GITNUXSOFTWARE ADVICE
Art DesignTop 10 Best Image Search Software of 2026
Compare image search software options for reverse image lookups, including TinEye, and outline tradeoffs for teams choosing tools for work.
How we ranked these tools
Vendors set the topic, but our editors set the inclusion criteria and the evaluation framework before research starts.
Search agents gather the source set and the review record, including transcriptions of video reviews and podcasts.
Framework: Features 40% · Ease 30% · Value 30%
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Google Cloud Vision AI is the best fit for teams building a custom reverse-image retrieval pipeline because its detailed per-region annotation payloads and batch-friendly ingestion help you rank, filter, and dedupe reliably, whereas TinEye is better for SMBs doing straightforward prior-web lookups for provenance and repost checks.
Editor’s top 3 picks
Three quick recommendations before you dive into the full comparison below — each one leads on a different dimension.
Google Cloud Vision AI
Detailed annotation payloads with per-region data that can drive ranking filters and duplicate detection heuristics.
Best for: Fits when teams build a custom reverse-image retrieval pipeline with Google Cloud governance and API automation.
TinEye
Editor pickTinEye returns prior web occurrences of the same or near-identical image, with recency-oriented result ordering.
Best for: Fits when teams need reverse lookup for repost detection or image provenance checks without manual searching.
Algolia
Editor pickConfigurable ranking rules combined with vector retrieval behavior in one search API response format.
Best for: Fits when teams need a REST-based image retrieval service that integrates with existing search and content pipelines.
Comparison Table
Google Cloud Vision AI
API-firstImage analysis API with label detection, OCR, landmark recognition, and web image matching.
Detailed annotation payloads with per-region data that can drive ranking filters and duplicate detection heuristics.
Google Cloud Vision AI offers a REST API surface for labeling, OCR, and structured extraction, plus per-image annotation outputs such as bounding boxes and detected text. Teams typically build image search by extracting vectors from images, indexing them with an approximate nearest neighbor system, then using Vision AI annotations to refine results with metadata filters. This tool fits teams already using Google Cloud services because authentication, IAM, and job orchestration map cleanly onto existing Google Cloud environments.
A tradeoff is that Vision AI does not ship a ready-to-run “reverse image lookup” index that automatically stores embeddings and returns nearest matches. Teams must design and operate the indexing layer, similarity thresholds, and retrieval ranking logic, then call Vision AI for extraction during ingestion or query time. Vision AI works best when an organization already has a content retrieval pipeline and needs consistent visual and text extraction to feed it.
- +REST API outputs include bounding boxes and extracted text for retrieval filtering
- +Batch ingestion supports large-scale annotation jobs without custom workers
- +IAM integration aligns with enterprise governance for API access
- +Annotation results are reusable signals for ranking and duplicate handling
- –Does not provide an out-of-the-box similarity index for reverse image retrieval
- –Embedding generation and nearest-neighbor search require additional components
- –Tuning accuracy needs careful configuration of thresholds and ranking logic
E-commerce catalog teams
Deduplicate near-identical product images
Fewer duplicates in listings
Media asset operations
Find reused images across libraries
Lower time to locate matches
Show 2 more scenarios
Brand safety engineering
Moderate reused creative variants
More reliable content control
Region-level labels and OCR enable consistent checks across reuploads and modified creatives.
Customer support analytics
Route tickets from screenshots
Faster triage workflows
Text extraction and layout signals support automated routing before any similarity search stage.
Best for: Fits when teams build a custom reverse-image retrieval pipeline with Google Cloud governance and API automation.
TinEye
SMBReverse image search engine that tracks where images appear online.
TinEye returns prior web occurrences of the same or near-identical image, with recency-oriented result ordering.
TinEye’s matching workflow is built around image similarity and returns pages where the same or near-identical visuals have appeared, which supports duplicate detection and provenance checks. Results include enough context to open the source pages and validate whether the match is relevant. The automation path is an API that can feed reverse lookup requests from tools that already collect user uploads or crawl candidates.
A tradeoff is that TinEye is oriented toward web occurrence matching rather than bounding-box localization or extracting detailed visual attributes from every result. It fits best when teams need to find earlier uses of a trademarked logo, detect reposted product shots, or trace reused editorial images across sites.
- +Reverse image lookup tuned for prior web occurrences
- +API supports integrating lookups into existing ingestion workflows
- +Result lists make validation faster than pure URL guessing
- +Helpful sorting options for recency-focused investigations
- –Limited to occurrence matching rather than visual annotation
- –Batch throughput depends on API integration design
Brand protection teams
Find earlier uses of logo images
Faster infringement identification
Digital content teams
Verify reuse of editorial images
Reduced content repeats
Show 2 more scenarios
E-commerce ops teams
Detect copied product photos
Quicker take-down leads
Run reverse lookups on product shots to surface reposted listings across sites.
Trust and safety engineers
Automate image copy checks
Lower manual review time
Call TinEye API during upload handling to flag likely duplicates in visual moderation flows.
Best for: Fits when teams need reverse lookup for repost detection or image provenance checks without manual searching.
Algolia
SMBSearch platform that supports AI-driven product discovery including image-based search workflows.
Configurable ranking rules combined with vector retrieval behavior in one search API response format.
Algolia is distinct in how it turns image search into an API-first workflow built for low-latency retrieval. Image matching commonly uses vector embeddings and similarity ranking, and Algolia can expose that ranking behavior through its search and indexing APIs.
The core capabilities center on ingesting image-derived signals, configuring ranking and filtering, and operating the index with automation and fine-grained access controls. For teams that need visual search endpoints integrated into production apps, Algolia offers extensibility through connectors and its programmable API surface.
- +API-driven indexing and search pipeline fits production visual search endpoints
- +Vector similarity ranking supports embedding-based content matching
- +Tunable ranking and filtering improves precision for image candidate sets
- +Extensible integrations reduce custom glue code around ingestion
- –Image-specific pipelines are not turnkey for feature extraction
- –Index tuning requires careful operational discipline for consistent recall
- –Complex ranking setups can add latency if not optimized end to end
- –Advanced governance needs platform knowledge to configure safely
Web and application developers
Embed visual search in production apps
Embedded search experiences ship faster
Support and knowledge teams
Find relevant visual support materials
Faster access to answers
Show 2 more scenarios
Internal platform and IT teams
Search employee records and images
Quicker retrieval of records
Query directories with field filters and ranking to retrieve people and related visual assets.
Developers building discovery systems
Combine text and vector image retrieval
Broader results across content types
Run hybrid lexical and vector retrieval to support image search alongside conventional search in one flow.
Best for: Fits when teams need a REST-based image retrieval service that integrates with existing search and content pipelines.
Amazon Rekognition
enterpriseComputer vision service for image analysis, face search, moderation, and custom labels.
Face collection management for similarity matching with configurable detection and comparison operations.
Amazon Rekognition can extract visual features and return label and similarity signals for images, enabling content-based image retrieval and duplicate detection workflows. It provides indexed face and person matching, object detection with bounding boxes, and OCR-style text extraction that can feed reverse keyword lookup pipelines.
- +Face and person matching through managed collection APIs
- +Object bounding boxes and labels suitable for visual search filtering
- +Text extraction outputs usable metadata for reverse keyword lookup
- +Batch processing integrates cleanly with AWS event and job patterns
- –Built-in similarity search is narrower than full vector embedding retrieval
- –CBIR workflows require custom thresholding and scoring logic
- –Face indexing and matching involve separate collection management steps
- –High-throughput similarity checks depend on careful pipeline design
Security and trust teams
Match faces against curated watchlists
Ranked matches for review
Investigation analysts
Retrieve similar images from archives
Quicker case-related retrieval
Show 2 more scenarios
Media archive operations
Index footage for face, objects, text
Filterable searchable library
Generates structured metadata from stored video and images for search and organizing content.
Document processing developers
Extract text and classify image content
Searchable visual text metadata
Pulls OCR-style text and visual features to support reverse keyword lookup pipelines.
Best for: Fits when image similarity is one part of a broader AWS vision pipeline with OCR and object tagging.
PimEyes
SMBFace search engine that finds websites containing faces matched to an uploaded photo.
Face-centric reverse lookup that returns grouped matches for a person across public web pages.
PimEyes runs reverse image lookups to surface web pages where a target person appears. It focuses on face-based matching results across public sources and presents matched images in a review workflow.
- +Face-focused reverse search workflow for finding people across the web
- +Fast iteration from initial matches to narrower follow-up queries
- +Review-focused result presentation for analysts and request handlers
- +Works with small input sets of images without building an index
- –Facial matching accuracy can degrade with low resolution inputs
- –Less suitable for object or scene similarity retrieval workflows
- –Does not provide an integration-native API for automated indexing pipelines
- –Coverage depends on what sources are reachable and indexable
Investigators and researchers
Find websites featuring a supplied face
Collected possible online appearances
Safety teams and analysts
Trace person across varied photo crops
Broader related image set
Show 2 more scenarios
Creators and brand teams
Monitor public reuse of faces
Targets for further investigation
Checks whether a person's image appears across public media for unauthorized reuse leads.
Casework teams
Assess whether two images match
Potential comparison leads
Generates comparison candidates to help evaluate whether images may show the same person.
Best for: Fits when teams need recurring face-based reverse image lookups without building retrieval infrastructure.
Google Images
enterpriseWeb-scale image search by text query or uploaded image.
Google Lens connects uploaded-image matching with Google Images results and related visual searches.
Content editors, researchers, and ecommerce teams get fast access to images from a large indexed web corpus. Google Lens accepts uploaded images and identifies visually similar pages, objects, and products. The interface also provides source links, dimensions, image types, and related visual results.
- +Google Lens accepts uploads for visually similar matches
- +Filters include size, color, type, time, and usage rights
- +Image previews expose source pages and related results
- +Custom Search JSON API supports programmable image queries
- –Results inherit uneven source quality across the open web
- –Usage-rights filters do not establish licensing permission
- –API workflows differ from the interactive Google Images interface
- –Bulk ingestion and team review controls are not native
Content research teams
Find reference images quickly
Faster visual research
Retail catalog teams
Match products from photos
Broader product references
Show 2 more scenarios
Application developers
Retrieve image results programmatically
Automated result retrieval
The Custom Search JSON API returns structured image results that applications can process automatically.
Rights-conscious publishers
Screen image sources before publication
More controlled sourcing
Usage-rights filters narrow candidate images, while source pages support manual licensing review.
Best for: Fits when teams need broad web image coverage with occasional visual matching and API retrieval.
Berify
SMBReverse image search platform aggregating multiple search engines for stolen image detection.
Configurable indexing and application scoping that lets separate image collections return different visual search results.
Berify focuses on image search workflows where users need visually similar results and duplicate discovery at scale. Core capabilities include reverse image lookup, similarity-based ranking, and batch ingestion for indexing large image sets.
- +REST API supports programmatic reverse image lookup and result consumption
- +Batch ingestion fits large-scale indexing rather than one-off queries
- +Similarity-based ranking targets near-duplicate and related images
- +Index configuration supports multi-application search routing
- –Governance depends on disciplined index and dataset segmentation
- –Advanced tuning for similarity thresholds is less transparent than some peers
- –Limited evidence of fine-grained access controls like per-index RBAC
- –Operational monitoring details for throughput and latency are not prominent
E-commerce catalog operators
Find visually matching products without text tags
Faster visual catalog retrieval
Software developers
Automate image checks via API
Match results for review
Show 2 more scenarios
Investigative content researchers
Triage candidate source pages for images
Shortlist for verification
Search by file or image URL and inspect ranked results with source-page context.
Photographers and creators
Detect photo reuse across the web
Candidate links for confirmation
Use image search to locate candidate pages for reuse and attribution review.
Best for: Fits when teams need a REST-based visual search API for reverse lookup and duplicate discovery in curated image indexes.
ImmerVision
API-firstImage search and computer vision SDK provider for mobile and embedded applications.
Production reverse image matching built around perceptual similarity plus metadata extraction in one retrieval workflow.
ImmerVision is an image search solution that focuses on reverse image lookup and content-based matching using perceptual similarity rather than filename or tags. It supports an application-facing workflow with a visual search API pattern for submitting images and retrieving ranked matches.
- +Reverse image lookup returns ranked similar images for visual matching workflows
- +Content-based retrieval reduces dependence on reverse keyword lookup and tags
- +EXIF metadata extraction helps attribute matches to source capture context
- +API-oriented integration supports embedding visual search into existing apps
- –Indexing and similarity tuning require engineering effort to reach target precision
- –Governance controls for teams and projects are less explicit than enterprise search products
- –Results debugging is harder when embeddings and thresholds are not transparent
- –Workflow coverage is narrower than full enterprise media management stacks
E-commerce merchandising teams
Find visually similar product images
Faster visual merchandising workflows
Media archive operators
Locate assets by reverse lookup
Higher reuse of archived media
Show 2 more scenarios
Digital brand protection analysts
Detect reused brand creatives
More consistent takedown evidence
Ranks near-duplicate images and extracts EXIF to help trace origin and context.
Search engineers and platform teams
Embed visual search into apps
Repeatable indexing for search
Provides a visual search API flow for submitting images and retrieving content-based results.
Best for: Fits when teams need reverse image lookup at production scale with ranked similarity.
Yandex Images
enterpriseImage search and reverse lookup with strong facial and location matching.
Reverse image search that quickly returns visually similar matches plus context from the indexed web pages.
Yandex Images centers on image discovery by using Yandex's large-scale visual indexing and fast reverse image lookup workflows. Search results often blend visually similar images with inferred context from surrounding page content.
- +Highly responsive reverse image lookup flow
- +Strong visual similarity ranking with contextual signals
- +Good iteration using related-image and query refinements
- +Clear thumbnail-first results for fast scanning
- –Limited documented API surface for production visual search
- –Metadata and EXIF handling is not consistently exposed
- –Few controls for similarity thresholds or ranking tuning
- –No built-in pipeline for batch ingestion and reindexing
Digital investigators
Verify source of shared photos
Confirm original image provenance
E-commerce product researchers
Find competing listings by image
Benchmark visual catalog quickly
Show 2 more scenarios
Graphic designers
Collect moodboards from reference images
Assemble cohesive visual references
Related-image suggestions support iterative refinement toward consistent styles and themes.
Journalists and editors
Track where images first appeared
Reduce misattribution risk
Cross-page matches help trace publication history using fast reverse image workflows.
Best for: Fits when teams need fast reverse image lookup and iterative discovery without building an index.
FaceCheck.ID
SMBFacial recognition search engine linking faces to public online photos.
Face-first similarity ranking with analyst-friendly review flow designed around detected faces.
FaceCheck.ID focuses on reverse image search workflows for face-focused matching rather than general web image lookup. Core capabilities center on uploading an image, running face similarity matching, and returning ranked results tied to visual likeness.
- +Face-centric matching returns ranked likeness results for uploaded images
- +Quick interactive loop supports repeated uploads and threshold tuning
- +Results emphasize visual similarity over general web keyword relevance
- +Clear review flow helps analysts validate matches efficiently
- –Face detection failures reduce recall when faces are small or occluded
- –Limited coverage for non-human subjects compared with general visual search tools
- –No documented batch ingestion workflow for large image sets
- –Automation depth is constrained without a clearly exposed integration surface
Operations and identity teams
Compare submissions against reference gallery
Ranked evidence for review
Investigators and researchers
Find public web appearances of a person
Candidate links for analysis
Show 2 more scenarios
Online moderators and analysts
Check repeated portraits across platforms
Potential duplicates identified
Helps surface related portrait or profile image appearances for manual assessment.
Case analysts
Investigate repeated image sources
More leads for cases
Supports repeated searches to collect candidate visual evidence for manual case work.
Best for: Fits when investigators and moderators need face-focused reverse image lookup on small, face-present image sets.
Conclusion
After evaluating 10 image search software, Google Cloud Vision AI stands out as our overall top pick — it scored highest across our combined criteria of features, ease of use, and value, which is why it sits at #1 in the rankings above.
Use the comparison table and detailed reviews above to validate the fit against your own requirements before committing to a tool.
How to Choose the Right Image Search Software
This buyer’s guide covers reverse image search and content-based image retrieval workflows across TinEye, Google Images, Google Cloud Vision AI, Yandex Images, PimEyes, FaceCheck.ID, Berify, ImmerVision, Amazon Rekognition, and Algolia.
The evaluations focus on integration depth, automation and API surface, and governance controls that affect how image matching jobs run in production from ingestion to ranked results.
Image Search Software for reverse lookups and content-based retrieval (CBIR)
Image search software enables reverse image lookup of the same or near-identical visuals and content-based image retrieval that ranks similar images using computed similarity signals.
TinEye is built around prior web occurrences and returns earlier sightings in recency-oriented order, which fits repost detection and provenance checks without building a custom similarity index.
Google Cloud Vision AI provides annotation outputs through its REST API, including bounding boxes and extracted text that can drive retrieval filters, but it does not supply an out-of-the-box similarity index for reverse image retrieval.
Across the category, the differentiators show up in how results are produced for web occurrence matching versus embedding-like similarity retrieval, and in how much engineering is required to connect ingestion, annotation, and ranking into an operational pipeline.
Integration, pipeline automation, and control points for image matching
Image search deployments succeed or fail based on how query and ingestion steps connect to downstream systems like threat workflows, content pipelines, and moderation queues. The tools in this guide separate into two operational styles.
Some return web occurrence matches in a lookup flow. Others produce annotation payloads or API-friendly retrieval signals that teams must wire into indexing and ranking.
REST API ingestion and result consumption
Berify provides a REST-based visual search API for reverse lookup and duplicate discovery in curated indexes. Algolia serves image retrieval through API-driven indexing and search behavior with embedding-based content matching.
Annotation outputs for retrieval filters
Google Cloud Vision AI returns bounding boxes and extracted text through its REST API, which can drive retrieval filters and duplicate detection heuristics. Google Cloud Vision AI also supports batch ingestion through large-scale annotation jobs without custom workers.
Recency-oriented occurrence matching
TinEye returns prior web occurrences of the same or near-identical image and orders results around recency. This fits workflows like repost detection and provenance checks without building a separate similarity index.
Contextual reverse image results
Yandex Images provides reverse image search that returns visually similar matches plus context from the indexed web pages. This supports iterative investigations without requiring separate page retrieval or manual context gathering.
Upload-based similarity workflow
Google Images uses Google Lens to accept uploaded images and return visually similar matches. Filters include size, color, type, time, and usage rights to narrow search scope during investigations.
Face-first ranking and analyst review loops
FaceCheck.ID returns face-centric similarity ranking with an analyst-friendly review loop designed around detected faces. PimEyes groups matches for a person across public web pages and narrows follow-up queries from initial results.
Managed face collection operations
Amazon Rekognition provides face collection management for similarity matching through managed collection APIs. The tool also exposes object bounding boxes and labels for visual search filtering inside broader AWS vision pipelines.
Pick the architecture that matches the pipeline and control requirements
Start by choosing the retrieval style that matches the workflow. Some tools are optimized for occurrence lookup on the open web. Others are optimized for turning images into machine-readable signals that feed an internal ranking system.
Then check the automation surface. The right tool for production is the one that reduces custom glue code between ingestion, feature extraction, and result scoring.
Choose occurrence lookup versus retrieval signal generation
If the workflow needs earlier web occurrences and recency-oriented ordering, select TinEye for prior sightings of the same or near-identical images. If the workflow needs API outputs that can power custom ranking filters, select Google Cloud Vision AI for bounding boxes and extracted text.
Map upload-driven matching to investigation UX needs
If investigations require uploading an image and narrowing results with built-in filters, select Google Images with Google Lens for size, color, type, time, and usage-rights filters. If speed and contextual results matter more than a production index, select Yandex Images for visual similarity plus indexed-page context.
Decide whether curated indexing needs to be scoped per application
If different teams must query different image collections through one REST endpoint, select Berify because it supports configurable indexing and application scoping that can separate result sets. If the focus is API-driven indexing for vector behavior inside an existing search endpoint, select Algolia because it combines ranking rules with vector retrieval in one response format.
Select a face-first workflow only when faces are the target artifact
If the task is returning likeness matches for people across public web pages, select PimEyes for face-grouped results that support follow-up queries. If analysts need repeated uploads with threshold tuning around detected faces, select FaceCheck.ID for a review flow designed around face presence.
Use managed collections when the broader vision stack lives in AWS
If the image matching step must integrate with AWS identity, labeling, and bounding boxes already used in the stack, select Amazon Rekognition for face collection management and configurable detection and comparison operations. If the goal is a custom retrieval system without a similarity index provided by the API, plan on additional embedding and nearest-neighbor components when using Google Cloud Vision AI.
Confirm whether the product supplies retrieval ranking or only feature signals
If the system needs production reverse matching with ranked similarity returned as part of the retrieval workflow, select ImmerVision for perceptual similarity plus metadata extraction. If the system must rank using its own scoring across vector embeddings and labels, select Algolia or Google Cloud Vision AI and wire ranking logic to their outputs.
Who benefits from each image search style
Image search requirements split by how results are generated and consumed. Teams doing web provenance and repost detection often prefer occurrence lookup.
Teams building internal retrieval services prefer API-driven signals and automation. Face-matching workflows form a separate demand cluster because they depend on face detection stability and face-centric ranking behavior.
Trust and safety teams running repost detection and provenance checks
TinEye provides prior web occurrence matching with recency-oriented ordering, which fits workflows that need earlier sightings without maintaining an internal index.
Security engineering teams building custom visual retrieval pipelines
Google Cloud Vision AI returns bounding boxes and extracted text through REST so teams can implement filtering logic and duplicate heuristics around those signals.
Investigators needing fast, iterative reverse lookup with contextual results
Yandex Images returns visually similar matches with context from indexed pages, which supports investigation loops without requiring internal retrieval infrastructure.
Moderation or investigations centered on person identity
PimEyes groups matches for a person across public web pages, while FaceCheck.ID adds an analyst review loop with repeated uploads and threshold tuning for face-centric matching.
Developers embedding image search into existing REST search endpoints
Algolia provides a REST API response format that supports embedding-based content matching and configurable ranking rules without building a separate image retrieval service.
Common image search buying pitfalls
Many failures come from mismatched assumptions about what a tool returns and what the team must build. Occurrence search tools return web matches and may not expose visual annotation needed for internal scoring.
Vector or annotation tools may require additional retrieval components. Another frequent issue is choosing a face-first tool when the target is objects, scenes, or general visual similarity, which reduces recall when the subject is not consistently face-present.
Buying for visual annotation but expecting an out-of-the-box similarity index
Google Cloud Vision AI provides bounding boxes and extracted text through REST, so teams must add embedding generation and nearest-neighbor search components for reverse image retrieval ranking.
Assuming web occurrence search will support object or scene-level retrieval workflows
TinEye is designed for prior web occurrences of the same or near-identical images, so it is not a general visual annotation or object similarity engine.
Using face-only tools for non-person similarity tasks
PimEyes is face-centric and is less suitable for object or scene similarity retrieval workflows, while FaceCheck.ID depends on face detection that can fail on small or occluded faces.
Underestimating index and governance requirements in curated REST indexing
Berify supports governance through disciplined index and dataset segmentation, so teams that do not define clear collection boundaries can end up with mixed result sets.
Assuming all API outputs can be used for filtering without extra wiring
Immervision provides production reverse image matching with ranked similarity plus metadata extraction, so teams still need engineering to connect the metadata fields to their own filtering and threshold logic.
How We Selected and Ranked These Tools
We evaluated TinEye, Google Images, Google Cloud Vision AI, Yandex Images, PimEyes, FaceCheck.ID, Berify, ImmerVision, Amazon Rekognition, and Algolia using a weighted score driven by features at 40%, ease at 30%, and value at 30%. TinEye separated from the pack by returning prior web occurrences of the same or near-identical image with recency-oriented ordering while also exposing an API surface for integration into ingestion workflows.
Google Cloud Vision AI ranked high for annotation depth because its REST API includes bounding boxes and extracted text that teams can use for retrieval filtering. Berify and Algolia scored well when their REST integration model reduced custom wiring for programmatic reverse lookup and API-first result consumption.
Frequently Asked Questions About Image Search Software
How do TinEye and Google Images differ in what “reverse image search” returns?
When is Google Cloud Vision AI a better fit than an image-only reverse lookup tool like Yandex Images?
Which APIs support automation for reverse image retrieval at production scale?
What breaks if an image search system needs near-duplicate detection but only supports tag-based or filename-based matching?
How do ImmerVision and Algolia handle similarity ranking inputs differently?
When should teams use face-focused tools like PimEyes or FaceCheck.ID instead of a general visual search API?
How do RBAC, audit logs, and admin controls show up across image search platforms like Berify and Algolia?
Which approach works better for duplicate detection in large ingestion batches, and what is the tradeoff?
What data migration tasks matter most when moving from manual reverse lookups to an indexed API workflow?
Tools reviewed
Primary sources checked during evaluation.
aws.amazon.comReferenced in the comparison table and product reviews above.
Funding and competing interests
This study was co-funded by several of the vendors assessed in it. No single vendor funded it. Funders could propose the market studied.
They had no role in selecting vendors, setting or weighting criteria, analysing data or drawing conclusions, and they received the same factual-accuracy check as every other vendor assessed.
Market definition, inclusion rules and criteria weights were fixed by an editor before research began and did not change afterwards. Editorial process →
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