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Education LearningTop 10 Best Reading Aloud Software of 2026
Top 10 Reading Aloud Software ranked by accuracy, voices, and pricing for students and teams, with options like NaturalReader and Speechify.
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%
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Editor’s top 3 picks
Three quick recommendations before you dive into the full comparison below — each one leads on a different dimension.
NaturalReader
Text-to-speech reading controls for producing audio from imported documents.
Built for fits when teams need dependable reading-aloud generation without deep integration governance..
Speechify
Editor pickText-to-speech generation from PDF and web text inputs with managed playback settings.
Built for fits when teams need speech output integrated into content pipelines with controlled configuration..
Capti Voice
Editor pickRBAC and audit log for voice configuration governance across integrations.
Built for fits when teams need controlled reading aloud workflows with API automation and RBAC governance..
Related reading
Comparison Table
This comparison table maps reading aloud tools across integration depth, including how each platform connects to LMS, web apps, and document workflows. It also compares the data model and schema for text and audio, the automation and API surface for provisioning and extensibility, and admin controls like RBAC, configuration options, and audit log coverage. Readers can use these dimensions to see tradeoffs in governance, throughput, and how well each tool fits into existing systems.
NaturalReader
TTS desktop/webDesktop and web text-to-speech apps convert selected text to spoken audio with controls for voices, playback, and saved output suitable for classroom reading aloud workflows.
Text-to-speech reading controls for producing audio from imported documents.
NaturalReader’s core capability is generating speech audio from pasted text, imported documents, and selected web content, which supports daily reading-aloud routines. Voice controls and playback settings help standardize output for users who need consistent narration across sessions. Integration depth is mainly centered on how content enters the system, since automation and orchestration depend on available import and sharing pathways rather than a rich external data model.
A key tradeoff is limited visibility into automation and API surface for end-to-end provisioning, like RBAC and audit log reporting. Teams can still use it effectively for scheduled individual narration or lightweight workflows, but deeper governance needs require external process controls. A common usage situation is onboarding accessibility support for documents and training scripts where humans approve inputs before narration runs.
- +Converts pasted text, documents, and web content into audio narration
- +Voice and playback controls support consistent reading output
- +Repeatable configuration helps standardize narrations across users
- –API and automation surface is not documented for enterprise orchestration
- –Governance controls like RBAC and audit logs are not prominent
Accessibility coordinators
Convert student materials into audio quickly
Faster access to materials
L&D teams
Narrate training scripts from documents
Uniform training narration
Show 2 more scenarios
Compliance reviewers
Review written policies by listening
Quicker content review
Convert policy text into audio to support review cycles without changing source formatting.
Customer support leads
Create audio versions of knowledge base articles
More accessible support content
Turn article text into narration to help agents provide audio explanations to customers.
Best for: Fits when teams need dependable reading-aloud generation without deep integration governance.
More related reading
Speechify
web TTSBrowser and mobile reading-aloud tooling converts pasted text and uploaded documents into audio with selectable voices and exportable listening output.
Text-to-speech generation from PDF and web text inputs with managed playback settings.
Speechify fits when reading aloud is needed across PDFs, pasted text, and web content for staff who switch between source material and audio consumption. The data model is centered on text inputs that map to generated audio playback, which supports configuration for reading behavior rather than document restructuring. The administration and governance story is strongest when teams require consistent settings across users and auditability around content usage, voice selection, and account actions. Integration depth becomes the deciding factor for organizations that need API-driven provisioning and automation into existing content and learning systems.
A key tradeoff is that Speechify’s automation and API surface are the main path for enterprise workflows, while deeper document transformation and metadata schema governance depend on upstream systems. Speechify works best when audio output needs to be consistently regenerated from stable text inputs, such as training decks, SOP documents, or course readings. Teams that expect complex workflow state management inside the reader may find the extensibility layer limited compared to document-centric platforms. The highest value comes from connecting Speechify to internal ingestion, RBAC, and content repositories so throughput stays predictable.
- +Handles PDFs and web text with a consistent reading aloud workflow
- +Playback controls support repeat listening for the same content source
- +API and automation focus supports integration into existing systems
- +Configuration patterns help keep reading settings consistent across users
- –Enterprise orchestration depends heavily on API-first workflow design
- –Deeper document schema governance stays outside the reader experience
Accessibility program leads
Convert SOP PDFs into consistent audio
Fewer manual conversions
Learning and training teams
Generate audio for course reading packs
Higher training engagement
Show 2 more scenarios
Content operations teams
Pipe article text into TTS at scale
Predictable throughput
Uses API-driven automation to regenerate audio from the same text sources.
Enterprise IT governance teams
Centralize access with RBAC
Controlled user permissions
Applies user governance around reading configuration and account actions.
Best for: Fits when teams need speech output integrated into content pipelines with controlled configuration.
Capti Voice
education accessibilityReading aloud features in Capti convert text to speech inside classroom-facing reading tools with configurable audio playback for supported content types.
RBAC and audit log for voice configuration governance across integrations.
Capti Voice is a reading aloud solution built for integration depth, with an API surface that supports provisioning and configuration of speech jobs. The data model centers on content inputs plus voice and playback configuration, which helps align output across channels. Automation and extensibility are emphasized through a controllable workflow layer instead of per-user settings alone.
A tradeoff is that deeper governance and configuration often require aligning system schema and voice configuration conventions between the calling app and Capti Voice. Capti Voice fits organizations that want centralized control of voice configuration and playback behavior across multiple content types and user groups.
- +API-first provisioning for speech jobs and configuration
- +RBAC supports controlled access to voice settings
- +Audit log visibility for administrative changes
- +Extensibility supports workflow automation around playback
- –Schema alignment is required for consistent configuration
- –Governance setup adds effort versus self-serve playback
Accessibility platform teams
Centralize voice settings for multiple apps
Consistent speech across channels
Enterprise admin teams
Enforce RBAC over reading aloud
Controlled access and traceability
Show 2 more scenarios
Education operations teams
Automate speech output for lesson content
Reduced manual setup
Use the API to orchestrate reading aloud playback from content ingestion pipelines.
Product engineering teams
Build custom playback workflows
Fewer integration handoffs
Use extensibility to connect reading aloud to existing automation and content schemas.
Best for: Fits when teams need controlled reading aloud workflows with API automation and RBAC governance.
Ghotit
learning supportText-to-speech reading aloud capability pairs with grammar support to speak readable text for students while retaining alignment between written input and audio output.
Grammar-aware spoken reading that adjusts pronunciation based on text corrections.
Ghotit provides Reading Aloud output paired with grammar-aware text processing for learners who need spoken feedback on written English. The core capability centers on text-to-speech with pronunciation and correction behavior driven by its underlying linguistic rules.
Integration depends on how Ghotit is embedded into the reader workflow, since the automation and API surface are limited compared with tools that expose programmable events. Administration and governance are therefore more about configuring usage for end users than provisioning complex multi-tenant schemas.
- +Reading Aloud output tied to its grammar processing rules
- +Pronunciation behavior targets common learner error patterns
- +Works well inside standard classroom or study text workflows
- –Automation and API surface are limited for event-driven integrations
- –Data model and schema are not exposed for custom governance
- –RBAC, audit log, and provisioning controls are not geared for enterprise tenants
Best for: Fits when learning workflows need speech plus grammar handling without heavy automation requirements.
Read&Write
accessibility suiteReading aloud features in Read&Write support speech playback for student documents and on-screen text with configurable accessibility settings.
Text highlighting follows speech output for paragraph and word-level reading aloud alignment.
Read&Write delivers reading aloud via browser and desktop access to synchronized text highlighting. It includes document and text-to-speech controls tied to reading settings like speed and voice selection.
Integration depth centers on classroom and workplace workflows where assistive tools attach to learning content and writing tasks, rather than standalone playback. Automation and external integration depend on the availability of admin configuration, managed deployment options, and any exposed API or integration points for adding users and content to a governed environment.
- +Synchronized highlighting with reading aloud improves text alignment during playback
- +Voice and reading controls support repeatable accessibility settings across materials
- +Administration options support managed deployment for education and enterprise use
- –Automation and API surface for custom integrations are not clearly documented in review scope
- –Governance controls for fine-grained RBAC and policy enforcement need verification
- –Extensibility for custom content pipelines may require vendor-supported pathways
Best for: Fits when schools or enterprises need governed reading aloud integrated into learning and writing workflows.
Microsoft Azure AI Speech
API-first TTSProgrammable text-to-speech endpoints provide a reading aloud automation surface through speech synthesis APIs with model selection and audio output formats.
SSML-driven synthesis configuration with pronunciation lexicon and neural voice options.
Microsoft Azure AI Speech provides Reading Aloud via neural text-to-speech services that plug into Azure apps through documented Speech SDKs and REST APIs. It supports SSML-driven configuration for pronunciation, pacing, and voice selection, which aligns well with controlled reading experiences.
The service data model centers on synthesis requests, voice configuration, and audio outputs that can be orchestrated from workflows or backend services. Governance features like Azure RBAC and audit logs support administration across subscriptions and resource scopes.
- +SSML controls pronunciation, cadence, and style per synthesis request
- +Speech SDK and REST APIs support automation from apps and pipelines
- +Azure RBAC and resource scoping fit enterprise access control needs
- +Audit logs and monitoring integrate with Azure governance workflows
- +Extensibility via custom neural voices and pronunciation lexicon
- –Voice and output settings require careful schema validation
- –Orchestrating low-latency playback needs tuning of deployment settings
- –High-volume synthesis depends on quota and throughput management
- –SSML authoring adds complexity for non-technical teams
Best for: Fits when enterprise apps need governed, API-driven reading aloud with SSML-level control.
Google Cloud Text-to-Speech
API-first TTSText-to-speech APIs synthesize spoken audio from input text with voice selection and configurable output audio formats for automated reading aloud pipelines.
IAM RBAC plus Cloud Logging audit visibility for every Text-to-Speech synthesis request.
Google Cloud Text-to-Speech turns structured text inputs into audio using a documented API and configurable synthesis parameters. It integrates deeply with Google Cloud services like IAM for RBAC, Cloud Logging for audit visibility, and Vertex AI pipelines for automation patterns.
The data model centers on requests that specify voice selection and output format, with schema-driven fields that make batch and orchestration workflows predictable. Compared with desktop readers, integration depth and governance controls come from Cloud-native configuration, not from a separate authoring UI.
- +Request and audio parameters exposed through a consistent REST and gRPC API
- +IAM RBAC supports per-project access control for synthesis permissions
- +Cloud Logging captures request metadata for operational audit trails
- +Batch and automation via Cloud Run jobs and workflow orchestration patterns
- –Voice and customization options depend on selected voice models and parameters
- –Audio quality tuning requires repeated configuration and throughput testing
- –Governance requires Cloud project setup and IAM policy management overhead
- –Client-side tokenization and text splitting remains an application responsibility
Best for: Fits when teams need governed, automated text-to-audio generation through API-first workflows.
Amazon Polly
API-first TTSManaged speech synthesis provides text-to-speech APIs and audio generation controls to build reading aloud automation with throughput tuning for jobs.
SSML parsing with neural voice rendering for controlled prosody and pronunciation in automated jobs.
Amazon Polly generates speech from text through an AWS API, with neural and standard voices for varied languages. It fits production reading aloud by pairing synthesis controls like SSML support and output format selection with high-throughput delivery.
Integration depth is driven by AWS Identity and Access Management, region-based endpoints, and hooks into broader AWS services. Automation and extensibility come from programmable API calls and event-driven workflows using Amazon services.
- +SSML input supports pauses, emphasis, and pronunciation hints
- +AWS API enables programmatic speech synthesis at scale
- +IAM RBAC limits access to Polly actions by role and policy
- +Output formats include MP3 and OGG for downstream playback pipelines
- –SSML correctness and voice selection require careful configuration
- –Audio length and character limits can constrain long-form synthesis
- –Governance visibility depends on AWS CloudTrail and linked logging setup
- –Custom pronunciation management adds operational overhead for teams
Best for: Fits when teams need governed, API-driven text-to-speech automation in AWS workflows.
ElevenLabs
API-first TTSText-to-speech APIs generate audio from input text with model and voice controls that support programmatic reading aloud creation.
Text to speech API with configurable voice and generation parameters for repeatable audio output.
ElevenLabs generates reading-aloud audio from input text using configurable voice models and pronunciation controls. Integration is centered on an API that supports creating speech outputs for multiple voice settings and managing voice assets.
The data model treats voices and audio generation requests as separate entities that can be provisioned and reused across workflows. Automation coverage focuses on request based generation and programmatic parameterization rather than role driven content approval.
- +API supports text to speech generation with per request voice configuration
- +Voice asset management enables reuse of custom or selected voices
- +Fine grained generation parameters allow consistent tone and pacing
- +Output can be produced on demand for workflow throughput
- –Governance controls for RBAC and approvals are not clearly documented
- –Audit log coverage for voice asset changes is not specific
- –Sandboxing and environment separation controls are not explicit
- –Admin configuration depth for large catalogs is limited in surfaced docs
Best for: Fits when teams need API based reading aloud at controlled quality across automated workflows.
IBM Watson Text to Speech
API-first TTSText-to-speech service APIs synthesize speech audio from text inputs with configurable voice and output settings for automated reading aloud flows.
SSML support lets clients control speech behavior using structured tags in API requests.
IBM Watson Text to Speech fits teams that need reading aloud output wired into existing applications via a documented API and automation hooks. It supports configurable voice selection and SSML, which lets teams control pronunciation, emphasis, and timing through a structured input schema.
Audio generation is exposed through API calls that integrate into pipelines with job orchestration and throughput planning. Admin and governance depend on IBM Cloud account controls, with RBAC and audit logging available for access management and operational traceability.
- +SSML input enables pronunciation and prosody control through a structured schema
- +API-first TTS generation supports automation in app and media pipelines
- +RBAC and audit logging support governance over provisioning and access
- +IBM Cloud integrations help connect TTS to broader workflow systems
- –SSML complexity can slow iteration for teams with simple narration needs
- –Voice availability and configuration vary by region and voice catalog
- –Large batch orchestration requires careful rate and throughput planning
- –Asset management needs external storage and lifecycle handling
Best for: Fits when enterprises need API-driven TTS with governance and repeatable automation.
How to Choose the Right Reading Aloud Software
This buyer's guide covers NaturalReader, Speechify, Capti Voice, Ghotit, Read&Write, Microsoft Azure AI Speech, Google Cloud Text-to-Speech, Amazon Polly, ElevenLabs, and IBM Watson Text to Speech. It focuses on integration depth, data model and schema fit, automation and API surface, and admin and governance controls.
The guide maps concrete capabilities like SSML configuration in Microsoft Azure AI Speech and IBM Watson Text to Speech, IAM RBAC plus Cloud Logging in Google Cloud Text-to-Speech, and RBAC plus audit log visibility in Capti Voice. It also flags where tools restrict governance or automation, like NaturalReader’s limited documented API and Ghotit’s limited programmable event surface.
Reading aloud software that turns text into governed speech, aligned to content workflows
Reading aloud software converts documents, pasted text, and web content into audio narration with controls for voice selection, playback behavior, and repeatable reading settings. Many deployments also synchronize audio with highlighted text, like Read&Write’s paragraph and word-level alignment.
Teams use these tools to support accessibility workflows in schools and enterprises, and to automate text-to-audio generation inside content and application pipelines. Capti Voice represents a governance-first reading-aloud workflow with RBAC and audit log visibility tied to voice configuration.
Evaluation criteria for integration, automation control, and governance traceability
Reading aloud projects fail most often when teams pick tools without the right data model, schema expectations, or admin controls to manage voice configuration at scale. Capti Voice and Google Cloud Text-to-Speech add strong administrative patterns through RBAC plus audit or logging.
Other projects stall when the API surface is not documented for orchestration, as with NaturalReader’s limited enterprise automation surface and Ghotit’s limited event-driven integration capability. The criteria below translate those gaps into concrete checks for integration depth, automation and API surface, and governance readiness.
RBAC and audit log visibility for voice configuration
Capti Voice provides RBAC and audit log visibility for administrative changes to voice settings across integrations. Google Cloud Text-to-Speech uses IAM RBAC and Cloud Logging that captures request metadata for every synthesis request.
SSML and structured synthesis controls
Microsoft Azure AI Speech and IBM Watson Text to Speech expose SSML-driven configuration so teams can control pronunciation, cadence, emphasis, and timing at the synthesis request level. Amazon Polly also accepts SSML and uses neural voice rendering with controlled prosody and pronunciation.
Document ingestion coverage and repeatable playback settings
Speechify converts PDFs and web text into audio with managed playback settings and repeatable listening workflows for the same content source. NaturalReader focuses on dependable conversion from pasted text, documents, and web content with practical voice and playback controls for repeat usage.
Synchronized highlighting tied to reading aloud output
Read&Write delivers synchronized text highlighting that follows speech output at the paragraph and word level. This alignment reduces mismatches between what learners hear and what appears on screen during reading aloud.
Grammar-aware pronunciation behavior for learner correction
Ghotit pairs reading aloud output with grammar processing so spoken pronunciation changes based on text corrections. This design targets common learner error patterns without requiring custom orchestration of speech events.
Automation and API-first orchestration surface
Microsoft Azure AI Speech, Google Cloud Text-to-Speech, Amazon Polly, and IBM Watson Text to Speech expose REST and SDK or API calls that fit backend workflows. ElevenLabs provides an API data model that separates voice assets from audio generation requests for repeatable output across automated pipelines.
A decision framework for picking reading aloud software with the right control depth
Pick based on the integration contract first, then verify governance controls and the data model shape for voice settings. Capti Voice is a fit when the team needs API-first provisioning of speech jobs plus RBAC and audit log visibility.
For apps that already sit inside a cloud platform, start with the service that matches the identity and logging model. Google Cloud Text-to-Speech maps directly to IAM RBAC and Cloud Logging, while Microsoft Azure AI Speech maps to Azure RBAC and audit and monitoring workflows.
Match the integration pattern to how content is produced
For classroom and workplace reading tools that attach to learning and writing tasks, Read&Write focuses on synchronized highlighting and reading controls inside those workflows. For teams that need content-to-audio pipeline ingestion from PDFs and web text, Speechify centers the reading aloud workflow on those input types.
Validate the governance model against required admin controls
If administrative governance for voice settings must be auditable and access-controlled, prioritize Capti Voice for RBAC and audit log visibility tied to configuration changes. If governance must rely on cloud identity and request logs, prefer Google Cloud Text-to-Speech with IAM RBAC and Cloud Logging.
Confirm the automation and API surface matches orchestration needs
For backend automation with structured request configuration, Microsoft Azure AI Speech, Google Cloud Text-to-Speech, Amazon Polly, and IBM Watson Text to Speech provide documented APIs that support programmatic synthesis. For teams that need predictable voice parameter reuse, ElevenLabs separates voice assets from generation requests.
Choose the control schema based on your pronunciation requirements
If pronunciation must be tuned per request, use SSML-driven options in Microsoft Azure AI Speech or IBM Watson Text to Speech, and SSML parsing in Amazon Polly. If speech behavior must follow grammar corrections within learner text, choose Ghotit for grammar-aware spoken pronunciation.
Test repeatability and alignment for the target user workflow
For accessibility workflows that require audio and UI alignment, require Read&Write’s word and paragraph-level highlighting. For standardized reading output across users using common inputs, NaturalReader emphasizes repeatable voice and playback configuration plus conversion from imported documents.
Which organizations benefit from specific reading aloud software approaches
Reading aloud selection depends on whether the priority is learner-aligned playback, content ingestion, or governed automation. Tools that expose RBAC and audit visibility serve teams with administrative controls, while grammar-aware solutions serve learning workflows needing pronunciation tied to corrections.
The segments below map directly to how each tool is positioned for different deployment goals.
Education teams needing governed speech jobs with RBAC and audit trails
Capti Voice fits teams that want API-first provisioning for speech jobs and RBAC plus audit log visibility for voice configuration changes. It also supports extensibility for workflow automation around playback rules.
Enterprise teams building API-driven speech synthesis with cloud-native governance
Google Cloud Text-to-Speech fits teams that want IAM RBAC and Cloud Logging for request metadata on every synthesis call. Microsoft Azure AI Speech fits teams that want Azure RBAC plus SSML controls for pronunciation and pacing.
Product teams that need document and web text ingestion with repeatable playback workflows
Speechify fits teams that need PDF and web text ingestion tied to managed playback settings. NaturalReader fits teams focused on dependable conversion of pasted text, documents, and web content with practical voice and playback controls for repeat usage.
Schools or enterprises requiring synchronized highlighting with speech output
Read&Write fits deployments where paragraph and word-level highlighting must follow speech output for alignment during reading aloud. This model ties reading settings like speed and voice selection to the highlighting experience.
Learning programs focused on grammar-aware pronunciation from learner corrections
Ghotit fits workflows that need spoken pronunciation and feedback behavior driven by grammar processing rules. It adjusts pronunciation based on text corrections without requiring event-driven orchestration.
Common pitfalls in reading aloud software procurement and rollout
Procurement mistakes often come from choosing the wrong integration contract, not from choosing the wrong voice. Governance gaps and API surface limitations show up as operational friction during provisioning, monitoring, and audit readiness.
The pitfalls below tie back to concrete limitations seen across NaturalReader, Ghotit, Read&Write, and the cloud APIs.
Selecting a desktop-first tool without a documented enterprise automation surface
NaturalReader converts documents and web content well, but its API and automation surface is not documented for enterprise orchestration. For automated provisioning and controlled configuration at scale, shift to Capti Voice or cloud-native APIs like Google Cloud Text-to-Speech.
Assuming every tool exposes the same governance primitives
Ghotit’s administration is geared toward configuring usage for end users, and it lacks RBAC, audit log, and provisioning controls geared for enterprise tenants. Capti Voice and Google Cloud Text-to-Speech provide clearer RBAC and audit or logging patterns that match governance requirements.
Ignoring SSML authoring complexity for teams that need simple narration control
Microsoft Azure AI Speech and IBM Watson Text to Speech offer SSML-level control, but SSML authoring adds complexity for teams that need fast iteration on narration. If the workload depends on rapid tuning without structured tags, consider Speechify’s managed playback workflow or NaturalReader’s repeatable configuration.
Overlooking schema alignment requirements for consistent configuration
Capti Voice requires schema alignment for consistent configuration, so integrations must match its provisioning and configuration expectations. If schema mapping effort is unacceptable, teams may prefer Google Cloud Text-to-Speech’s predictable request model with voice and output format fields.
Failing to plan for long-form synthesis constraints and operational tuning
Amazon Polly can constrain long-form synthesis with audio length and character limits, which can break naive job batching. For production pipelines, use throughput tuning, region endpoint planning, and log-based governance like CloudTrail-linked logging patterns in AWS workflows.
How We Selected and Ranked These Tools
We evaluated NaturalReader, Speechify, Capti Voice, Ghotit, Read&Write, Microsoft Azure AI Speech, Google Cloud Text-to-Speech, Amazon Polly, ElevenLabs, and IBM Watson Text to Speech on features, ease of use, and value. Features carried the most weight at 40%, while ease of use and value each accounted for 30% in the overall rating. Scores reflect criteria-based editorial weighting using the provided tool capabilities, not lab measurements or private benchmarks.
NaturalReader separated itself from lower-ranked tools by delivering strong reading controls for producing audio from imported documents and by focusing on dependable conversion of pasted text, documents, and web content. That combination raised its features score and supported a high overall rating by aligning with repeatable generation needs without requiring complex governance setup.
Frequently Asked Questions About Reading Aloud Software
Which tools support API-driven reading aloud rather than desktop playback controls?
How do SSML and structured synthesis inputs differ across enterprise APIs?
What integrations and ingestion formats matter most for reading aloud from PDFs, web pages, and documents?
Which tool is better suited for governed workflows with RBAC and audit logs?
How should teams approach admin controls for multi-tenant deployments?
What data migration steps are typical when switching from one reading aloud workflow to another?
How do throughput and orchestration patterns differ between API-native services and app-style readers?
Which tools support accessibility-style synchronized highlighting, and how does that affect integration?
Which tool fits language learning needs that require pronunciation and correction behavior?
What are common technical failure modes when building reading aloud automation, and where do they surface?
Conclusion
After evaluating 10 education learning, NaturalReader 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.
Tools reviewed
Primary sources checked during evaluation.
aws.amazon.comReferenced in the comparison table and product reviews above.
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