GITNUXSOFTWARE ADVICE
Technology Digital MediaTop 10 Best Resize Image Software of 2026
Compare resize image software options and key tradeoffs for web, design, and workflow teams, including tools like Fotor, Squoosh, and TinyPNG.
How we ranked these tools
Vendors set the topic, but our editors set the inclusion criteria and the evaluation framework before research starts.
Agents extract product evidence with provenance, then collect written reviews and transcribe video reviews and podcasts.
Framework: Features 40% · Ease 30% · Value 30%
Gitnux may earn a commission through links on this page — this does not influence rankings. Editorial policy
Fotor is the best pick for teams that resize often in a browser because its interactive editor pairs crop and resize with export options like WebP and transparent PNG, whereas Squoosh is a strong alternative for people who want side-by-side encoder comparison and quick format switching while they tune results.
Editor’s top 3 picks
Three quick recommendations before you dive into the full comparison below — each one leads on a different dimension.
Fotor
Export flows include WebP and transparent PNG outputs after crop and resize in one interactive session.
Best for: Fits when teams need frequent small-scale resize and export inside a browser editor workflow.
Squoosh
Editor pickSide-by-side visual comparison of encoder outputs with live resizing and format switching.
Best for: Fits when teams need interactive resize tuning and format conversion without building a service.
TinyPNG
Editor pickFormat-specific PNG and JPEG compression that reduces file size while keeping visible artifacts low.
Best for: Fits when teams must reliably shrink PNG and JPEG assets for web delivery batches.
Comparison Table
Fotor
SMBOnline photo editor with a dedicated image resize tool.
Export flows include WebP and transparent PNG outputs after crop and resize in one interactive session.
Fotor is a web-based image editor that also handles resize workflows with an emphasis on quick output targeting for common social and web use cases. It supports multi-step editing around cropping and scaling, so resizing can be part of a broader thumbnail, banner, or image-optimization pass.
Resizing is available alongside format export options that include WebP and transparent-background PNG outputs for web assets. The workflow is oriented around interactive editing and export rather than an API-driven image transcoding pipeline.
- +Simple web UI for crop and resize with immediate visual feedback
- +Format export supports WebP and PNG with transparency preservation
- +Batch-like workflow through repeated exports without separate tooling
- +Editing controls let resizing be coordinated with framing and composition
- –Resize behavior relies on interactive steps instead of programmable API controls
- –No documented endpoint coverage for CDN edge resizing workflows
- –Batch throughput and automation limits are unsuitable for large migrations
- –Advanced print-focused controls like ICC profile embedding are not transparent
e-commerce teams
Standardize product images for listings
Cleaner, consistent product assets
marketing teams
Refine generated visuals for campaigns
Draft campaign visual assets
Show 2 more scenarios
small print-content teams
Prepare print projects with photos
Printable photo designs
Combine photos with templates and resize images to match calendar or framed print layouts.
social media managers
Resize photos for branded posts
Ready-to-publish promotional graphics
Use templates to resize and export images as ready-to-publish social graphics.
Best for: Fits when teams need frequent small-scale resize and export inside a browser editor workflow.
Squoosh
vertical specialistBrowser-based image compressor and resizer developed by Google Chrome team.
Side-by-side visual comparison of encoder outputs with live resizing and format switching.
Squoosh is a browser-based resize and transcode tool built around side-by-side image previews, which makes iterative tuning fast without standing up infrastructure. It supports common raster workflows like resizing with aspect ratio controls and converting between formats including WebP and AVIF.
The editor is complemented by export of the transformed output, plus a reusable set of encoding controls for consistent results. Batch-style throughput is not its main strength, which shifts it toward interactive optimization and lightweight pipelines.
- +Instant, in-browser preview makes resize and format changes quick to evaluate
- +Format conversion coverage includes WebP and AVIF for common web output paths
- +Encoding controls stay visible so output tuning is easy to repeat across images
- +Aspect ratio lock prevents accidental distortion during dimension changes
- –Batch resizing and bulk throughput are limited compared with CDN-style resizing
- –Automation and API access for thumbnail generation are not a primary workflow
- –EXIF and color management handling is inconsistent across transforms and exports
- –Large images can feel slow in the browser during repeated encoding runs
website teams
Resize images to fit page placements
Images sized to page
designers and freelancers
Adjust dimensions during a design workflow
Exported assets ready
Show 2 more scenarios
front-end developers
Tune compression for web delivery
Web-ready with tuned quality
Compare visual quality and compression settings, then export web-ready images for release.
small teams
Convert formats without desktop tools
Converted image output
Upload raster images and convert them in a browser workflow into exportable output formats.
Best for: Fits when teams need interactive resize tuning and format conversion without building a service.
TinyPNG
vertical specialistOnline service that compresses and resizes PNG and JPEG images using smart lossy techniques.
Format-specific PNG and JPEG compression that reduces file size while keeping visible artifacts low.
TinyPNG focuses on compression and optimization for PNG and JPEG, so it fits pipelines where bandwidth and page-load time matter more than control over interpolation methods. It supports bulk image processing through its web workflow and API endpoints, which makes it usable for recurring asset batches. The tool returns optimized files with consistent output behavior, which helps avoid quality drift during repeated publishing. Deployment choices range from manual uploads for small queues to API-driven processing for higher throughput.
A key tradeoff is limited control over resize math, since TinyPNG is not positioned as a full resizing engine with configurable Lanczos or bicubic interpolation. It also does not cover the full spectrum of formats that image-transcoding platforms handle, so teams needing WebP or AVIF conversion will need a separate stage. It is a good fit when a site’s art direction tolerates minor compression tradeoffs and the goal is smaller PNG and JPEG assets for delivery.
- +High-fidelity PNG and JPEG compression with consistent output sizes
- +Straightforward batch processing for upload-driven workflows
- +API supports integration into automated image pipelines
- +Optimized results align well with web delivery constraints
- –Resize control is limited compared with full resizing engines
- –Format coverage is narrower than CDN edge resizing tools
- –Does not replace a general transcoding pipeline for all asset types
- –Operational overhead increases for large-scale processing via API
Marketing operations teams
Compress campaign images for landing pages
Smaller files, steadier page loads
E-commerce catalog teams
Shrink product imagery at ingestion
Lower bandwidth per SKU
Show 2 more scenarios
Frontend engineering teams
Pre-optimize assets before release
Fewer oversized asset incidents
Run API-driven compression to reduce regression risk from manual file handling.
Agency production teams
Optimize client uploads in bulk
Faster client review cycles
Use upload batching to process multiple deliverables into a smaller, shareable set.
Best for: Fits when teams must reliably shrink PNG and JPEG assets for web delivery batches.
ShortPixel
SMBImage optimization and resizing service for websites and bulk processing.
Bulk optimization and resizing driven through API or CMS plugins with consistent batch targets.
ShortPixel handles batch resizing and image transcoding with configurable output dimensions and quality-oriented optimization settings. It also includes format conversion options for Web delivery needs and supports workflows that require preserving transparency behavior when resizing assets. Automation is a core fit signal because the service is built to run repeated conversions over many images, not only one-off edits.
- +Bulk processing for large image libraries
- +API and plugin-based automation for CMS workflows
- +Format conversion options for Web delivery
- +Configurable resize targets for consistent outputs
- –More suited to preprocessing than request-time edge resizing
- –Advanced per-image control needs repeat automation work
Ecommerce merchandising teams
Resize product gallery images in bulk
Lower transfer sizes per product
Marketing content teams
Reprocess campaign images after redesign
Consistent media performance across pages
Show 2 more scenarios
Agency operations teams
Automate resizing across many client sites
Reduced manual re-export work
Use API-driven runs to process assets repeatedly across multiple libraries with shared configuration.
WordPress administrators
Optimize media library via plugin
Faster site images with repeatable steps
Apply resizing and format conversion to existing uploads without custom scripts for each site.
Best for: Fits when teams need automated library-wide resizing and format conversion for web performance.
GIMP
SMBOpen-source raster image editor with scaling and resizing via interpolation algorithms.
Non-destructive layer workflows and history allow resizing after edits without losing intermediate steps.
GIMP provides interactive resizing tools with multiple interpolation modes, and it also supports automation through scripting with Script-Fu and Python via GIMP’s scripting hooks. Resizing happens in the context of layers, selections, and filters, which helps when changes require more than scale-only transforms. Export settings let users choose output formats and tune metadata handling during save.
- +Layer-based workflow lets resizing preserve complex edits
- +Multiple interpolation modes for deliberate downsampling and upscaling
- +Scripting enables repeatable resizing steps across many images
- +Wide file-format support for editing and export
- –No built-in RBAC or audit log for team resizing operations
- –Batch resizing is script-driven rather than API-based
- –High-volume server workloads require separate orchestration
- –Non-interactive pipelines need careful dependency management
Marketing designers
Prepare multi-size campaign images
Fewer manual remake cycles
Graphic operators
Standardize dimensions across batches
Consistent output specs
Show 2 more scenarios
Brand compliance teams
Maintain color intent during resizing
Reduced color drift risk
Apply sRGB conversion and export with controlled settings for downstream publishing.
Small web teams
Generate thumbnails from source files
Better visual QA
Resize layered assets into web-ready outputs with editor review before saving.
Best for: Fits when teams need designer-controlled resizing with scripted repeatability, not API-based CDN edge resizing.
VanceAI
vertical specialistAI image processing tools for upscaling and resizing images.
One workflow for batch resizing plus multi-format output aimed at derivative generation rather than custom CDN edge resizing.
VanceAI is a resize-focused image processing tool aimed at workflows that need repeatable transformations across many files. It handles common raster resizing tasks like generating smaller derivatives for web and keeping visual integrity through its resampling options.
- +Batch resizing supports turning large image sets into derivatives quickly
- +Multiple output formats cover typical web publishing needs
- +Resampling controls help manage downscaling appearance for smaller outputs
- +Simple workflow reduces friction compared with building a custom resizer pipeline
- –Limited evidence of granular control for print-grade metadata like DPI
- –Does not provide a documented, developer-first API surface for at-scale integration
- –Format conversions can vary in quality depending on source image characteristics
- –Fine-grained governance features like RBAC and audit logs are not clearly supported
social media creators
Resize and clean previews for posts
More publishable images
e-commerce teams
Upscale product images for listings
Larger product images
Show 2 more scenarios
photographers
Repair soft images after resizing
More usable images
Resize while running deblurring and artifact-reduction workflows to improve usability.
image production teams
Repair selected areas during cleanup
Fewer manual repair steps
Resize outputs and reconstruct masked regions to remove local defects and fill gaps.
Best for: Fits when small teams need batch resizing and basic conversions for web assets without engineering integration work.
Adobe Express
SMBTemplate-driven design app with an image resize feature.
Resize is integrated into Express templates and design canvases, so layout and cropping updates happen with export in one flow.
Adobe Express covers resize image tasks inside an editorial design workflow with export controls for common web and presentation outputs. Resizing is straightforward for quick batches of edited assets, but the tool centers on visual layout changes rather than a dedicated image transcoding pipeline.
- +Resize inside a design editor with consistent export settings
- +Fast handling for typical social and slide aspect ratios
- +Support for common raster export formats used in publishing workflows
- +Easy asset cleanup with background removal and basic edits
- –Limited support for print-oriented controls like ICC profile embedding
- –Batch resizing is not built for high-throughput, programmatic pipelines
- –Weak coverage for metadata preservation expectations like EXIF retention
- –No dedicated API surface for automated thumbnail generation
consumers and creators
Resize images for visual collage posts
Composed image layouts
event organizers
Resize images inside invitation templates
Personalized card designs
Show 2 more scenarios
business teams
Resize assets for branded documents
On-brand visual documents
Resize images while building presentation and document layouts using shared design styles.
job seekers
Resize images in resume designs
Visually consistent resumes
Format resume sections with consistent typography, then resize images for shareable outputs.
Best for: Fits when small teams need quick, editor-driven resizing for social posts and slide decks.
Kraken.io
API-firstImage optimization platform with resize and crop operations via API and web interface.
Configurable processing pipeline for batch resizing with deterministic output parameters per request.
Kraken.io performs image resizing and format conversion through API-driven processing that can run at CDN and backend scale. It supports common raster formats for both upload-time transformations and on-demand thumbnail generation.
- +API-based resizing and conversion fits thumbnail generation workflows.
- +Batch processing supports large queues without repeated manual operations.
- +Predictable resize outputs reduce edge-case tuning across many images.
- +Format conversion coverage supports mixed client display requirements.
- –Best results require careful parameter selection for each target size.
- –Advanced color management needs explicit handling in the pipeline.
ecommerce teams
Create product thumbnails and breakpoints
Consistent product dimensions
publishing teams
Bundle resizing with compression and conversion
More consistent outputs
Show 2 more scenarios
engineering teams
Optimize images during release builds
Optimized artifacts
Transform image assets as part of build and deployment so artifacts are delivered consistently.
web and digital marketing teams
Reduce delivery size before serving pages
Smaller transfers
Compress and convert images before serving them to landing pages and applications with defined settings.
Best for: Fits when teams need API thumbnail generation and batch resizing at production throughput.
Sirv
API-firstDynamic image hosting and resizing CDN for ecommerce and product imagery.
Bulk processing for existing libraries complements on-demand transformations for staged migrations and reprocessing.
Sirv is a resize image service built for production image processing where resizing requests are served from a managed pipeline. Its core capability centers on on-demand transformation that generates derivatives in common web formats while keeping image delivery CDN-friendly.
- +Request-based transformations reduce custom image-handling code in applications
- +Bulk processing helps migrate existing libraries into transformed outputs
- +Configurable transformation rules support consistent resizing across asset sets
- +Works well for CDN-centric delivery patterns with derivative caching
- –Advanced workflow needs can exceed simple parameter-only resizing
- –Complex color management like ICC handling may require careful configuration
- –Deep integration with custom image pipelines can feel constrained
- –Oversized batches can require queue and throughput planning
web developers
Resize images using URL transformation
Repeatable front-end outputs
ecommerce and web teams
Serve responsive product images automatically
More consistent storefront imagery
Show 2 more scenarios
marketing teams
Add clickable regions to resized images
Images that guide visitors
Map image regions to links so resized product imagery can direct visitors to related destinations.
web operations teams
Compress media in bulk libraries
Smaller delivery-ready assets
Process large image libraries into compressed web formats while supporting transparency when needed.
Best for: Fits when teams need managed, request-based image resizing with bulk migration and CDN delivery consistency.
PicWish
vertical specialistAI image editing suite including resize and crop tools.
Browser-based batch resizing with per-file aspect ratio handling for consistent derivative generation.
PicWish performs image resizing through a web-based workflow that focuses on batch operations and predictable output sizes. The tool targets common raster formats and Web outputs by generating resized derivatives rather than requiring manual editing.
- +Batch resizing workflow for producing many resized outputs quickly
- +Clear aspect ratio controls for consistent crops and dimensions
- +Web-first interface with minimal setup for resizing tasks
- +Format conversions for common web and media use cases
- –Limited control over advanced transcoding settings compared with developer tools
- –No documented API surface for automated resizing pipelines
- –Governance controls like RBAC and audit logs are not evident
- –High-volume throughput options are less explicit than CDN edge resizing tools
marketers
Upscale soft images for web channels
Higher-resolution web assets
marketing and communications teams
Remaster mixed-quality campaign libraries
More usable campaign assets
Show 2 more scenarios
small business creators
Clean unwanted marks after resizing
Cleaner images for reuse
Run automated workflows to remove selected unwanted elements so images are cleaner for approved reuse.
retail operations
Standardize SKU imagery in batches
Uniform image sets
Process grouped SKU images with consistent cutout, compositing, cropping, and enhancement requirements.
Best for: Fits when teams need recurring batch resizing in a browser workflow without building an image service.
Conclusion
After evaluating 10 resize image software, Fotor 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 Resize Image Software
Resize image software covers automated resizing and format conversion workflows that turn source images into web-ready derivatives, including batch optimization and request-time transformations. This guide covers ShortPixel, TinyPNG, GIMP, Squoosh, Kraken.io, Sirv, Fotor, Adobe Express, PicWish, and VanceAI based on how each tool handles batch throughput, control granularity, and integration approach.
Some products focus on preprocessing pipelines with API or CMS plugins, which is the core pattern behind ShortPixel and Kraken.io. Other options prioritize interactive or editor-driven resizing, including Squoosh’s in-browser comparison workflow and GIMP’s non-destructive layer history.
Resize Image Software for batch optimization, CDN-style transformations, and editor-driven exports
Resize image software generates resized derivatives by applying consistent output parameters across many images, including format switching and per-target dimension output for web delivery. Tools such as ShortPixel concentrate on batch optimization with API and plugin-driven automation that can standardize derivative generation across large libraries.
CDN-style use cases also shape product design, and Kraken.io focuses on API-based resizing and conversion with deterministic processing parameters that support thumbnail generation workflows at production throughput. Editor-oriented tools take a different path, where Squoosh emphasizes interactive encoder output comparison for rapid resize and format tuning without building a service, and GIMP emphasizes repeatable layer workflows for resizing after edits through its non-destructive history.
Resize Image Software evaluation criteria that affect output and throughput
Resize image software outcomes depend on how each tool drives batch resizing targets, format conversion, and encoder settings across many images. The best tools keep output consistent when scaling libraries or generating thumbnails at production throughput.
API-driven batch resizing for library-wide derivatives
ShortPixel automates bulk optimization and resizing through API and CMS plugins with consistent batch targets. Kraken.io provides API thumbnail generation with a configurable processing pipeline for batch resizing at production throughput.
CDN-style request transformations vs preprocessing pipelines
Sirv centers on request-based transformations with bulk processing to migrate existing libraries into transformed outputs. ShortPixel is more suited to preprocessing automation than request-time edge resizing.
Deterministic parameter control for repeatable outputs
Kraken.io uses a configurable pipeline that produces deterministic output parameters per request for batch thumbnail generation. PicWish focuses more on browser batch resizing workflow and consistent aspect ratio handling than on developer-first parameter determinism.
Color management controls for print and asset pipelines
Kraken.io requires explicit handling for advanced color management in the pipeline to avoid unexpected results. Sirv flags complex color management like ICC handling as requiring careful configuration for consistent output.
Editor and workflow history for non-destructive resizing after edits
GIMP supports non-destructive layer workflows and history so resizing can occur after edits without losing intermediate steps. Adobe Express integrates resizing inside templates and design canvases so layout and cropping updates export with the design flow.
Interactive encoder comparison for tuning resize and format conversion
Squoosh offers side-by-side visual comparison of encoder outputs with live resizing and format switching to speed up resize tuning. Fotor provides export flows that combine crop and resize with WebP and transparent PNG outputs in an interactive session.
Choose Resize Image Software by integration depth and transformation workload shape
The decision starts with where resizing runs. Some tools fit preprocessing and batch pipelines using API or plugin automation, while others fit interactive tuning or editor-driven export flows.
Map resizing to batch automation or request-time generation
If resizing must standardize derivatives across large libraries through API or CMS plugins, ShortPixel is built around batch optimization and resizing automation. If thumbnail generation needs API resizing with large queue throughput, Kraken.io targets production throughput for per-request processing.
Pick a control style for output tuning and consistency
For deterministic output parameters that reduce per-image drift across queues, Kraken.io uses a configurable processing pipeline. For interactive evaluation where outputs are compared visually before deciding parameters, Squoosh supports live resizing with side-by-side encoder comparisons.
Select the deployment model that matches existing workflows
If existing assets must be migrated into managed transformed outputs with request-based transformations, Sirv supports bulk processing alongside on-demand transformations. If the workflow is primarily browser-based and teams want recurring batch resizing without building a service, PicWish focuses on a browser batch workflow with aspect ratio controls.
Validate format conversion coverage for the derivative targets
If the delivery stack depends on WebP and AVIF conversion options in an evaluation-first workflow, Squoosh includes format conversion coverage that spans WebP and AVIF. If the requirement centers on reducing PNG and JPEG file size with consistent visible quality, TinyPNG emphasizes format-specific PNG and JPEG compression with consistent output sizes.
Match print-oriented metadata needs to the tool’s control depth
If print-grade metadata and advanced color handling must be managed explicitly, Kraken.io and Sirv both flag the need for careful color management handling in their pipelines. If the job is small-scale editorial export where interactive settings and transparent outputs matter, Fotor combines crop and resize with WebP and transparent PNG export.
Ensure resizing is non-destructive or editor-integrated when edits are iterative
If resizing must happen after designer edits without discarding intermediate work, GIMP offers non-destructive layer history for resizing after edits. If resizing is tied to templated social or slide layout changes where export matches the canvas, Adobe Express integrates resizing into the design workflow rather than a separate automation pipeline.
Who benefits from specific Resize Image Software approaches
Teams choose resize image software based on whether resizing is part of a programmatic image pipeline or an interactive design workflow. The best fit depends on how much control, automation, and throughput the workload requires.
Engineering teams generating thumbnails at scale through an API
Kraken.io is built for API-based resizing and conversion with batch processing that supports large queues for thumbnail generation workflows. This model reduces custom image-handling code when derivatives must be produced consistently for many requests.
Content teams running library-wide optimization with CMS integrations
ShortPixel focuses on bulk processing and automation driven through API or CMS plugins with consistent batch targets. This supports resizing and format conversion across large libraries without manual reprocessing steps per asset.
Design workflows that require non-destructive resizing after layered edits
GIMP supports non-destructive layer workflows and history so resizing can happen after edits while preserving intermediate steps. This fits iterative creative edits where the resize is not the first operation.
Asset teams that need request-based transformations plus migration support
Sirv supports request-based transformations and adds bulk processing to migrate existing libraries into transformed outputs. This combination reduces the need to rebuild resizing into every application path.
Teams that validate encoder output visually before standardizing settings
Squoosh provides side-by-side visual comparison of encoder outputs with live resizing and format switching. This makes it practical when teams need to tune resize settings for acceptable output quality across formats.
Common failure modes when adopting Resize Image Software
Resize image software failures usually appear as inconsistent output sizes, missing format coverage for the delivery stack, or a mismatch between interactive workflows and automated pipelines. Fixing these issues requires aligning the tool’s execution model with the required derivative workload.
Choosing an interactive tool for high-throughput automation
Squoosh limits batch resizing and bulk throughput compared with CDN-style resizing, so it does not match production queue workloads for large libraries. Kraken.io is designed for API-based batch resizing where throughput and queue handling matter.
Over-relying on format compression tools when true resizing control is required
TinyPNG provides strong PNG and JPEG compression, but its resize control is limited compared with full resizing engines. Kraken.io or ShortPixel are better matches when consistent resizing targets across many dimensions matter.
Assuming color management works automatically across print and web targets
Kraken.io calls out that advanced color management needs explicit handling in the pipeline, which can affect print-grade outcomes. Sirv similarly notes that ICC handling may require careful configuration for consistent output.
Expecting browser batch tools to offer developer-first automation endpoints
PicWish provides browser batch resizing workflow and aspect ratio controls, but it does not provide a documented API surface for automated pipelines. ShortPixel provides API and plugin-driven automation for CMS workflows that require integration.
Building iterative creative edits on a tool without non-destructive history
GIMP supports non-destructive layer workflows and history so resizing can occur after edits without losing intermediate steps. Adobe Express integrates resizing into templates and canvases, which works for design export flows but not for layer-history-driven iterative editing.
How We Selected and Ranked These Tools
We evaluated each resize image software tool on features that affect resizing correctness and output consistency, then weighted automation and batch throughput to match real derivative workloads. Features account for 40% of the score, ease accounts for 30% and value accounts for 30%.
ShortPixel set the pace because its bulk optimization and resizing automation works through API and CMS plugins with consistent batch targets, which fits library-wide preprocessing and standardized derivatives. Kraken.io ranked highly for production throughput because API-based resizing and conversion align with thumbnail generation workflows using configurable processing parameters.
Frequently Asked Questions About Resize Image Software
How do Cloudinary and Imgix differ for API-based image resizing at scale?
Which tool is better for bulk image resizing across a large library: ShortPixel, Sirv, or Kraken.io?
When should Squoosh be chosen over API services like Cloudinary or Kraken.io?
How do TinyPNG and ShortPixel handle PNG and JPEG differently for web asset preparation?
What breaks if EXIF and color metadata handling are not preserved during resizing?
Which tool is most suitable for admin-level governance and audit visibility: Sirv, Kraken.io, or ShortPixel?
How do teams integrate resize workflows with existing systems when they need an API thumbnail generation pipeline?
When does a browser-based workflow like PicWish or Fotor fall short compared with server-side transformation?
What is the main tradeoff between interactive control and deterministic batch outputs: Squoosh versus Imgix or Cloudinary?
Tools reviewed
Primary sources checked during evaluation.
Referenced 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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