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VerdictPal · editorial desk · updated 16 Oct 2026VerdictPal
Compare · Head-to-head

ASReview LAB vs Rayyan: pricing, privacy & capabilities

Put two dossiers side by side: fit, cost, privacy, and the failure modes that actually change the choice.
ToolsModels

Compare · Tool A / Tool B·Head-to-head

ASReview LABvsRayyan

In the Systematic review set, ASReview LAB edges ahead overall (editorial fit 70 vs 60). ASReview LAB wins on citation honesty, source quality, privacy controls, and value for money; Rayyan wins on speed. Both pass our quality gate. The right pick is the one whose strengths match the job you're trying to do.

ASReview LAB · avg
67
wins 11 of 16
Rayyan · avg
67
wins 5 of 16
Fields differ
13
of 16 dossier rows
Receipts
20
sources + bench notes

Checked 2026-10-12 · ASReview LAB·Checked 2026-09-14 · Rayyan

Dossier fields

Sixteen fields, side by side.

No.FieldASReview LABRayyan
01Status · Editorial quality gateLightweightSolid
02Editorial fit · 0–100 score70 / 10060 / 100
03CategorySystematic reviewSystematic review
04Primary surfaceLocal appProsumer SaaS
05Modalities
  • Text
  • Data
  • Text
  • Data
06Role in a stackLocal active-learning screening for systematic reviewsSystematic review screening and collaborative triage
07Best for
  • PhD students or librarians with thousands of titles and abstracts to screen against defined inclusion criteria
  • Reviews that need inspectable methods and records kept on a local machine or an institution's own server
  • Students with no licence budget who can install Python or arrange a self-hosted setup
  • Methods work comparing active-learning models on a fully labelled dataset
  • Screening titles and abstracts in systematic or scoping reviews
  • Teams that need blinded/collaborative inclusion decisions before full-text review
  • Students learning review workflows who need dedupe, labels, PICO hints, and PRISMA support in one workspace
  • Institutions that want centralized review workflows with ResearchPilot and larger reviewer/viewer pools
08Avoid if
  • You need full-text review, extraction, risk-of-bias assessment and PRISMA reporting in one platform
  • You cannot install Python or Docker and have no institutional host
  • Nobody on the review team can justify and document a stopping rule
  • You expect the tool to find papers that your database search never retrieved
  • You need collaborators to inherit premium features on reviews you do not own without an Institutional plan
  • You only need free screening for more than three active reviews simultaneously
  • You want PRISMA automation or AI screening without checking tier limits and audit requirements
  • Your review library includes sensitive, unpublished, or identifiable material that cannot live in a cloud review workspace
09Capabilities tracked
  • Title and abstract screening with human relevance labels
  • Active-learning ranking updated from your labels
  • CSV, RIS and Excel dataset import
  • Prior-knowledge labels to initialise the model
  • Preconfigured ELAS models and custom model components
  • Simulation on fully labelled datasets
  • Editable labels, tags and screening notes
  • Automatic hiding of duplicate titles and texts in version 3
  • Progress dashboard with a reviewer-set stopping threshold
  • Export of labels and records to CSV, TSV, Excel or RIS
  • Export and re-import of an entire .asreview project
  • Self-hosted LAB Server with authentication and multiple reviewers
  • Title/abstract screening
  • AI/ML/NLP prioritization
  • labels/facets/filters
  • duplicate detection
  • auto-resolve duplicates on paid tiers
  • PICO extraction AI on Advanced
  • PRISMA Flow Diagram Generator
  • mobile app as premium feature
  • Free/Essential/Advanced reviewer-viewer pools
  • institutional ResearchPilot suite
  • subscriptions auto-renew by default
10Failure modes · Documented, not buried
  • A run of irrelevant records does not prove there are no relevant studies left. Stopping early can miss studies; the dashboard has no default threshold and the docs say the ideal threshold is still being researched.
  • The model can only rank the records you import. An incomplete search or missing titles and abstracts can leave relevant studies outside the useful screening pool.
  • Your labels train the ranking. The SAFE paper warns that a seed set chosen only from familiar key papers can bias the model, and recommends a random training sample.
  • Free software still needs installation and maintenance. The current package requires Python 3.10 or later; a shared server also needs someone responsible for hosting and access.
  • Screening is not the whole review. Full-text eligibility checks, extraction, risk-of-bias assessment and reporting still need a separate process.
  • Automatic duplicate hiding in version 3 changes what appears during screening. PyPI says hidden duplicates can be included in exports; do not treat the visible screening count as a complete deduplication report.
  • Free tier caps active reviews and collaborators
  • Free reviewers/viewers do not inherit premium AI/PICO/mobile features
  • Institutional plans cannot be purchased online and require sales setup
  • Quarterly billing costs more than annual
  • ResearchPilot is institutional-only beyond help mode
  • AI screening/extraction still require human audit
11Evidence levelSynthesizedSynthesized
12Sources verified · Receipts on the dossier
13Benchmark entries
  • Check who makes screening decisions
    Current documentation assigns ranking to the model and relevance labels to the human. Simulation uses a dataset whose labels are already known.
  • Check the stopping threshold
    The progress docs confirm no default threshold. The SAFE paper proposes four phases and explicitly warns that its numeric examples are not exact rules.
  • Check version, licence and data handling
    PyPI lists 3.0.8, Apache-2.0 and Python 3.10+. PyPI and the docs state no usage or user-data collection; the docs describe LAB Server as self-hosted.
None recorded
14Alternatives tracked
  • Rayyan
  • Covidence
  • Elicit
  • EPPI-Reviewer
  • Covidence
  • DistillerSR
  • ASReview
  • EPPI-Reviewer
  • Colandr
15Pricing checked2026-10-122026-09-14
16Last verified2026-10-122026-09-14
16 metrics

Sixteen numbers, one shared midline.

ASReview LABvsRayyan

In the Systematic review set, ASReview LAB edges ahead overall (editorial fit 70 vs 60). ASReview LAB wins on citation honesty, source quality, privacy controls, and value for money; Rayyan wins on speed. Both pass our quality gate. The right pick is the one whose strengths match the job you're trying to do.

  1. Editorial fit
    ASReview LAB70Rayyan60
  2. Source quality
    ASReview LAB70Rayyan55
  3. Citation honesty
    ASReview LAB75Rayyan66
  4. Privacy controls
    ASReview LAB85Rayyan53
  5. Value for money
    ASReview LAB85Rayyan78
  6. Speed
    ASReview LAB40Rayyan70
  7. Integration
    ASReview LAB65Rayyan57
  8. Onboarding
    ASReview LAB40Rayyan70
  9. Reliability
    ASReview LAB0Rayyan76
  10. Feature depth
    ASReview LAB75Rayyan90
  11. Data portability
    ASReview LAB80Rayyan49
  12. Transparency
    ASReview LAB80Rayyan98
  13. Ecosystem reach
    ASReview LAB65Rayyan57
  14. Documentation
    ASReview LAB70Rayyan55
  15. Independence
    ASReview LAB85Rayyan53
  16. Affordability
    ASReview LAB85Rayyan79
ASReview LAB · avg 67 · wins 11 of 160avg 67 · wins 5 of 16 · Rayyan
EvidenceASReview LAB 4 · Rayyan 0
  1. 01Editorial fit
    70
    60
  2. 02Source quality
    70
    55
  3. 03Citation honesty
    75
    66
  4. 04Privacy controls
    85
    53
PrivacyASReview LAB 2 · Rayyan 2
  1. 05Value for money
    85
    78
  2. 06Speed
    40
    70
  3. 07Integration
    65
    57
  4. 08Onboarding
    40
    70
CapabilityASReview LAB 1 · Rayyan 3
  1. 09Reliability
    0
    76
  2. 10Feature depth
    75
    90
  3. 11Data portability
    80
    49
  4. 12Transparency
    80
    98
Cost & fitASReview LAB 4 · Rayyan 0
  1. 13Ecosystem reach
    65
    57
  2. 14Documentation
    70
    55
  3. 15Independence
    85
    53
  4. 16Affordability
    85
    79
TOOL A · ASReview LAB
Systematic review
ASReview LAB
70/100
ROLELocal active-learning screening for systematic reviews
Editorial fit
70
Source quality
70
Citation honesty
75
Privacy controls
85
Value for money
85
Speed
40
FREEASReview LAB
LightweightVerified 2026-10-12VP·METHOD

Local active-learning screening for systematic reviews — Free, open-source title and abstract screening: ASReview LAB learns from your relevance labels and re-ranks imported records locally, but the stopping decision is still yours.

TOOL B · Rayyan
Systematic review
Rayyan
60/100
ROLESystematic review screening and collaborative triage
Editorial fit
60
Source quality
55
Citation honesty
66
Privacy controls
53
Value for money
78
Speed
70
FREE
$4.99/MOper seat
$8.33/MOper seat
SolidVerified 2026-09-14VP·METHOD

Systematic review screening and collaborative triage — Cloud systematic-review screening workspace for title/abstract triage, deduplication, labels, PICO extraction, PRISMA flow support, collaborators, mobile screening, and AI-assisted inclusion decisions.

Privacy facets

Where each tool is actually private.

ASReview LABRayyan
Training on your dataYesYes: In the standard local workflow, ASReview trains a model on your project's relevance labels; this is local active learning, not vendor-cloud training. The developers state that ASReview LAB collects no usage or user data. On a self-hosted server, your operator controls the data environment. Optional extensions and hosted demos need separate checks; their external API use is not verified here. Sources: ASReview introduction and PyPI, checked 12 Oct 2026 (linked in Sources).UnknownUnknown: Not recorded as a plan-wide guarantee. Check the dossier's privacy notes and sources.
EU data residencyUnknownUnknown: UnknownUnknownUnknown: Not recorded as a plan-wide guarantee. Check the dossier's privacy notes and sources.
SOC 2 attestationUnknownUnknown: UnknownUnknownUnknown: Not recorded as a plan-wide guarantee. Check the dossier's privacy notes and sources.
Local-first by defaultYesYes: ASReview LAB runs locally; LAB Server is self-hosted by you or your institution. The local-data statement covers that setup, not an unverified hosted demo or every optional extension.UnknownUnknown: Not recorded as a plan-wide guarantee. Check the dossier's privacy notes and sources.
Capability split

What each tool does uniquely.

Only ASReview LAB12

  • Title and abstract screening with human relevance labels
  • Active-learning ranking updated from your labels
  • CSV, RIS and Excel dataset import
  • Prior-knowledge labels to initialise the model
  • Preconfigured ELAS models and custom model components
  • Simulation on fully labelled datasets
  • Editable labels, tags and screening notes
  • Automatic hiding of duplicate titles and texts in version 3
  • Progress dashboard with a reviewer-set stopping threshold
  • Export of labels and records to CSV, TSV, Excel or RIS
  • Export and re-import of an entire .asreview project
  • Self-hosted LAB Server with authentication and multiple reviewers

Shared0

No overlap detected.

Only Rayyan11

  • Title/abstract screening
  • AI/ML/NLP prioritization
  • labels/facets/filters
  • duplicate detection
  • auto-resolve duplicates on paid tiers
  • PICO extraction AI on Advanced
  • PRISMA Flow Diagram Generator
  • mobile app as premium feature
  • Free/Essential/Advanced reviewer-viewer pools
  • institutional ResearchPilot suite
  • subscriptions auto-renew by default
Failure modes

Where each tool breaks.

ASReview LAB

  • A run of irrelevant records does not prove there are no relevant studies left. Stopping early can miss studies; the dashboard has no default threshold and the docs say the ideal threshold is still being researched.
  • The model can only rank the records you import. An incomplete search or missing titles and abstracts can leave relevant studies outside the useful screening pool.
  • Your labels train the ranking. The SAFE paper warns that a seed set chosen only from familiar key papers can bias the model, and recommends a random training sample.
  • Free software still needs installation and maintenance. The current package requires Python 3.10 or later; a shared server also needs someone responsible for hosting and access.
  • Screening is not the whole review. Full-text eligibility checks, extraction, risk-of-bias assessment and reporting still need a separate process.
  • Automatic duplicate hiding in version 3 changes what appears during screening. PyPI says hidden duplicates can be included in exports; do not treat the visible screening count as a complete deduplication report.

Rayyan

  • Free tier caps active reviews and collaborators
  • Free reviewers/viewers do not inherit premium AI/PICO/mobile features
  • Institutional plans cannot be purchased online and require sales setup
  • Quarterly billing costs more than annual
  • ResearchPilot is institutional-only beyond help mode
  • AI screening/extraction still require human audit
Pricing

Pulled straight from each dossier.

ASReview LAB

Systematic review
  • FREEASReview LAB

raw · Free, open source under Apache-2.0. No paid software tiers found in the checked sources. PyPI lists ASReview LAB 3.0.8, released 18 Jun 2026, requiring Python 3.10 or later. Local installation has no licence fee; your own hosting, IT support and setup time are separate costs.

Rayyan

Systematic review
  • FREE
  • $4.99/MOper seat
  • $8.33/MOper seat

raw · Live 2026-09-14 from rayyan.ai/pricing: Free $0 — 3 active reviews, 2 free reviewers. Essential $4.99/seat/month billed annually or $8.33/month billed quarterly. Advanced $8.33/seat/month billed annually or $13.33/month billed quarterly (9 active reviews, PICO extraction AI). Business/Academic $41.67/license/month billed annually starting at 5 licenses ($2,500); academic discounts available. Enterprise contact sales. Annual billing saves up to 40%.

Verdict

Which tool to actually pick.

In the Systematic review set, ASReview LAB edges ahead overall (editorial fit 70 vs 60). ASReview LAB wins on citation honesty, source quality, privacy controls, and value for money; Rayyan wins on speed. Both pass our quality gate. The right pick is the one whose strengths match the job you're trying to do.

Open ASReview LAB's dossierOpen Rayyan's dossier
Try ASReview LAB ↗Try Rayyan ↗

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Questions this comparison answers

Every answer below is assembled from the dated fields on this page. Nothing is written separately for search.

Should you use ASReview LAB or Rayyan?

In the Systematic review set, ASReview LAB edges ahead overall (editorial fit 70 vs 60). ASReview LAB wins on citation honesty, source quality, privacy controls, and value for money; Rayyan wins on speed. Both pass our quality gate. The right pick is the one whose strengths match the job you're trying to do.

What can ASReview LAB do that Rayyan cannot?

Capabilities documented on the ASReview LAB tool but not the Rayyan tool:

  • Title and abstract screening with human relevance labels
  • Active-learning ranking updated from your labels
  • CSV, RIS and Excel dataset import
  • Prior-knowledge labels to initialise the model
  • Preconfigured ELAS models and custom model components
  • Simulation on fully labelled datasets
  • Editable labels, tags and screening notes
  • Automatic hiding of duplicate titles and texts in version 3
  • Progress dashboard with a reviewer-set stopping threshold
  • Export of labels and records to CSV, TSV, Excel or RIS
  • Export and re-import of an entire .asreview project
  • Self-hosted LAB Server with authentication and multiple reviewers

What can Rayyan do that ASReview LAB cannot?

Capabilities documented on the Rayyan tool but not the ASReview LAB tool:

  • Title/abstract screening
  • AI/ML/NLP prioritization
  • labels/facets/filters
  • duplicate detection
  • auto-resolve duplicates on paid tiers
  • PICO extraction AI on Advanced
  • PRISMA Flow Diagram Generator
  • mobile app as premium feature
  • Free/Essential/Advanced reviewer-viewer pools
  • institutional ResearchPilot suite
  • subscriptions auto-renew by default

Is ASReview LAB or Rayyan cheaper?

ASReview LAB: ASReview LAB Free. Rayyan: Free Free · Essential (annual) $4.99/mo · Advanced (annual) $8.33/mo · Institutional $41.67/mo.

How do ASReview LAB and Rayyan score against each other?

On editorial fit, ASReview LAB scores 70 out of 100 and Rayyan scores 60 out of 100. The score is a fit judgement for source-heavy research work, not a quality ranking — read both dossiers before you pick.