Karat vs Manatal
Karat
Recruitment
Outsourced technical interviewing, engineers conduct your candidate interviews for you
- From
- On request
- Rated
- -
Manatal
Recruitment
Low-cost applicant tracking and recruitment CRM with candidate enrichment, sold to both in-house teams and agencies
- From
- On request
- Rated
- -
The short version
- Each has a real cost: Karat pricing is quote-only, so a buyer cannot model cost per hire against internal interviewer time without a sales conversation; Manatal pricing is per recruiter seat, so putting every hiring manager into the system to review candidates multiplies the licence count and erases the price advantage.
- They diverge on capability: Karat covers Interview Engineers, Manatal covers Candidate enrichment.
- Prices and features above were last checked on 1 September 2026.
Where they differ
Only the attributes on which Karat and Manatal actually diverge.
Identical on both: starting price (On request), free tier (No), platforms (Web), user rating (Not yet rated), category (Recruitment).
What each one covers
Drawn from each product's published feature list. An absence here means we hold no record of it - not that the product lacks it.
Only in Karat
- Interview Engineers
- Structured scoring
- High-volume screening
- Interview intelligence
- Scheduling integration
Only in Manatal
- Candidate enrichment
- AI candidate scoring
- Recruitment CRM
- Multi-board posting
- CV parsing
- Careers page builder
- Custom pipelines
- Reporting
What people use each for
The jobs each tool is most often brought in to do.
Karat
- A company scaling engineering headcount quickly and unable to free up enough internal engineer time for first-round technical interviewsnot Manatal
- A hiring team wanting a consistent bar across interviewers when growth has diluted interview qualitynot Manatal
- A seasonal hiring push, such as a new-grad or intern cohort, that needs a large volume of structured interviews in a short windownot Manatal
- A company that has measured interviewer no-shows or inconsistent scoring as a source of candidate drop-offnot Manatal
Manatal
- A small recruitment agency that needs client, candidate and placement tracking without an enterprise contractnot Karat
- An in-house team in Asia Pacific that wants candidate enrichment without paying enterprise ATS pricesnot Karat
- A company processing high applicant volume that needs automated ranking to triage a shortlistnot Karat
- A recruiter running both contingent search and internal hiring from one databasenot Karat
Where each one falls short
Documented limitations, not opinions. Every one is a constraint you would hit in normal use.
Karat
- Pricing is quote-only, so a buyer cannot model cost per hire against internal interviewer time without a sales conversation
- Handing the interview itself to a contractor removes the one part of the process where candidates meet a real employee, which can weaken employer brand for roles where culture fit matters most
- It solves interviewer capacity for technical screens but does not replace hiring manager or team-fit rounds, so it only removes part of the funnel
- Quality depends on how well the client-provided rubric and questions are written; a poor rubric produces poor Karat interviews just as it would internally
- Contracted interview volume can create pressure to keep using the service even after an internal hiring slowdown, since commitments are typically annual
- Candidates sometimes react negatively on discovering the interviewer is not an employee of the company they applied to, which is a reputational risk not visible until candidates start talking about it publicly
Manatal
- Pricing is per recruiter seat, so putting every hiring manager into the system to review candidates multiplies the licence count and erases the price advantage.
- Candidate enrichment depends on public profile data, so accuracy drops sharply in markets where professional networking sites have low penetration and the scores become misleading rather than useful.
- Integrations with major HRIS and payroll systems are limited compared with Western competitors, so new hire handover is frequently a CSV export rather than a sync.
- Support operates on Indochina business hours, which leaves European afternoons and all of North American working hours outside the responsive window.
- Reporting covers recruiter activity and source data but lacks the cohort and quality-of-hire analysis a mature talent function expects, and custom analysis means exporting to a spreadsheet.
Pricing, plan by plan
Karat
On request- Karat$undefined/year
- Interview Engineer coverage
- Structured technical interviews
- Interview analytics and calibration
Manatal
On request- Professional$undefined/month
- Per recruiter seat
- Unlimited jobs and candidates
- Candidate enrichment and AI scoring
- Enterprise$undefined/month
- Per recruiter seat at a higher rate
- Custom fields and advanced permissions
- Recruitment CRM for agency use
- Custom$undefined/year
- Negotiated terms for larger teams
- Single sign-on and security review
- Dedicated account management
Which should you pick?
Questions people ask
- Is Karat or Manatal better?
- Neither clearly leads. Karat starts at On request and Manatal at On request, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, Karat or Manatal?
- Karat starts at On request and Manatal at On request.
- Does Karat or Manatal run on more platforms?
- Both run on Web, so platform support will not decide this one for you.
- What is Karat best used for?
- Karat is most often used for a company scaling engineering headcount quickly and unable to free up enough internal engineer time for first-round technical interviews, a hiring team wanting a consistent bar across interviewers when growth has diluted interview quality, a seasonal hiring push, such as a new-grad or intern cohort, that needs a large volume of structured interviews in a short window, a company that has measured interviewer no-shows or inconsistent scoring as a source of candidate drop-off. Of those, a company scaling engineering headcount quickly and unable to free up enough internal engineer time for first-round technical interviews and a hiring team wanting a consistent bar across interviewers when growth has diluted interview quality are not what Manatal is typically brought in for.
- What can Karat do that Manatal cannot?
- Karat covers Interview Engineers, Structured scoring, High-volume screening, Interview intelligence. Manatal covers Candidate enrichment, AI candidate scoring, Recruitment CRM, Multi-board posting.
Answered from the vendors’ own pages
Karat: Do candidates know they are being interviewed by a contractor rather than an employee?
Karat discloses that Interview Engineers are trained third-party interviewers rather than employees of the hiring company, though disclosure practices are set by the client.
Manatal: Is it for agencies or in-house teams?
Both. The agency mode adds client and placement management; the corporate mode is a conventional ATS. Buying the wrong mode means paying for modules you never open.
Karat: Can Karat interview for languages or stacks outside mainstream web and backend engineering?
Coverage depends on the pool of Interview Engineers Karat maintains for a given stack; niche or highly specialised roles may have less coverage than common web and backend roles.
Manatal: How is it priced compared with other ATS products?
Per recruiter seat per month. That is not comparable with vendors that charge per active job slot or per employee, so model your real user count before comparing headline numbers.
Karat: Is pricing per interview or per contracted block of volume?
Karat does not publish pricing; it is quoted per client based on expected interview volume and contract term.
Manatal: Where is candidate data hosted?
On cloud infrastructure with GDPR commitments, but region-specific residency guarantees are weaker than EU-native vendors offer, which matters for regulated European buyers.
Manatal: Does the AI scoring actually work?
It ranks reliably against structured requirements such as skills and experience length. It is far less reliable where enrichment data is sparse.
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