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Top 10 Best Sandboxing Software of 2026

GITNUXSOFTWARE REVIEW

Cybersecurity Information Security

Top 10 Best Sandboxing Software of 2026

Compare sandboxing software options for enterprise security testing, including fit, tradeoffs, and limits, with tools like Menlo Security reviewed.

19 min readUpdated Editorially scoped research
How we ranked these tools
01Editorial Scope

Vendors set the topic, but our editors set the inclusion criteria and the evaluation framework before research starts.

02Source Research

Agents extract product evidence with provenance, then collect written reviews and transcribe video reviews and podcasts.

Read our full methodology →

Framework: Features 40% · Ease 30% · Value 30%

Gitnux may earn a commission through links on this page. This does not influence our reviews. Editorial policy

Sandboxing software tools separate untrusted files, URLs, and scripts from user and endpoint environments to reduce exposure from malware and web-borne payloads. This comparison helps scanners weigh mechanisms like evasion-resistant detonation, browser isolation, and API-first automation so incident response, threat hunting, and detonation pipelines can match expected throughput and coverage.

VMRay is the best choice for security teams that need evidence-grade detonation reporting because its behavioral recording turns execution into analyst-readable artifacts for triage, while gVisor fits teams running untrusted container workloads since its user-space syscall interception model reduces host kernel exposure.

Editor’s top 3 picks

Three quick recommendations before you dive into the full comparison below — each one leads on a different dimension.

Editor pick
1

VMRay

Evidence-grade behavioral recording that maps execution actions to analyst-readable artifacts.

Best for: Fits when security teams need evidence-rich detonation reports for SOC triage and malware investigation.

2

Menlo Security

Editor pick

Traffic-level detonation of user web sessions routes results back into enforcement so policy can react to observed behavior.

Best for: Fits when web-borne threats demand repeatable isolation for interactive browsing sessions.

3

gVisor

Editor pick

Sentry user-space kernel intercepts and mediates system calls to enforce isolation inside container runtimes.

Best for: Fits when containerized services must run untrusted or third-party workloads with reduced kernel exposure.

Comparison Table

1
VMRayBest overall
enterprise
9.5/10
Overall
2
enterprise
8.7/10
Overall
3
API-first
8.6/10
Overall
4
8.3/10
Overall
5
8.2/10
Overall
6
API-first
7.9/10
Overall
7
7.6/10
Overall
8
7.1/10
Overall
9
API-first
6.8/10
Overall
10
6.7/10
Overall
#1

VMRay

enterprise

Hypervisor-based malware analysis sandbox with evasion-resistant detonation.

9.5/10
Overall
Features9.5/10
Ease of Use9.5/10
Value9.5/10
Standout feature

Evidence-grade behavioral recording that maps execution actions to analyst-readable artifacts.

VMRay supports detonation of suspicious files and web links, then correlates what the sample attempts to do during execution. The output emphasizes analyst evidence, including observed behaviors and artifacts that support incident decision-making. Deployment can be aligned to an organization’s environment by integrating the sandbox outputs into downstream processes through available interfaces.

A key tradeoff is that the sandboxing pipeline requires operational discipline to keep workloads, network access, and observation settings aligned with what is being tested. Teams use it when malware volume is high and when investigations require repeatable evidence rather than quick labels. It also fits scenarios where analysts need to validate exploit behavior and post-execution persistence attempts using the produced behavioral records.

Pros
  • +Behavior-focused reports provide investigator-grade evidence
  • +File and URL detonations cover common delivery paths
  • +Automation-friendly outputs support integration into SOC workflows
  • +Repeatable execution context improves analysis consistency
Cons
  • –Operational tuning is needed to maintain reliable observations
  • –High-detail reports can increase analyst time per case
  • –Integration requires planning around existing case management
Use scenarios
  • SOC analysts

    Triage suspicious attachments behaviorally

    Faster verdicts and cleaner triage

  • Threat intel teams

    Validate URL-delivered payloads

    Stronger reporting and confidence

Show 2 more scenarios
  • Incident response teams

    Reproduce exploit attempts

    More actionable containment steps

    Use controlled execution to observe what the sample tries to do and document key behaviors.

  • Security engineering

    Automate sandbox-driven investigations

    Reduced manual case handling

    Feed suspected artifacts into the detonation workflow and route resulting reports into existing tooling.

Best for: Fits when security teams need evidence-rich detonation reports for SOC triage and malware investigation.

#2

Menlo Security

enterprise

Browser isolation platform that executes web content in remote sandboxed environments.

8.7/10
Overall
Features8.7/10
Ease of Use8.7/10
Value8.7/10
Standout feature

Traffic-level detonation of user web sessions routes results back into enforcement so policy can react to observed behavior.

Menlo Security routes unknown or risky web sessions through its isolation layer so malware detonation happens without exposing corporate browsers to untrusted content. The workflow supports both safe viewing and post-execution analysis, with security teams able to inspect artifacts produced during the isolated session. Policy integration supports automated handling so detonation results can directly drive enforcement outcomes for subsequent users.

A key tradeoff is that isolation centers on web-delivered content, so non-web execution paths may require separate detonation coverage in parallel tooling. Menlo Security fits organizations that receive heavy user browsing risk and need repeatable outcomes for phishing, credential theft pages, and drive-by downloads while keeping internal systems insulated.

Pros
  • +Web-session isolation keeps interactive browsing away from internal endpoints
  • +Session outcomes can feed enforcement decisions automatically
  • +Analyst visibility into isolated execution results supports fast triage
  • +Deployment aligns with existing enterprise web traffic control paths
Cons
  • –Coverage is narrower for non-web malware delivery paths
  • –Tuning policies and user workflows requires governance discipline
Use scenarios
  • SOC analysts

    Triage suspected phishing links

    Faster containment decisions

  • Security engineering teams

    Automate block decisions by session

    Reduced manual review

Show 1 more scenario
  • IT security operations

    Limit browsing exposure to users

    Lower internal compromise risk

    Unknown web content runs in an isolated rendering environment rather than endpoint.

Best for: Fits when web-borne threats demand repeatable isolation for interactive browsing sessions.

#3

gVisor

API-first

Application kernel that isolates containers by intercepting system calls.

8.6/10
Overall
Features8.6/10
Ease of Use8.6/10
Value8.6/10
Standout feature

Sentry user-space kernel intercepts and mediates system calls to enforce isolation inside container runtimes.

gVisor runs Linux processes under a user-space kernel that intercepts system calls, which changes the isolation surface compared with hardware or full VM approaches. It is commonly deployed with container runtimes like containerd and Kubernetes so untrusted workloads can run with reduced kernel exposure.

Isolation is implemented through the Sentry component and syscall filtering, which limits direct kernel interactions while still supporting many Linux userland binaries. gVisor also provides an API surface for sandboxing configuration, including networking and filesystem mounts, so deployments can tune what the sandbox can access.

Pros
  • +User-space kernel model intercepts syscalls to reduce host kernel exposure
  • +Works with container runtimes and Kubernetes for workload isolation
  • +Sentry architecture supports per-workload filesystem and network mounting
  • +Widely used for untrusted container execution with clear separation boundaries
Cons
  • –Syscall compatibility gaps can break workloads compared with native execution
  • –Performance overhead can increase under syscall-heavy or I O heavy workloads
  • –Networking and filesystem behaviors may require tuning for edge cases
  • –Operational setup needs governance to standardize sandbox configuration
Use scenarios
  • Security platform teams

    Run third-party containers safely

    Lower host kernel exposure

  • Kubernetes security administrators

    Sandbox selected pods only

    Stronger isolation for pods

Show 1 more scenario
  • Container runtime operators

    Constrain risky process capabilities

    Reduced escape surface

    Limit what processes can do by filtering system calls and controlling sandbox configuration for networking and mounts.

Best for: Fits when containerized services must run untrusted or third-party workloads with reduced kernel exposure.

#4

Hatching Triage

API-first

Cloud-based malware sandbox with API-first design for automated analysis.

8.3/10
Overall
Features8.3/10
Ease of Use8.3/10
Value8.3/10
Standout feature

Case-oriented triage reporting that turns execution evidence into standardized analyst outputs for routing decisions.

Hatching Triage is designed around short analyst loops, where submitted artifacts run dynamic analysis and produce structured findings that can be compared across runs. The workflow focus is on turning sandbox results into repeatable case context, including indicators extracted from executions and deterministic artifacts like process trees and behavioral summaries. It is most useful when the goal is quick attribution of what happened and what to do next, not deep custom instrumentation. Integration breadth matters here because many teams rely on moving triage outcomes into ticketing and detection pipelines.

Pros
  • +Analyst-first triage outputs reduce time spent translating reports into evidence
  • +Automation support helps route detonation outcomes into downstream case workflows
  • +Consistent report structure supports repeatable review across analysts
  • +URL and file submission flows support common intake patterns
Cons
  • –Isolation internals customization is limited compared with research-grade sandboxes
  • –Deep tuning can take time when workflows require strict consistency across cases
  • –Less suitable for teams needing kernel-level experiment control
  • –Complex automation chains may require engineering effort to standardize
Use scenarios
  • Security operations analysts

    Rapid URL and file triage

    Faster block or escalation

  • Threat hunting teams

    Consistent behavioral confirmation

    More confident hunting leads

Show 2 more scenarios
  • Incident response coordinators

    Evidence packaging for containment

    Clearer containment justification

    Generate analyst-ready reports from detonations to support containment actions during incidents.

  • Security automation engineers

    Automated triage workflow routing

    Reduced manual case handling

    Integrate sandbox runs into existing pipelines using automation interfaces for consistent handoffs.

Best for: Fits when SOC workflows need fast evidence packs for deciding block or detonate next steps.

#5

Cloudflare Browser Isolation

enterprise

Remote browser execution that separates web activity from user devices.

8.2/10
Overall
Features8.2/10
Ease of Use8.3/10
Value8.1/10
Standout feature

Cloudflare-managed browser isolation sessions enforce safer rendering by gating risky site access through isolation at the edge.

Cloudflare Browser Isolation uses a cloud-hosted browser rendering and isolation flow to handle web content in a controlled environment before it reaches end users. It is distinct because it focuses on safe delivery of potentially risky websites through a policy-driven browser isolation experience rather than host-based process detonation.

Pros
  • +Edge-enforced isolation for web browsing paths without installing endpoint agents
  • +Policy controls can steer users toward isolated sessions based on URL risk
  • +Session handling keeps unsafe web execution separated from user browsers
  • +Fits into existing Cloudflare security controls for centralized administration
Cons
  • –Coverage is centered on web content, not general file or process malware detonation
  • –Isolation outcomes depend on correct URL and site policy tuning for false positives
  • –Browser-only workflow can miss non-browser attack chains targeting endpoints
  • –Operational troubleshooting requires understanding isolation session logs and flows
Use scenarios
  • Security operations teams

    Gate risky links at the edge

    Fewer user-facing threats

  • Web security administrators

    Apply policies by browsing context

    Consistent risk handling

Show 1 more scenario
  • Edge security engineers

    Standardize isolation enforcement

    Unified security enforcement

    Coordinate isolation with other platform security and traffic controls so enforcement stays consistent for users.

Best for: Fits when security teams need browser-based exploit containment for risky URLs without endpoint sandbox deployments.

#6

Firecracker

API-first

MicroVM technology for running workloads in lightweight virtual machines.

7.9/10
Overall
Features7.9/10
Ease of Use7.9/10
Value7.9/10
Standout feature

Firecracker microVM design prioritizes fast, low-overhead isolation to run untrusted workloads with strong containment boundaries.

Firecracker runs untrusted workloads inside lightweight microVMs built on hardware-assisted virtualization. It focuses on strong process and kernel boundary isolation with a purpose-built VM manager rather than app-level sandbox hooks.

Pros
  • +MicroVM isolation reduces blast radius versus shared-kernel execution
  • +Tight integration with a microVM lifecycle enables repeatable detonation runs
  • +Clear boundaries for syscall-level interception inside the VM context
  • +Deterministic VM startup helps scale automated malware detonation pipelines
Cons
  • –Requires engineering effort to integrate VM orchestration and storage plumbing
  • –No built-in malware analysis workflow or scoring engine is provided
  • –Network and artifact handling require custom setup for each environment
  • –Operational governance depends on external tooling for audit trails and RBAC
Use scenarios
  • Infrastructure automation teams

    Run untrusted workloads in microVMs

    Stronger containment boundaries

  • Cloud security engineers

    Provide kernel isolation for tasks

    Reduced host exposure

Show 1 more scenario
  • Sandbox pipeline builders

    Integrate isolation into orchestration

    Repeatable detonation runs

    Embed the VM interface into automated detonation runs that need repeatability and containment mechanics.

Best for: Fits when security teams need high-containment microVM execution for automated malware detonation and custom analysis pipelines.

#7

Trend Micro Deep Discovery Analyzer

enterprise

Virtual malware analysis appliance for suspicious files and targeted attacks.

7.6/10
Overall
Features7.6/10
Ease of Use7.6/10
Value7.6/10
Standout feature

Deep Discovery Analyzer correlates detonation outcomes to investigation context from monitored email and web flows.

Trend Micro Deep Discovery Analyzer is built for malware detonation workflows driven by email, web, and network traffic, with analysis results mapped to enterprise incident response. The product focuses on detonation and behavioral evidence that security teams can correlate to campaigns and indicators of compromise.

Pros
  • +Detonation workflow designed for email and web investigation cases
  • +Investigation outputs emphasize behavioral evidence for triage teams
  • +Centralized reporting for correlating findings across monitored sources
  • +Supports analysis at enterprise scale with managed capture inputs
Cons
  • –Sandbox throughput can bottleneck when detonation queues grow
  • –Operational effectiveness depends on tuning capture sources and rules
  • –Automation depth is less flexible than tools focused on custom pipelines
  • –Less suited for file-only detonation without surrounding telemetry
Use scenarios
  • Incident response teams

    Detonate suspicious email content

    Actionable behavior evidence

  • Threat hunting analysts

    Analyze web-delivered malware

    Correlated investigation signals

Show 1 more scenario
  • Security program administrators

    Scale detonation across sources

    Operational detonation at scale

    Manage and report detonation runs at scale across monitored inputs so results can map to campaigns.

Best for: Fits when security teams need detonation evidence tied to enterprise investigation context from email and network traffic.

#8

Browserling Browser Sandbox

SMB

Online browser environment for opening websites in an isolated remote session.

7.1/10
Overall
Features7.1/10
Ease of Use7.1/10
Value7.1/10
Standout feature

Shareable browser run sessions for URL and file testing, built around inspection of in-browser execution traces.

Browserling Browser Sandbox provides controlled browser execution for URL and file-based testing with shareable results rather than endpoint installation. The workflow centers on running a page or opening a submitted file inside a managed browser session to observe rendering, redirects, and client-side behavior.

Pros
  • +Browser session runs in the cloud, avoiding local detonation setup
  • +URL and file submission support common browser-driven analysis workflows
  • +Session sharing helps route findings to analysts and triage teams
  • +Console and network logs support fast triage of client-side behavior
Cons
  • –Coverage focuses on browser execution and does not replace host-level detonation
  • –Automation and API hooks are limited compared with detonation-first sandboxes
  • –Deep memory and system-call interception signals are not the primary output
  • –Advanced governance controls like RBAC and audit logs are not the core focus
Use scenarios
  • Browser-based security testers

    Test URLs in an isolated browser

    Repeatable browser runs

  • Malware analysis teams

    Detonate JS-dependent artifacts

    Signals captured during execution

Show 1 more scenario
  • QA security validation teams

    Run submitted files in sandbox

    Clear execution traces

    Open a submitted file inside the sandbox and collect console and network activity for analysis.

Best for: Fits when teams need repeatable browser isolation for URL and document-driven behavior.

#9

E2B

API-first

Cloud sandbox infrastructure for executing isolated code and AI-generated workloads.

6.8/10
Overall
Features6.8/10
Ease of Use6.8/10
Value6.8/10
Standout feature

Per-execution environment construction with microVM isolation for API-driven, short-lived untrusted code runs.

E2B (e2b.dev) is a sandboxing service built for running untrusted code via an API rather than a desktop workflow. Its microVM isolation model runs user code inside ephemeral environments with controlled input files and environment variables.

Pros
  • +API-first execution and lifecycle control for high automation throughput
  • +MicroVM-based isolation model for stronger process containment boundaries
  • +Deterministic file and environment injection per run for reproducible executions
  • +Log and output capture designed for programmatic analysis pipelines
Cons
  • –Kernel-level visibility and deep system-call interception controls are limited
  • –Sandbox escape detection and detonation-grade analysis workflows need custom instrumentation
  • –Complex governance like fine-grained RBAC and audit log exports are not the primary focus
  • –Best results require engineering effort to adapt code and dependencies to each run
Use scenarios
  • Developer security teams

    Run untrusted code via API

    Safe code execution

  • Automation engineers

    Execute many short-lived tasks

    High-throughput containment

Show 1 more scenario
  • Platform integrators

    Collect logs for repeatable runs

    Repeatable analysis evidence

    Integrate programmatic provisioning and log collection to make execution results repeatable across runs.

Best for: Fits when teams need programmatic, repeatable code execution containment for automated analysis jobs.

#10

Kata Containers

API-first

Container runtime that runs workloads inside lightweight virtual machines.

6.7/10
Overall
Features6.7/10
Ease of Use6.7/10
Value6.7/10
Standout feature

MicroVM isolation for each container workload using Kata’s runtime integration instead of sharing the host kernel namespace.

Kata Containers uses virtualization-based container isolation by running workloads inside lightweight virtual machines. It integrates with standard container tooling through a Kubernetes and container-runtime compatible workflow.

Pros
  • +MicroVM-based process isolation reduces host kernel exposure
  • +Kubernetes and container-runtime integration supports existing deployment flows
  • +Fine-grained VM lifecycle controls for workload boundaries
  • +Well-defined device and filesystem passthrough options for least privilege
Cons
  • –Higher operational complexity than user-mode sandboxing approaches
  • –Throughput overhead from VM startup and resource isolation
  • –Sandbox escape signal fidelity depends on guest workload telemetry
  • –Not a dedicated malware detonation workflow for evidence capture
Use scenarios
  • Platform and security teams

    Execute untrusted workloads with VMs

    Reduced exposure of host kernel

  • Kubernetes administrators

    Sandbox sensitive pods with VM runtime

    Stronger isolation for pods

Show 2 more scenarios
  • Container platform teams

    Use OCI-compatible VM-backed runtime

    Familiar container interfaces

    Provide a VM-backed execution option using familiar container interfaces when process containers are insufficient.

  • Operators of shared clusters

    Separate tenants using VM boundaries

    Smaller cross-tenant blast radius

    Add a VM boundary for workloads on shared nodes to shrink the cross-tenant blast radius.

Best for: Fits when Kubernetes teams need stronger container isolation boundaries for untrusted workloads without adding a detonation appliance.

Conclusion

After evaluating 10 sandboxing software, VMRay 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.

Our Top Pick
VMRay

Use the comparison table and detailed reviews above to validate the fit against your own requirements before committing to a tool.

Frequently Asked Questions About Sandboxing Software

How do Joe Sandbox and Cloudflare Browser Isolation differ in what gets isolated and where policy is enforced?
Menlo Security detours suspicious traffic into an isolation workflow that prevents direct access to internal systems and ties detonation outcomes to policy decisions. Cloudflare Browser Isolation performs cloud-hosted browser rendering at the edge and gates risky site access through isolation sessions before content reaches end users.
Which tools provide detonation evidence artifacts designed for SOC triage rather than just execution output?
VMRay produces high-fidelity dynamic analysis with evidence-grade behavioral recording that becomes analyst-readable artifacts. Hatching Triage focuses on case-oriented triage reporting that converts execution evidence into standardized outputs for routing decisions.
What breaks if automation needs API-driven provisioning instead of analyst-driven browser sessions?
Browserling Browser Sandbox centers on shareable browser run sessions for URL and file testing, which is not the same execution model as programmatic provisioning. E2B is built for API-driven sandboxing where each run provisions an ephemeral microVM, collects logs, and returns results for automation.
When should gVisor be chosen over microVM approaches like Firecracker for Linux untrusted workloads?
gVisor uses a user-space kernel with system call interception through Sentry, which changes isolation behavior compared with full microVM boundaries. Firecracker targets lightweight microVM execution with hardware-assisted virtualization for stronger containment mechanics and low-overhead repeated detonation pipelines.
How do VMRay and Trend Micro Deep Discovery Analyzer connect detonation results to investigation context?
VMRay outputs deep behavioral capture and evidence artifacts that support SOC triage and malware investigation work. Trend Micro Deep Discovery Analyzer maps detonation results to enterprise incident response context from monitored email, web, and network traffic.
What integration paths exist when security teams need to push indicators for detonation and route outcomes to downstream systems?
Hatching Triage supports automation hooks for ingesting indicators and exporting results to downstream security processes. VMRay emphasizes integration via automation interfaces and exportable report data for workflows that consume detonation outcomes.
Which sandboxing option is designed to isolate browser sessions that depend on JavaScript execution?
Browserling Browser Sandbox runs pages and submitted files in managed browser sessions so client-side behavior can be observed through in-browser execution traces. Menlo Security and Cloudflare Browser Isolation also isolate web access, but Browserling is framed around repeatable browser execution output for URL and document-driven signals.
When does Kata Containers provide better alignment than generic container isolation for untrusted workloads in Kubernetes?
Kata Containers runs each pod workload inside a lightweight virtual machine, giving stronger boundaries than approaches that share the host kernel namespace. gVisor also improves isolation for containers through system call interception, but Kata’s microVM-per-workload model is the differentiator for teams standardizing on Kubernetes runtimes.
What governance controls should teams expect when deploying sandboxing at scale across users and analysts?
Cloudflare Browser Isolation includes administrative controls that gate risky access based on browsing context. Menlo Security routes detonation outcomes into policy decisions that security teams use to block, allow, or route requests based on observed behavior.

Tools reviewed

Primary sources checked during evaluation.

Referenced in the comparison table and product reviews above.

How to Choose the Right Sandboxing Software

The real differences show up in how each tool runs samples safely and how that output gets controlled. We focus on Sandbox isolation level, Output fidelity, Automation and API surface, Audit and governance controls, and Throughput under workload so you can pick what fits your lab and ops flow.

Isolation boundary strength

Why it matters

Isolation boundary strength determines how reliably the tool contains risky execution paths and limits cross-sample effects. That matters when you run untrusted code repeatedly in the same environment.

How to check

Ask how the environment separates samples and users: for example, what prevents one run from changing artifacts used by the next. In a demo, submit two different sample types and confirm results never reuse the same workspace state without a clean reset.

Typical range

1–n distinct isolation modes depending on risk tier

Observable evidence detail

Why it matters

Observable evidence detail is what turns sandbox runs into decisions instead of screenshots. If the tool captures the right signals, you can validate findings and reduce false positives.

How to check

Run a sample during the demo and review what you can export: process tree, file/network artifacts, and timeline-like views. Confirm you can map key events to concrete outputs (inputs, dropped files, connections) rather than only summary notes.

Automation and API surface

Why it matters

Automation and API surface decides whether sandboxing fits into your pipeline or stays manual. Deep hooks also let you standardize provisioning, labeling, and retrieval of run results.

How to check

Ask for an end-to-end workflow: submit sample, set policy, run, retrieve results, and store them elsewhere. Check whether you can create tasks and fetch results via documented endpoints, then replay the workflow on a second sample without extra console work.

Typical range

At least submission + policy selection + results retrieval

Audit log and RBAC control

Why it matters

Audit log and RBAC control is how you keep sandbox activity traceable and prevent accidental policy changes. It also supports investigations when runs must be explainable later.

How to check

In the demo, ask who can create policies, trigger runs, and export evidence. Verify the audit log captures admin actions and sample-run lifecycle events, and confirm you can search by operator, policy, and time window.

Throughput under parallel runs

Why it matters

Throughput under parallel runs affects how quickly the tool can support real investigations. If queueing or slow cleanup dominates, your turnaround will break operational expectations.

How to check

Request a test run of multiple samples in parallel and watch queue time and completion stability. Compare results consistency across concurrent runs, and confirm the tool isolates workspace state between them.

Typical range

Sustained parallel execution without rising failure rate

Red flags

  • Isolation boundary is optional or inconsistently applied across samples
  • Evidence export is summarized and lacks event-level artifacts
  • RBAC or audit visibility is missing for policy and export actions

Fit by situation

  • If we need to run mixed-risk samples in shared labs, prioritize Isolation boundary strength.
  • If our analysts must reproduce decisions, prioritize Observable evidence detail.
  • If we must connect sandbox runs to existing workflows, prioritize Automation and API surface.
Commissioned Research

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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