GITNUXSOFTWARE ADVICE
AI In IndustryTop 10 Best Bot Management Software of 2026
Top 10 Bot Management Software picks with ranking criteria and tradeoffs for Cloudflare, Akamai, and AWS WAF Bot Control.
How we ranked these tools
Core product claims cross-referenced against official documentation, changelogs, and independent technical reviews.
Analyzed video reviews and hundreds of written evaluations to capture real-world user experiences with each tool.
AI persona simulations modeled how different user types would experience each tool across common use cases and workflows.
Final rankings reviewed and approved by our editorial team with authority to override AI-generated scores based on domain expertise.
Score: Features 40% · Ease 30% · Value 30%
Gitnux may earn a commission through links on this page — this does not influence rankings. Editorial policy
Editor’s top 3 picks
Three quick recommendations before you dive into the full comparison below — each one leads on a different dimension.
Cloudflare Bot Management
Managed challenges driven by bot score intelligence
Built for teams securing public web apps that need edge-scale bot mitigation and tuning.
Akami Bot Manager
Editor pickEdge bot classification with automated policy actions based on request behavior
Built for enterprises needing edge-based bot control for web and API traffic at scale.
AWS WAF Bot Control
Editor pickBot Control managed rule group for automated bot classification in AWS WAF
Built for aWS-first teams needing managed bot detection inside existing WAF rules.
Related reading
Comparison Table
The comparison table benchmarks Cloudflare Bot Management, Akamai Bot Manager, AWS WAF Bot Control, Google reCAPTCHA Enterprise, PerimeterX, and other bot control tools by integration depth, data model, and automation through API and provisioning workflows. It also maps admin and governance controls such as RBAC, configuration management, and audit log coverage so teams can evaluate how each system handles schema alignment, extensibility, and policy throughput.
Cloudflare Bot Management
enterpriseDetects and mitigates automated traffic with managed bot rules, supervised bot labeling, and challenge or block actions for HTTP and browser requests.
Managed challenges driven by bot score intelligence
Cloudflare Bot Management stands out for turning bot detection into an enforcement-ready control plane using Cloudflare edge signals. It combines bot score intelligence with managed challenges and rules so teams can distinguish likely automation from real users.
The product integrates with Cloudflare’s broader security stack, including WAF and DDoS controls, to coordinate bot defenses across applications. It also supports visibility via logs and analytics to track bot activity and tune mitigation outcomes.
- +Bot score signals enable targeted actions for both known and emerging bot behavior
- +Managed challenges reduce friction while still stopping automation attempts
- +Edge-level enforcement scales with traffic without adding origin overhead
- +Works cleanly with WAF and other Cloudflare security controls
- –Effective tuning can require iterative rule adjustments for each app context
- –High reliance on Cloudflare edge processing limits portability to other infrastructure
- –Granular outcomes depend on accurate bot classification and traffic telemetry
Security engineers
Enforce bot scores at the edge
Fewer credential-stuffing attempts
Fraud and risk teams
Reduce fraudulent signups and abuse
Lower abuse conversion rates
Show 2 more scenarios
Web operations teams
Tune mitigations using bot analytics
Reduced false positives
Review logs and analytics to adjust challenges and rules for better user outcomes.
Platform teams
Coordinate bot controls with WAF
Consistent policy enforcement
Align bot defenses with WAF and DDoS settings across multiple apps and hostnames.
Best for: Teams securing public web apps that need edge-scale bot mitigation and tuning
More related reading
Akami Bot Manager
enterpriseClassifies bots and enforces mitigation using Bot Manager with behavioral signals, fingerprinting, and policy-driven actions.
Edge bot classification with automated policy actions based on request behavior
Akami Bot Manager is designed for web and API bot detection using Akamai edge visibility and threat intelligence. It supports automated bot classification and policy actions to reduce fraud and scraping while preserving legitimate traffic.
The solution integrates with Akamai’s delivery stack so bot controls can run close to where requests enter the network. It also emphasizes operational feedback through reporting that helps refine rules for evolving bot behavior.
- +Edge-level bot detection provides low-latency classification
- +Policy-driven mitigations for both web and API request patterns
- +Strong integration with Akamai delivery and security tooling
- +Actionable reporting supports iterative rule tuning
- –Policy tuning can require security engineering expertise
- –Complex deployments may slow time to effective bot controls
- –Results can depend heavily on correct traffic and threat modeling
- –Limited transparency compared with single-purpose point solutions
Fraud operations teams
Stop credential stuffing and takeover attempts
Reduced fraud and account takeovers
Web security engineers
Control scraping without breaking customers
Lowered scraping with preserved access
Show 2 more scenarios
Digital commerce platform owners
Protect checkout and inventory endpoints
More reliable purchases and APIs
Manages bot traffic for web and API requests to safeguard high-value ecommerce workflows.
API governance teams
Limit abusive automation on APIs
Controlled API abuse
Applies automated bot classification and policy actions to API traffic entering Akamai.
Best for: Enterprises needing edge-based bot control for web and API traffic at scale
AWS WAF Bot Control
cloud-nativeUses managed bot detection inside AWS WAF to identify likely bots and apply rule actions on web requests.
Bot Control managed rule group for automated bot classification in AWS WAF
AWS WAF Bot Control provides enrichment signals that plug into AWS WAF managed rules, so bot-related fields can drive allow, block, or challenge actions without building custom classifiers. It emits bot category classifications like suspected bots and good bots, and it includes bot-detection indicators that can be consumed by WAF rule conditions and CloudWatch visibility.
Enforcement tuning is based on WAF logs and metrics, which makes it easier to validate how bot labels correlate with request rate, client behavior patterns, and mitigations over time. A practical tradeoff is that teams must design WAF rule logic around the available bot categories and thresholds, which can require iterative testing to avoid blocking legitimate automated clients.
- +Managed bot classification reduces custom rule engineering effort for common bot types
- +Integrates with AWS WAF rules for straightforward allow and block enforcement
- +Uses WAF logging and metrics for measurable bot traffic tuning and troubleshooting
- +Designed for AWS-native deployments like ALB and API Gateway protection
- –Best coverage assumes AWS-native traffic paths and AWS WAF rule placement
- –Tuning for edge-case bots often requires additional custom WAF logic
- –Bot decisions rely on WAF signals that may lag for rapidly changing adversaries
Security engineering teams
Block suspected bots at edge
Lower attack traffic volume
Platform and SRE teams
Tune enforcement using WAF logs
Fewer false positives
Show 2 more scenarios
DevOps teams
Challenge automated agents consistently
Stable application availability
They apply standard WAF Bot Control classifications to route automated traffic into challenges instead of blocks.
Threat detection analysts
Correlate bot signals with incidents
Faster incident triage
They use WAF metrics and logs to link bot categories with spike events and mitigation outcomes.
Best for: AWS-first teams needing managed bot detection inside existing WAF rules
More related reading
Google reCAPTCHA Enterprise
challenge-basedChallenges suspicious interactions with risk analysis, bot detection signals, and policy controls for sign-in and form submission endpoints.
Adaptive challenge orchestration driven by reCAPTCHA Enterprise risk assessment
Google reCAPTCHA Enterprise stands out by combining bot detection signals with friction controls that are tailored to each request. It provides risk scoring and bot behavior analysis that can integrate into existing login and checkout flows through site keys and backend verification. It also supports privacy and data controls through configuration options like event tokenization and consent handling for qualifying use cases.
- +Advanced risk scoring for bot likelihood across web and app interactions
- +Configurable challenge and friction levels to balance security and conversion
- +Granular verification signals that support custom allow and block logic
- –Tuning risk thresholds and actions requires careful integration work
- –Best results depend on consistent event instrumentation across key endpoints
- –Operational visibility relies heavily on proper setup of assessment and logging
Best for: Teams needing enterprise-grade bot risk signals with adjustable user friction
PerimeterX
enterpriseProvides bot detection and automated defense for web applications using behavioral analysis, fingerprinting, and adaptive mitigations.
Real-time bot risk scoring and automated mitigation via behavioral signals
PerimeterX stands out for its managed, behavior-first bot defense that focuses on real user and attacker interactions rather than signatures alone. It provides bot categorization, risk scoring, and automated mitigation controls that integrate with common web and CDN architectures.
The platform also supports continuous learning, so defenses adapt as traffic patterns and bot tactics shift over time. Teams use it to reduce account abuse, scraping, and automated fraud while maintaining legitimate traffic availability.
- +Behavioral bot detection with risk scoring across web and API traffic
- +Automated mitigation options reduce manual intervention during attacks
- +Strong coverage for scraping, credential abuse, and account takeover patterns
- +Integrates with CDN and web security stacks for faster deployment
- –Tuning policies for low false positives can take ongoing analyst effort
- –Automation can complicate troubleshooting when legitimate traffic is impacted
- –Advanced workflows need more configuration knowledge than basic gateways
Best for: Teams defending login, scraping, and API abuse with behavior-driven controls
Datadog AppSec Bot Detection
observabilityFlags likely bots in web traffic through security monitoring and applies remediation workflows with integrated AppSec signals.
AppSec Bot Detection provides bot likelihood scoring for security decisioning
Datadog AppSec Bot Detection adds bot visibility inside Datadog AppSec by using automated detection and scoring to separate likely bots from real users. It integrates with AppSec signals so security teams can tune controls based on bot behavior and context rather than blocking by IP alone. The solution supports operational workflows through Datadog dashboards and alerting so bot activity can be monitored alongside other application security telemetry.
- +Bot detection tied to AppSec signals and application context
- +Actionable alerting and dashboards through Datadog monitoring
- +Behavior-based scoring reduces reliance on static IP blocklists
- –Tuning detection thresholds can require careful testing per application
- –Operational setup depends on broader Datadog AppSec instrumentation
- –Response automation may be limited compared with full bot management suites
Best for: Teams using Datadog AppSec needing bot visibility and alerting
More related reading
Fastly Bot Detection
edgeDetects and mitigates bots at the edge using traffic classification, behavioral checks, and configurable blocking or challenge logic.
Fastly edge enforcement with bot detection integrated into CDN request handling
Fastly Bot Detection stands out as a CDN-native bot mitigation capability built into Fastly’s edge delivery pipeline. It helps identify automated traffic using request and behavioral signals, then enables enforcement through Fastly configurations. The tool fits teams already using Fastly for low-latency delivery and centralized traffic control across web properties.
- +Edge-based bot detection reduces latency impact on filtering decisions
- +Integrates cleanly with Fastly traffic controls for consistent enforcement
- +Leverages request context signals for practical bot identification
- +Centralizes bot management alongside performance and routing policies
- –Most effective results depend on solid Fastly configuration and traffic routing
- –Limited standalone usability for teams not already using Fastly
- –Fine-tuning detection logic can require deeper operational expertise
- –Action and reporting visibility can feel constrained versus dedicated platforms
Best for: Teams using Fastly that need edge bot mitigation without extra tooling
Imperva Bot Management
enterpriseDetects automated abuse and enforces policies through Imperva bot management features inside its web application security stack.
Bot traffic classification with configurable mitigation actions
Imperva Bot Management stands out with bot detection and mitigation designed to protect web applications against automated abuse. It supports bot categorization, traffic analysis, and rule-based actions such as blocking or challenging suspicious requests.
The product emphasizes operational visibility with event data and analytics to support tuning bot policies over time. It fits environments that already use Imperva security controls for web and application protection.
- +Strong bot classification and policy controls for automated traffic
- +Mitigation actions include block and challenge behavior on risky requests
- +Actionable visibility through bot events and traffic analytics for tuning
- –Policy tuning can require iterative refinement to reduce false positives
- –Integration and rule management add complexity for teams without existing Imperva deployments
- –Depth of analytics depends on upstream data sources and telemetry setup
Best for: Enterprises needing bot detection and mitigation for public web applications
More related reading
Sophos Web Protection
security-suiteUses threat and web filtering controls to reduce automation-driven abuse and suspicious traffic patterns targeting web endpoints.
Web policy enforcement with detailed web activity reporting
Sophos Web Protection distinguishes itself with security-led web filtering combined with defenses aimed at automated and abusive traffic. It supports URL categorization, policy-based traffic control, and threat-focused inspection to reduce bot-like browsing behavior.
Teams also gain reporting on web activity and policy hits, which helps tune access rules for non-human access patterns. Bot management is handled indirectly through web protection controls rather than through dedicated bot-specific detection and action workflows.
- +Granular web content policies that limit automated access paths
- +Comprehensive web activity reporting for policy tuning
- +Security inspection helps suppress malicious automated browsing
- –Bot management relies on web filtering, not bot-specific logic
- –Limited visibility into bot identity, fingerprinting, and score-based decisions
- –Less suited for advanced bot mitigation workflows
Best for: Organizations reducing automated web abuse via policy-controlled web browsing
Radware Bot Manager
DDoS and appManages bots with behavior-based detection, signature and anomaly analysis, and automated mitigation for web and API attacks.
Automated bot mitigation policies that enforce actions based on detected bot behavior
Radware Bot Manager is built for enterprise-grade bot traffic control using detection and automated mitigation workflows. It targets common bot behaviors like scraping, credential abuse, and fraud by combining bot fingerprinting with rule and policy enforcement. The solution integrates with security delivery and application edges so mitigation can occur close to where bot traffic enters.
- +Strong bot detection using behavioral analysis and fingerprinting techniques
- +Automated mitigation actions help reduce fraud and scraping impact quickly
- +Integrates with edge and application security architectures for near-source blocking
- +Policy-driven controls support different bot categories and response behaviors
- –Configuration and tuning can require security team ownership and traffic baselining
- –Less suitable for small teams needing quick, lightweight deployment
- –Deep customization can slow time-to-action when requirements change frequently
Best for: Enterprises needing edge-based bot detection and automated mitigation
Conclusion
After evaluating 10 ai in industry, Cloudflare Bot Management 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 Bot Management Software
This buyer's guide covers Cloudflare Bot Management, Akamai Bot Manager, AWS WAF Bot Control, Google reCAPTCHA Enterprise, PerimeterX, Datadog AppSec Bot Detection, Fastly Bot Detection, Imperva Bot Management, Sophos Web Protection, and Radware Bot Manager. It focuses on integration depth, bot data model and classification semantics, automation and API surface, and admin governance controls such as policy management and auditability.
The guide also compares these picks against Cloudflare, Akamai, and AWS WAF Bot Control for edge-scale bot enforcement and WAF-native decisioning. Use the sections on key features, decision steps, and common implementation pitfalls to map tool capabilities to bot mitigation outcomes across web and API traffic.
Bot control tooling that classifies automation and enforces actions at the edge or in app security
Bot management software turns bot signals into enforcement-ready controls for HTTP and browser requests, or it uses bot risk scores and policy categories to drive allow, block, or challenge decisions. It typically resolves common problems like scraping, credential abuse, account takeover attempts, and abusive automated browsing by combining detection, classification, and configurable mitigation actions.
Tools like Cloudflare Bot Management use edge bot score intelligence and managed challenges to enforce behavior at request ingress. AWS WAF Bot Control focuses on managed bot categories inside AWS WAF so existing WAF rules can take allow, block, or challenge actions based on bot-related fields.
Evaluation criteria mapped to integration depth, data model, automation surface, and governance controls
Bot management outcomes depend on how bot classification outputs map into enforcement and how quickly teams can tune those mappings per application context. Integration depth matters because edge enforcement and WAF-native labels change where decisions run and what telemetry becomes available for validation.
Automation and API surface matters because rule provisioning, policy changes, and bot classification inputs must connect to existing operational workflows. Admin and governance controls matter because multiple services and teams often share the same bot controls, and changes must remain attributable and testable.
Bot score intelligence that drives managed challenge or block actions
Cloudflare Bot Management uses managed challenges driven by bot score intelligence to target likely automation with enforcement actions rather than blanket blocking. PerimeterX also emphasizes real-time bot risk scoring linked to automated mitigation via behavioral signals, which reduces manual intervention during bot surges.
Edge-native classification integrated into CDN or edge delivery pipelines
Akami Bot Manager performs edge bot classification with automated policy actions based on request behavior so classification and mitigation happen close to ingress. Fastly Bot Detection integrates into Fastly traffic controls for consistent enforcement and centralizes bot handling alongside performance and routing policies.
WAF-native bot labels that plug into managed rule groups
AWS WAF Bot Control provides a managed bot classification rule group so bot-related fields can drive allow, block, or challenge actions inside AWS WAF. This approach is most frictionless for AWS-first deployments where WAF log metrics validate label-to-mitigation correlation over time.
Risk scoring and adaptive friction for high-value user flows
Google reCAPTCHA Enterprise combines bot detection signals with risk scoring and challenge or friction controls tailored to sign-in and form submission endpoints. This model supports granular verification signals that can integrate into custom allow and block logic for workflows where user friction must be tightly controlled.
Operational feedback loops via logs, analytics, and alerting
Cloudflare Bot Management provides logs and analytics so tuning can be driven by observed bot patterns and mitigation outcomes. Datadog AppSec Bot Detection adds bot likelihood scoring into Datadog AppSec dashboards and alerting so bot activity is monitored alongside other application security telemetry.
Policy configuration depth with category-based governance
Imperva Bot Management supports bot categorization and rule-based actions such as blocking or challenging suspicious requests with event data and traffic analytics for tuning. Radware Bot Manager uses policy-driven controls that enforce different bot categories with automated mitigation policies based on detected bot behavior.
A control-plane decision framework for selecting the right bot management tool
The selection process should start with where enforcement must occur and which enforcement engine already runs in production, such as a CDN edge pipeline or a WAF. Next, the bot data model should be validated by checking whether the tool produces actionable outputs such as bot scores, bot categories, or risk assessments that map directly to allow, block, or challenge actions.
Finally, admin and governance controls should be assessed by reviewing how policies are configured, tuned, and validated with logs and telemetry. This framework reduces the chance of creating a detection-only deployment that cannot reliably control automated traffic.
Match enforcement locality to existing ingress control points
If Cloudflare is already the edge, Cloudflare Bot Management runs bot detection and managed challenges at the edge using bot score intelligence. If AWS WAF is the enforcement plane, AWS WAF Bot Control provides managed bot classification that can drive WAF rule actions on web requests.
Verify the bot outputs align with the action model needed by the app
For targeted disruption with lower false-positive risk, Cloudflare Bot Management and PerimeterX both emphasize risk scoring and managed mitigation actions. For user-flow friction control, Google reCAPTCHA Enterprise maps risk assessment into adaptive challenge orchestration that can fit sign-in and form submission endpoints.
Evaluate integration depth against app and API architectures
Akami Bot Manager is built for edge-based bot control for both web and API traffic, which supports policy-driven actions driven by request behavior. Radware Bot Manager and Imperva Bot Management also focus on web and API abuse patterns, but they require security ownership for configuration and traffic baselining.
Plan the tuning workflow using telemetry the tool exposes
Cloudflare Bot Management and Imperva Bot Management provide logs and analytics that support rule tuning based on observed bot patterns and mitigation outcomes. Datadog AppSec Bot Detection ties bot likelihood scoring into dashboards and alerting so threshold tuning can be validated through operational monitoring.
Assess admin governance needs for multiple teams and services
Tools with policy-driven controls like Imperva Bot Management and Radware Bot Manager require clear ownership of mitigation policies to avoid broad rule changes that raise false positives. AWS WAF Bot Control also requires teams to design WAF rule logic around the available bot categories and thresholds, which benefits from disciplined governance of rule revisions and WAF log review.
Which teams should prioritize bot management versus web filtering or single-purpose challenges
Bot management software fits teams that must classify automation and enforce actions across web and sometimes browser traffic with controlled outcomes. It is less suited for organizations that only need URL categorization and web policy hits because Sophos Web Protection handles bot-like behavior indirectly through web filtering rather than dedicated bot-specific decision workflows. The right pick depends on whether enforcement should happen at the edge, inside a WAF, or inside app security telemetry workflows.
Public web app teams running on Cloudflare edge
Cloudflare Bot Management is built for edge-scale bot mitigation using bot score signals and managed challenges, which fits teams that need enforcement at request ingress and iterative tuning from logs and analytics.
Enterprise teams standardizing on Akamai delivery and security
Akami Bot Manager targets web and API traffic at the edge using automated bot classification and policy actions based on request behavior, which aligns with environments that already operate within Akamai’s delivery stack.
AWS-first teams using AWS WAF managed rules for security decisions
AWS WAF Bot Control integrates managed bot classification into AWS WAF so bot categories can drive allow, block, or challenge actions, and WAF logging and metrics support measurable tuning over time.
Security operations teams with Datadog AppSec instrumentation
Datadog AppSec Bot Detection supports bot visibility and operational workflows in Datadog by connecting bot likelihood scoring to AppSec signals and dashboards and alerting.
CDN teams on Fastly needing centralized edge bot enforcement
Fastly Bot Detection centralizes bot mitigation alongside Fastly routing and traffic controls, which reduces latency impact because detection and enforcement run in the Fastly edge delivery pipeline.
Implementation pitfalls that cause bot controls to misfire or become unmanageable
Most failures come from mismatched enforcement locality, a weak mapping between bot outputs and action logic, or insufficient telemetry to tune outcomes. Several tools also require ongoing policy tuning effort, and those operational costs must be planned before rollout. A third recurring issue is choosing a web filtering tool when dedicated bot identity and score-driven enforcement is required.
Tuning policies without a repeatable telemetry feedback loop
Cloudflare Bot Management and Imperva Bot Management both rely on logs and analytics for tuning, so tuning without actionable telemetry leads to noisy enforcement. AWS WAF Bot Control also depends on WAF logs and metrics to validate bot label correlation with mitigations over time.
Treating WAF-managed bot categories as universal classifiers
AWS WAF Bot Control requires teams to design WAF rule logic around available bot categories and thresholds, and this can take iterative testing to avoid blocking legitimate automated clients. This pitfall also shows up in edge deployments when bots change and WAF signals lag for rapidly changing adversaries.
Choosing web filtering for bot management requirements
Sophos Web Protection emphasizes URL categorization and web policy enforcement with reporting on policy hits, which limits visibility into bot identity, fingerprinting, and score-based decisions. For score-driven enforcement, tools like Cloudflare Bot Management, PerimeterX, and Google reCAPTCHA Enterprise provide risk scoring that directly drives challenges or friction.
Underestimating the operational ownership needed for policy tuning
Akami Bot Manager and Imperva Bot Management note that policy tuning can require security engineering expertise or iterative refinement to reduce false positives. Radware Bot Manager also calls out configuration and tuning that require traffic baselining, which can slow time to effective mitigation without dedicated ownership.
Overrelying on static network rules instead of behavior-driven classification
Datadog AppSec Bot Detection ties bot detection to AppSec signals and application context rather than IP-only blocking, which prevents simplistic network blocks from breaking legitimate traffic. PerimeterX emphasizes behavior-first bot defense, while Sophos Web Protection focuses on web filtering, so choosing IP-first or content-only controls can miss scraping and credential abuse patterns.
How We Selected and Ranked These Tools
We evaluated Cloudflare Bot Management, Akami Bot Manager, AWS WAF Bot Control, Google reCAPTCHA Enterprise, PerimeterX, Datadog AppSec Bot Detection, Fastly Bot Detection, Imperva Bot Management, Sophos Web Protection, and Radware Bot Manager using three criteria tied to real operational needs: features, ease of use, and value, with features carrying the most weight at 40%. Ease of use and value each accounted for the remaining share, and the overall rating is a weighted average of those three scored categories using the provided overall and category ratings.
Cloudflare Bot Management ranks highest because managed challenges are driven by bot score intelligence and it integrates cleanly with Cloudflare WAF and other security controls, which lifts features and also supports practical tuning through edge-level logs and analytics. This ranking reflects criteria-based editorial scoring rather than hands-on lab benchmarking because no private benchmark experiments or direct runtime testing results were provided.
Frequently Asked Questions About Bot Management Software
How do Cloudflare Bot Management and AWS WAF Bot Control differ in enforcement mechanics?
Which tools provide API or integration paths for wiring bot signals into existing security automation?
What role do SSO and RBAC controls typically play in bot operations, and which products support admin governance?
How is data migration handled when switching from one bot mitigation setup to another vendor?
Which platforms support extensibility for custom workflows beyond managed classifications?
How do teams validate that bot categories or scores correlate with real abuse and do not block legitimate automation?
Which tool is more suitable for bot control at CDN edge, and what tradeoff comes with that?
How do solutions like reCAPTCHA Enterprise and bot-management platforms differ for login and checkout protection workflows?
Why do some tools integrate indirectly with bot mitigation through web policy controls instead of dedicated bot workflows?
What performance and throughput considerations matter when enabling bot detection and challenges at scale?
Tools reviewed
Primary sources checked during evaluation.
akamai.comaws.amazon.comfastly.comReferenced in the comparison table and product reviews above.
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