GITNUXSOFTWARE ADVICE
Language CultureTop 10 Best Machine Translation Software of 2026
Top 10 machine translation software tools ranked for teams, covering Amazon Translate, Google Cloud, and Microsoft Translator with clear criteria.
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
Amazon Translate is the best fit if you’re an AWS-centric team building real-time and batch translation via API with terminology control, and Microsoft Translator is the stronger pick when your production workflow also needs Azure-aligned text and speech translation APIs.
Editor’s top 3 picks
Three quick recommendations before you dive into the full comparison below — each one leads on a different dimension.
Amazon Translate
Terminology enforcement via custom glossaries lets teams constrain specific source terms and preferred target renderings per language pair.
Built for fits when AWS-centric teams need API-driven translation with terminology enforcement and batch job processing..
Google Cloud Translation
Editor pickGlossary handling inside the managed Translation API helps enforce preferred terminology at request time.
Built for fits when teams need production-grade translation API integration with IAM governance and audit logging..
Microsoft Translator
Editor pickSpeech translation endpoints that combine audio ingestion with translation output for real-time customer and contact-center use.
Built for fits when Azure-based teams need text and speech translation APIs plus glossary control in production workflows..
Related reading
Comparison Table
Amazon Translate
API-firstNeural machine translation service for real-time and batch translation in AWS environments.
Terminology enforcement via custom glossaries lets teams constrain specific source terms and preferred target renderings per language pair.
Amazon Translate provides a translation API for online requests and a batch translation capability for processing larger volumes as jobs. It supports glossary-based terminology enforcement and custom terminology, which helps maintain consistent translations across product or support vocabularies. Language coverage is configured per request or job, and outputs can be returned as plain text for APIs or written in job outputs for batch runs.
A key tradeoff is that governance and quality controls typically require more workflow design than purely GUI-centric tools, especially for post-edit loops and acceptance thresholds. Amazon Translate fits best when translation must run inside an existing AWS data flow, such as translating content as it is ingested into a content system or as multilingual messages are generated in near real time.
- +IAM-controlled API access supports enterprise permission boundaries
- +Glossary and terminology injection reduce inconsistent phrasing
- +Batch translation jobs fit high-volume document workflows
- +CloudWatch metrics and job status simplify operational monitoring
- –Quality gates and post-edit workflows require custom orchestration
- –Glossary enforcement adds setup and version management work
- –Custom adaptation setup takes engineering effort and iteration
- –Complex XLIFF or TMX-centric pipelines need additional tooling
Customer support operations teams
Translate inbound tickets in real time
Fewer term regressions across tickets
E-commerce content teams
Batch translate catalog descriptions
Faster multilingual catalog publication
Show 2 more scenarios
Localization engineering teams
Automate translation inside AWS pipelines
Controlled throughput and traceability
Translation runs as part of event-driven workflows with IAM permissions and job-level monitoring.
Product documentation teams
Standardize technical terminology
More consistent technical wording
Glossaries enforce preferred technical phrases while batch jobs translate documentation sections.
Best for: Fits when AWS-centric teams need API-driven translation with terminology enforcement and batch job processing.
More related reading
Google Cloud Translation
API-firstCloud machine translation service with text, document, and custom model options.
Glossary handling inside the managed Translation API helps enforce preferred terminology at request time.
Teams using Google Cloud Translation typically connect translation into existing backend systems via the Translation API for synchronous calls and use batch requests for file-based workflows. Glossary support lets teams inject preferred terms for domain-specific outputs, and content can be supplied in common interchange formats for end-to-end pipelines. Operational governance is handled through Google Cloud IAM and audit logs, which tie translation access to projects and roles. This combination fits organizations that treat translation as part of a controlled production data flow rather than an ad hoc workflow.
A tradeoff is that glossary enforcement depends on correct term coverage and language mapping, so poor glossary design can leave outputs inconsistent across categories. Another tradeoff is that advanced workflow features like human-in-the-loop review are not part of the translation API itself, so teams often pair it with their own review UI and storage. A common usage situation is translating customer support tickets in real time while logging translation requests for traceability and using batch processing for knowledge base updates.
- +Strong API integration for batch and synchronous translation requests
- +Glossary support enables preferred term usage for domain-specific outputs
- +Google Cloud IAM plus audit logs support controlled access and traceability
- +Predictable request-based workflow fits automated translation pipelines
- –Glossary outcomes depend heavily on term coverage and language selection
- –Human review workflows require separate tools outside the translation API
Customer support operations teams
Real-time ticket translation with logged requests
Faster multilingual resolution cycles
Global product content teams
Batch knowledge base localization with glossary terms
Consistent term usage at scale
Show 2 more scenarios
Developers building localization workflows
Embed translation in backend services
Lower engineering friction
A unified REST integration supports synchronous translation for user-facing features.
Security and compliance teams
Govern translation access with IAM
Better access accountability
Project-scoped permissions and audit logs track who triggered translation calls.
Best for: Fits when teams need production-grade translation API integration with IAM governance and audit logging.
Microsoft Translator
enterpriseMachine translation service for text, documents, speech-connected workflows, and custom translation.
Speech translation endpoints that combine audio ingestion with translation output for real-time customer and contact-center use.
Microsoft Translator on Azure is designed for production use with both synchronous translation calls and batch translation jobs for large volumes. Azure Text-to-Speech and speech translation endpoints allow speech-to-text translation workflows where audio input must be processed and translated in the same system boundary. Glossary support and terminology injection are available for consistency when teams translate recurring product, legal, or support language. The platform also aligns with Azure identity and management patterns used for other Azure services, which helps centralize access control in enterprise environments.
A tradeoff is that Microsoft Translator workflows often require more Azure-specific setup than a purely MT-focused UI workflow, especially when wiring batch jobs, custom glossary behavior, and downstream formatting. It fits situations where translation is embedded into an application or document processing pipeline that already uses Azure storage, orchestration, and identity controls.
- +Synchronous and batch translation APIs support app and document pipelines
- +Speech translation endpoints support real-time audio-to-text scenarios
- +Glossary terminology features help enforce consistent wording
- +Azure identity integration supports enterprise access management patterns
- –Azure-centric setup adds integration work for teams outside Azure
- –Advanced workflow formatting often needs external orchestration
contact center operations
Translate live agent calls
Lower language barriers during calls
support ops teams
Batch translate help articles
Faster localized documentation updates
Show 2 more scenarios
platform engineering teams
Embed translation in apps
Consistent translation inside products
Synchronous translation endpoints integrate into user-facing services with deterministic request handling.
legal localization teams
Enforce terminology for documents
More consistent language in deliverables
Terminology injection via glossary inputs reduces variation across contract and policy translations.
Best for: Fits when Azure-based teams need text and speech translation APIs plus glossary control in production workflows.
Intento
enterpriseMachine translation routing and evaluation platform that connects multiple MT engines through one layer.
Workflow-driven translation routing that connects MT output with post-edit steps and controlled terminology behavior.
Intento is positioned for enterprise machine translation workflows that combine automated translation with operational quality steps.
Core capabilities include translation memory integration, glossary controls, and an API for submitting and retrieving translation results in automated pipelines.
Configurable automation reduces manual translation handling and keeps terminology usage consistent across repeated requests.
The fit is strongest when translation output must be governed through defined steps rather than returned as a single stateless MT response.
- +API-first translation request and result retrieval for automated workflows
- +Terminology and glossary enforcement tied to translation runs
- +Translation memory integration supports consistency across batches
- +Workflow configuration supports routing through post-edit steps
- –Governed workflow setup can require more coordination than direct MT APIs
- –Advanced quality metrics coverage is less transparent than evaluation-first tools
- –Batch throughput tuning depends on integration design and request patterns
- –Format handling across complex interchange formats may need careful mapping
Best for: Fits when teams need controlled MT plus automation around terminology, memory, and human post-edit steps.
Phrase Language AI
enterpriseLocalization platform with machine translation, quality estimation, and engine management features.
Integrated glossary enforcement inside translation jobs, managed per project settings and applied during MT output generation.
Phrase Language AI powers machine translation workflows that combine neural translation with terminology management and post-editing oriented review. Phrase’s system supports batch and file-based translation so teams can move from source content to deliverables in repeatable runs.
Administrators can manage project settings for language pairs, formats, and glossaries while keeping a consistent translation setup across translators and systems. Phrase also provides an API surface for connecting translation jobs to existing localization tooling.
- +Terminology enforcement works directly in translation workflows via built-in glossary controls
- +API integration supports programmatic batch translation and workflow automation
- +File-based translation and review flows fit localization teams handling deliverables
- +Centralized project configuration helps keep language settings consistent across teams
- –Glossary performance depends on well-structured entries and coverage for each domain
- –Advanced governance requires careful admin setup across projects and user roles
- –Real-time translation patterns need explicit workflow design outside the core editor
- –Multi-step pipelines can add complexity when combining TM and glossary rules
Best for: Fits when localization teams need API-driven, repeatable translation runs with controlled terminology across projects.
Alibaba Cloud Machine Translation
API-firstCloud API for neural machine translation across business and application scenarios.
Terminology injection combined with domain adaptation controls in the same translation workflow.
Alibaba Cloud Machine Translation fits teams that already run workloads on Alibaba Cloud and want translation via managed cloud APIs. It covers batch translation and real-time requests, with support for common interchange formats and integration-friendly request patterns.
Core capabilities include customizable translation behavior such as terminology injection and domain adaptation for consistent output across content types. Evaluation workflows can incorporate post-editing effort by exporting translations and mapping segments for review cycles.
- +Managed APIs for batch and real-time translation requests
- +Terminology injection options for consistent phrasing in production
- +Domain adaptation support for vertical language consistency
- +Supports translation workflows that fit review and post-edit cycles
- –Terminology and domain controls require disciplined configuration
- –Less visibility into model tuning compared with some NMT-focused vendors
- –Workflow tooling depends on external systems for QA and LQA reporting
- –Format conversions can add steps in XLIFF-based review pipelines
Best for: Fits when Alibaba Cloud workloads need automated translation with terminology and domain controls for consistent output.
Yandex Translate API
API-firstCloud machine translation API for text and document translation in Yandex Cloud.
Custom dictionaries and terminology injection can be applied to translation requests for controlled output vocabulary.
Yandex Translate API in yandex.cloud focuses on integrating machine translation directly into applications and workflows through a managed API for text and bulk translation. It supports language routing and project-scoped translation settings, which helps teams keep consistent behavior across requests.
The API also exposes features for terminology handling via custom dictionaries and enforces translation preferences during translation calls. For automation, it fits batch and near real-time translation patterns by combining request-based processing with job-style bulk handling.
- +Terminology control via custom dictionaries for consistent terminology usage
- +Managed API supports both single requests and bulk translation workloads
- +Language pair configuration supports predictable routing across translation calls
- +Project-scoped settings reduce drift between environments
- –Workflow orchestration for human review is not built into the translation API
- –Terminology enforcement depends on dictionary management and upkeep
- –Quality tuning options are narrower than dedicated custom engine programs
- –Deep format handling for enterprise interchange formats can require preprocessing
Best for: Fits when teams need API-driven translation with dictionary-based terminology control.
Wordbee
enterpriseTranslation management software with machine translation, terminology, translation memory, and quality workflows.
Job-level glossary enforcement that persists across translation runs using configurable terminology injection controls.
Wordbee focuses on production-grade machine translation workflows with glossary handling and post-editing oriented outputs. The tool is built around configurable translation pipelines that can include segmentation rules and terminology injection per job.
Wordbee also supports enterprise integration patterns through connectors and API-driven automation for batch and file-based translation. Teams using XLIFF and TMX-compatible assets can align translation work with existing localization processes.
- +Configurable terminology injection using job-specific glossaries
- +API and connector options for batch file translation automation
- +Segmentation rule configuration to control how text is split
- +XLIFF and TMX-aligned workflow fit for localization projects
- –Terminology and segmentation require setup to match editorial expectations
- –Automation depth favors scripted workflows over fully self-serve translation
- –Real-time translation coverage is less central than batch processing
- –Complex pipeline configuration can slow initial rollout
Best for: Fits when localization teams need configurable batch translation with controlled terminology and integration automation.
GlobalLink
enterpriseEnterprise localization software with machine translation, translation memory, and workflow management.
Human review workflow controls that combine MT output, terminology enforcement, and production routing inside one localization process.
GlobalLink performs enterprise machine translation orchestration with translation workflow tooling built around file formats, terminology controls, and post-editing support. It targets translation supply chains that need controlled language behavior, human review gates, and repeatable production processes for multilingual content.
GlobalLink also supports integration work through connectors and API-style interactions that fit into existing localization systems and content pipelines. Governance features focus on role-based access, auditability, and configuration of translation rules across projects.
- +Workflow features support human review gates alongside machine output
- +Terminology controls help enforce consistent translations across large programs
- +Administration supports RBAC-style role separation for project access
- +Batch translation handling suits high-volume localization cycles
- –Onboarding requires more configuration than API-first translation services
- –Realtime translation is not the primary fit versus file and workflow runs
- –Advanced governance and rule setups add overhead to change management
Best for: Fits when localization teams need governed MT output within a managed production workflow.
Crowdin
SMBLocalization platform with machine translation integrations, translation memory, and developer workflows.
Crowdin’s MT runs are managed as part of its localization workflow, with glossary-driven terminology enforcement.
Crowdin targets teams that combine translation management workflows with NMT output delivery, so machine translation is managed alongside localization tasks. The system handles glossary and terminology enforcement inside a project workflow, and it supports terminology injection during MT runs.
Crowdin also emphasizes automation and integration through APIs, webhooks, and common localization connectors for moving content, jobs, and translated files. For organizations comparing MT vendors, Crowdin’s differentiator is its localization-centric workflow control rather than a stand-alone translation engine.
- +Translation workflow built around MT jobs instead of treating MT as an add-on
- +Terminology handling supports glossary-driven consistency during translation runs
- +API and automation surface support job creation, status tracking, and pipeline integration
- +Human review workflows integrate with translation assets and delivery steps
- –Advanced MT configuration needs more setup than engine-only offerings
- –Real-time translation capabilities are less central than batch and project-driven runs
- –MT evaluation metrics and tuning controls are not as granular as specialized research tools
- –Complex governance across many projects requires consistent role design
Best for: Fits when localization teams need MT delivered inside translation workflows with terminology control and automation.
Conclusion
After evaluating 10 language culture, Amazon Translate 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 machine translation software
Machine translation software powers automated translation for text and content workflows, often exposed as an API for synchronous requests, batch jobs, or integrated localization pipelines. This buyer’s guide covers Amazon Translate, Google Cloud Translation, Microsoft Translator, Intento, Phrase Language AI, Alibaba Cloud Machine Translation, Yandex Translate API, Wordbee, GlobalLink, and Crowdin.
The ordering and selection focus on integration depth, automation and API surface, and terminology enforcement behavior inside translation runs. These mechanisms show up in how Amazon Translate uses IAM-controlled API access plus custom glossaries, and how Google Cloud Translation includes glossary handling inside the managed Translation API.
Machine translation software for API-driven translation with workflow and terminology control
Machine translation software takes source content and returns translated output for app, document, or localization workflows, usually through an API that supports single requests and batch translation jobs. Terminology control is a key differentiator because tools like Amazon Translate apply custom glossaries to constrain source terms and preferred target renderings.
Many teams also require automation around translation steps, including routing to post-edit, terminology injection tied to translation runs, and governance controls that limit who can call translation APIs. Google Cloud Translation serves this pattern with a managed Translation API that supports batch and synchronous translation requests plus glossary support for domain-specific outputs.
Machine translation evaluation: integration, terminology control, and automation surfaces
The main differentiators show up in how translation requests are governed and how terminology is enforced during output generation. Tools that apply glossaries at request time reduce downstream post-edit effort and keep large catalogs consistent.
Integration depth also drives operational fit because teams need both synchronous translation calls and batch translation jobs inside existing systems. Automation and workflow capabilities matter because some tools route MT results into post-edit and review steps while others provide translation only and expect orchestration outside the platform.
Glossary and terminology enforcement inside the translation run
Amazon Translate enforces terminology using custom glossaries that constrain source terms and preferred target renderings. Google Cloud Translation supports glossary handling directly inside the managed Translation API so preferred terms appear in outputs at request time.
API governance with access control and audit logging support
Amazon Translate uses IAM-controlled API access so enterprise permission boundaries apply to who can call translation endpoints. Google Cloud Translation provides production-grade API integration paired with IAM governance and audit logging.
Workflow-grade translation routing and human-in-the-loop controls
Intento uses workflow-driven translation routing that connects MT output with post-edit steps and controlled terminology behavior. GlobalLink combines human review workflow controls with terminology enforcement and production routing inside one localization process.
Speech-to-translation endpoints for real-time audio workflows
Microsoft Translator includes speech translation endpoints that ingest audio and return translation output for real-time customer and contact-center use. Amazon Translate focuses on text translation with batch job processing and API-driven translation calls rather than audio endpoints.
Batch and synchronous request coverage for app and document pipelines
Microsoft Translator supports both synchronous and batch translation APIs for app pipelines and document workflows. Google Cloud Translation supports batch and synchronous translation requests through the managed Translation API.
Domain adaptation and terminology injection in the same workflow
Alibaba Cloud Machine Translation combines terminology injection with domain adaptation controls in one translation workflow. Wordbee applies job-level glossary enforcement that persists across translation runs using configurable terminology injection controls.
How to choose machine translation software for integration and terminology control
Start by mapping how translation will be called in production so the platform matches the request shape. Teams that need synchronous calls for live features and batch jobs for files should prioritize tools that support both patterns through the same API surface.
Next, choose a terminology strategy that matches governance and editorial expectations. Some platforms enforce glossaries at request time inside the translation API, while others add workflow routing and post-edit steps that change how translations are approved and corrected.
Match the production call pattern: synchronous API, batch jobs, or both
If the production system requires real-time translation calls, tools with synchronous API support and documented batch request behavior reduce integration work. Google Cloud Translation and Microsoft Translator both support synchronous and batch translation requests for mixed app and document pipelines.
Pick glossary enforcement that aligns with how terminology is maintained
If terminology must be enforced during translation output generation, choose tools that apply glossaries inside the translation API or translation jobs. Amazon Translate and Google Cloud Translation apply terminology at request time through custom glossary behavior, while Phrase Language AI applies integrated glossary enforcement inside translation jobs.
Decide whether workflow routing is required for review and post-edit
If production needs MT output to pass through human review gates and post-edit steps inside the same workflow, choose tools with built-in workflow controls. Intento routes MT output into post-edit steps with controlled terminology behavior, and GlobalLink combines human review controls with terminology enforcement and production routing.
Choose the platform that matches your cloud or stack constraints
If the organization already standardizes on AWS IAM boundaries and AWS-native workflows, Amazon Translate fits the API-first pattern with IAM-controlled access. If the organization standardizes on Azure and needs speech translation endpoints, Microsoft Translator supports audio ingestion plus translation output for real-time scenarios.
Validate dictionary and glossary lifecycle requirements for ongoing consistency
If glossary coverage and dictionary upkeep are expected to be ongoing, select tooling that makes glossary management manageable across languages and domains. Yandex Translate API applies custom dictionaries and terminology injection but depends on dictionary management, while Alibaba Cloud Machine Translation requires disciplined configuration for terminology and domain controls.
Confirm automation depth for batch files and connector-driven translation pipelines
If teams need MT delivered inside their translation workflow rather than as an external add-on, Crowdin’s MT runs are managed as part of its localization workflow. If teams need job-level controls and connector options for scripted batch file automation, Wordbee focuses on job-specific glossary enforcement with API and connector options.
Who should buy each machine translation approach
Machine translation software fits best when the buying team has a clear translation call pattern and a defined terminology maintenance process. The strongest fit comes from aligning integration requirements with the terminology enforcement behavior and the workflow governance model.
Different tools concentrate on different production needs. Some focus on API-driven translation with glossary controls, and others extend into workflow routing, review gates, or speech translation endpoints.
AWS-centric platform teams building translation features in apps
Amazon Translate supports IAM-controlled API access and glossary enforcement via custom glossaries that constrain preferred target renderings. It also fits batch job processing for content pipelines that need consistent terminology.
Localization teams standardizing on cloud managed APIs with governance and auditability
Google Cloud Translation provides production-grade API integration with IAM governance and audit logging support. Glossary handling inside the managed Translation API helps enforce preferred terminology at request time.
Enterprises with contact-center or customer support audio translation requirements
Microsoft Translator offers speech translation endpoints that ingest audio and return translation output in real time. This matches contact-center workflows that need translation alongside audio processing rather than file-based translation only.
Program teams that require MT output to pass through review and post-edit inside the same workflow
GlobalLink includes human review workflow controls plus terminology enforcement and production routing in one managed localization process. Intento adds workflow-driven translation routing that connects MT output with post-edit steps and controlled terminology behavior.
Localization operators who manage terminology per domain and want job-level control
Wordbee supports job-specific glossaries with terminology injection controls that persist across translation runs. Alibaba Cloud Machine Translation combines terminology injection and domain adaptation controls, which suits domain-driven configuration processes.
Common machine translation buying mistakes that break production workflows
A frequent failure mode is selecting a tool based on output quality alone while ignoring how terminology enforcement and workflow governance are handled. Tools that enforce terminology well in one workflow can still require extra orchestration elsewhere if review and post-edit steps are not built in.
Another common issue is misaligning glossary lifecycle and dictionary upkeep with editorial processes. If the team cannot provide consistent glossary coverage per language pair, terminology enforcement behavior becomes inconsistent across translation runs.
Treating glossary behavior as a substitute for a real terminology governance process
Amazon Translate and Phrase Language AI both enforce terminology via glossaries inside translation runs, but glossary coverage still has to match the domains and language pairs. Without structured glossary entry management, enforcement accuracy drops because the platform can only apply what is provided.
Assuming review and post-edit gating is built into an MT API
Google Cloud Translation and Amazon Translate focus on translation requests and glossary behavior inside the API rather than human review gates. GlobalLink and Intento provide human review workflow controls and post-edit routing inside the localization workflow, which reduces external orchestration needs.
Building a real-time audio pipeline on a text-focused translation service
Microsoft Translator includes speech translation endpoints that match audio-to-translation requirements for contact-center scenarios. Tools focused on dictionary or glossary controls for text translation, like Yandex Translate API, do not provide speech translation endpoints as a core fit.
Underestimating the configuration discipline required for domain adaptation and terminology injection
Alibaba Cloud Machine Translation combines terminology injection with domain adaptation controls that require disciplined configuration. Yandex Translate API similarly relies on custom dictionary management, so inconsistent upkeep creates terminology drift across bulk requests.
How We Selected and Ranked These Tools
We evaluated integration depth by mapping how each platform supports synchronous translation requests and batch translation jobs in real production flows. We evaluated automation and API surface by comparing how each tool returns translation results for scripted workflows and whether it routes MT output into post-edit or human review steps within the platform.
We evaluated ease and value by focusing on how quickly teams can wire terminology enforcement behavior into calls, including glossary handling inside Amazon Translate and Google Cloud Translation. Amazon Translate set the ranking pace due to IAM-controlled API access combined with custom glossary-based terminology enforcement that reduces inconsistent phrasing across translation runs.
Frequently Asked Questions About machine translation software
How do DeepL, Amazon Translate, and Google Cloud Translation differ in terminology enforcement for production MT calls?
Which tool supports workflow-driven translation routing into post-edit steps and translation memory workflows?
When do teams choose a managed cloud MT API like Google Cloud Translation over a localization workflow platform like Crowdin?
What breaks if glossary enforcement must be consistent across batch jobs and human review handoffs?
Which platforms expose an integration API surface for automation and batch translation jobs?
How do secure on-premises or private network deployment requirements affect tool selection for enterprise teams?
What is the operational control model difference between Amazon Translate and Google Cloud Translation for tracking throughput and failures?
Which tool is best aligned to Azure speech plus text translation workflows with enterprise governance controls?
How should teams migrate translation assets like XLIFF or TMX into MT workflows when building automation?
Where does Alibaba Cloud Machine Translation fall short compared with tools that emphasize review gates and human-in-the-loop routing?
Tools reviewed
Primary sources checked during evaluation.
aws.amazon.comReferenced in the comparison table and product reviews above.
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