--- title: Vertex Explainable AI Reviews meta_title: 'Vertex Explainable AI Reviews 2026: Details, Pricing, & Features | G2' meta_description: Filter 17 reviews by the users' company size, role or industry to find out how Vertex Explainable AI works for a business like yours. aggregate_rating: rating_value: 4.7 review_count: 17 scale: '5' date_modified: '2026-09-27' parent_category: name: Artificial Intelligence url: https://www.g2.com/categories/artificial-intelligence --- # Vertex Explainable AI Reviews **Vendor:** Google **Category:** [MLOps Platforms](https://www.g2.com/categories/mlops-platforms) **Average Rating:** 4.7/5.0 **Total Reviews:** 17 **AI Verified:** At least 10 G2 reviewers have confirmed using this product's AI features and functionality. ## About Vertex Explainable AI Understand AI output and build trust. Explainable AI is a set of tools and frameworks to help you understand and interpret predictions made by your machine learning models, natively integrated with a number of Google's products and services. With it, you can debug and improve model performance, and help others understand your models' behavior. You can also generate feature attributions for model predictions in AutoML Tables, BigQuery ML and Vertex AI, and visually investigate model behavior using the What-If Tool. ## Vertex Explainable AI Pros & Cons Pros and Cons are compiled from review feedback and grouped into themes to provide an easy-to-understand summary of user reviews. **What users like:** - Users value the **collaboration capabilities** of Vertex Explainable AI, enhancing teamwork and shared insights effectively. (1 reviews) - Users recognize **cost savings** from using Vertex Explainable AI, enhancing efficiency while managing budgetary constraints. (1 reviews) - Users feel the need for improved **data management capabilities** in Vertex Explainable AI for enhanced explainability. (1 reviews) - Users find **easy access** to Vertex Explainable AI beneficial for understanding AI model outputs efficiently. (1 reviews) - Users note the **easy integrations** of Vertex Explainable AI, allowing seamless connections with existing systems. (1 reviews) - Security Protection (1 reviews) ## Vertex Explainable AI Reviews ### 1. Powerful AI Transparency and Trust-Building Tool for Machine Learning Models **Rating:** 5.0/5.0 stars **Reviewed by:** Khushal M. | Freelance Cloud Consultant, Consulting, Small-Business (50 or fewer emp.) **Current User:** The reviewer uploaded a screenshot or submitted the review in-app verifying them as current user. **Validated Reviewer:** Validated through LinkedIn **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **Reviewed Date:** August 21, 2026 **What do you like best about Vertex Explainable AI?** What I like most about Vertex Explainable AI is its ability to provide clear insights into how machine learning models arrive at their predictions. As organizations increasingly rely on AI for critical business decisions, understanding model behavior becomes just as important as achieving high accuracy. Vertex Explainable AI helps bridge this gap by offering feature attribution and explanation capabilities that make complex models much easier to interpret. The integration with Vertex AI is seamless, allowing explanations to be generated without significant additional development effort. The visualizations and explanation outputs help data scientists, developers, and business stakeholders better understand the factors influencing model predictions. This transparency improves trust in AI systems and makes it easier to validate model behavior before deploying models into production. Another major benefit is its support for responsible AI initiatives. The platform helps identify potential bias, unexpected model behavior, and factors that may negatively impact prediction quality. This visibility is valuable for organizations operating in regulated industries where accountability and explainability are important requirements. Vertex Explainable AI delivers strong value, particularly for organizations that prioritize model transparency, responsible AI practices, and regulatory compliance. While explainability may not directly improve model accuracy, it provides critical visibility into how models make decisions, which can significantly reduce risk and improve stakeholder trust. The value becomes even more apparent as AI adoption scales across the organization. Having explainability capabilities integrated directly into existing machine learning workflows reduces administrative overhead and allows teams to focus on developing and improving models. For enterprises that require explainable and trustworthy AI, the pricing aligns well with the operational, governance, and risk-management benefits the platform provides. **What do you dislike about Vertex Explainable AI?** While Vertex Explainable AI provides valuable insights, the explanations can sometimes require technical expertise to interpret correctly. Business users without a machine learning background may need assistance from data science teams to fully understand certain attribution results and model explanations. Additionally, explanation generation can add computational overhead for large-scale workloads, particularly when dealing with highly complex models or high volumes of predictions. Organizations may also need time to determine which explanation methods are most appropriate for specific use cases and models. Although the platform provides strong capabilities, maximizing its value often requires a solid understanding of machine learning concepts and model evaluation practices. **What problems is Vertex Explainable AI solving and how is that benefiting you?** Vertex Explainable AI solves one of the biggest challenges in modern machine learning: the "black box" nature of AI models. Many high-performing models can generate accurate predictions but provide little visibility into why those predictions were made. This lack of transparency can create concerns around trust, compliance, fairness, and accountability. By providing explainability and feature attribution capabilities, Vertex Explainable AI helps us understand the key drivers behind model decisions, validate model behavior, identify potential bias, and improve governance practices. This has benefited us by increasing confidence in AI-generated outcomes, simplifying model validation efforts, supporting responsible AI initiatives, and making it easier to communicate model decisions to stakeholders, auditors, and business teams. The platform has also helped accelerate AI adoption because decision-makers are more comfortable using models when they can clearly understand and justify the reasoning behind predictions. Overall, it improves transparency, trust, risk management, and operational effectiveness across our machine learning workflows. ### 2. Clear, Visual Model Explanations That Fit Seamlessly into Vertex AI Workflows **Rating:** 4.5/5.0 stars **Reviewed by:** Muhammed A. | Technical Project Manager , Logistics and Supply Chain, Mid-Market (51-1000 emp.) **Current User:** The reviewer uploaded a screenshot or submitted the review in-app verifying them as current user. **Validated Reviewer:** Validated through LinkedIn **Incentivized:** This reviewer was offered a nominal incentive as thanks for completing this review. **Source: Organic Review from User Profile:** Invitation from G2. This reviewer was offered a nominal incentive as thanks for completing this review. **G2 Icon:** Our network of Icons are G2 members who are recognized for their outstanding contributions and commitment to helping others through their expertise. **Reviewed Date:** August 07, 2026 **What do you like best about Vertex Explainable AI?** Vertex Explainable AI has made it much easier to understand why our models produce specific predictions, whether that's document classification results or complaint categorization, instead of treating the model as a black box. Being able to see feature attributions for individual predictions has helped us catch cases where a model was relying on unexpected or irrelevant signals, which we wouldn't have noticed without this kind of visibility. Integration with the rest of our Vertex AI and Google Cloud workflow was smooth, since explanations are generated as part of the same pipeline we already use for training and serving models. Having clear, visual explanations has also made it easier to communicate model behavior to non-technical stakeholders when questions come up about how a decision was made. **What do you dislike about Vertex Explainable AI?** Interpreting feature attribution outputs still requires some ML familiarity to translate into actionable insights, so it's not always immediately intuitive for team members without a technical background. Setting up explanations for more complex model architectures took additional configuration compared to simpler models. Pricing adds an extra cost layer on top of standard model serving, which becomes a bigger consideration as usage scales across more models. Some explanations for edge-case predictions felt less clear or actionable than for more typical, well-represented cases in the training data. **What problems is Vertex Explainable AI solving and how is that benefiting you?** Vertex Explainable AI has solved the problem of not understanding why our models make specific predictions, giving us visibility into feature-level reasoning instead of relying purely on output accuracy metrics. This has helped us catch and correct models relying on unintended patterns, improving trust in production predictions and making it easier to explain model behavior when stakeholders ask. ### 3. Transparent, Trustworthy Model Insights with Seamless Google Cloud Integration **Rating:** 4.5/5.0 stars **Reviewed by:** Anish R. | Member, Small-Business (50 or fewer emp.) **Current User:** The reviewer uploaded a screenshot or submitted the review in-app verifying them as current user. **Validated Reviewer:** Validated through LinkedIn **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **Reviewed Date:** August 04, 2026 **What do you like best about Vertex Explainable AI?** What I like most about Vertex Explainable AI is how it makes machine learning models easier to understand. Instead of treating the model like a "black box," it clearly shows which features had the biggest impact on a prediction. This makes it much easier to build trust in the results and identify potential issues or biases. I also appreciate how well it integrates with the Google Cloud ecosystem, making it simple to use alongside other AI tools. Overall, it's a valuable feature for anyone who wants transparent, reliable, and responsible AI models. **What do you dislike about Vertex Explainable AI?** One thing I dislike about Vertex Explainable AI is that it can be a bit overwhelming for beginners. Understanding the explanations and configuring the features often requires some knowledge of machine learning concepts. It can also take extra time and computing resources when working with large or complex models. While it's a powerful tool, a simpler interface and more beginner-friendly guidance would make it easier to use. **What problems is Vertex Explainable AI solving and how is that benefiting you?** Vertex Explainable AI solves the problem of understanding why an AI model makes a specific prediction. Instead of relying on a system that feels like a "black box," it provides clear explanations about which features influenced the outcome. This has helped me build more confidence in my models, identify potential biases, and troubleshoot prediction errors more quickly. It also makes it easier to communicate AI decisions to teammates and stakeholders, improving transparency and trust in the overall workflow. ### 4. Clear explanations in Vertex AI that streamline validation and deployment in Google Cloud **Rating:** 4.5/5.0 stars **Reviewed by:** Jorge Esteban C. | Senior Web Developer, Mid-Market (51-1000 emp.) **Current User:** The reviewer uploaded a screenshot or submitted the review in-app verifying them as current user. **Validated Reviewer:** Validated through LinkedIn **Source: Organic:** Organic review. This review was written entirely without invitation or incentive from G2, a seller, or an affiliate. **AI Translated:** This review has been translated from Spanish; Castilian using AI. **Reviewed Date:** July 21, 2026 **What do you like best about Vertex Explainable AI?** The most useful thing is being able to identify which variables are influencing a prediction without adding much extra work to the development. This makes it easier to validate models, analyze results, and discuss potential biases or unexpected behaviors. Additionally, being integrated into the Google Cloud ecosystem, it fits well with training and deployment processes, which helps save time and maintain workflow. It is not a perfect tool for all cases, but if you are already working with Vertex AI, it adds considerable value and makes it easier to trust the models. **What do you dislike about Vertex Explainable AI?** The main disadvantage is that the greatest benefit is obtained when you are already working within the Google Cloud ecosystem. If you use tools from other providers, the integration is not as smooth and may require additional work. Furthermore, in very complex models, the generation of explanations can also increase processing time. Overall, it is a quite solid tool, but I think there is still room to simplify some configurations and make the process easier for users with less experience. **What problems is Vertex Explainable AI solving and how is that benefiting you?** Vertex Explainable AI helps address one of the most common problems in machine learning: understanding why a model made a certain decision. In many projects, it is not enough to obtain good predictions; it is also necessary to justify them to clients, business teams, or due to regulatory requirements. The tool makes this process easier by showing which variables had the greatest impact on each result. In my case, this allowed me to validate models more easily, detect unexpected behaviors, and build confidence before deployment. Additionally, it reduced the time I spent explaining the results to people without a technical background, because the explanations are clearer and more actionable. ### 5. Clear, Trustworthy Model Insights with Vertex Explainable AI **Rating:** 4.5/5.0 stars **Reviewed by:** Tarun S. | Software Development Engineer, Mid-Market (51-1000 emp.) **Validated Reviewer:** Validated through a business email account **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **Reviewed Date:** September 10, 2026 **What do you like best about Vertex Explainable AI?** Vertex Explainable AI makes it much easier to understand why a machine learning model is producing a particular prediction. Its feature attribution and explanation capabilities are especially helpful when working with complex models, where it can otherwise be hard to see which inputs are driving the output. I also appreciate that it integrates smoothly with the broader Vertex AI ecosystem, so I can use explanations within an existing ML workflow instead of having to maintain a completely separate explainability solution. **What do you dislike about Vertex Explainable AI?** The initial setup and configuration can take a while, especially for teams that are new to Vertex AI. It also takes some ML knowledge to understand the different explanation methods and to choose the right configuration for a specific model. The documentation is generally solid, but some advanced use cases would benefit from clearer, more straightforward examples. **What problems is Vertex Explainable AI solving and how is that benefiting you?** It helps tackle the challenge of model transparency and debugging. Rather than treating model predictions as a black box, I can dig into which features contributed most to a specific prediction. This is especially useful for validating model behavior, spotting unexpected feature influence, and building greater confidence in ML-powered applications. ### 6. Vertex Explainable AI: Fast, Built-In Attributions That Make Predictions Clear **Rating:** 4.5/5.0 stars **Reviewed by:** Aquib M. | Senior AI policy Agent, Enterprise (> 1000 emp.) **Validated Reviewer:** Validated through LinkedIn **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **Reviewed Date:** September 06, 2026 **What do you like best about Vertex Explainable AI?** Vertext Explainable AI takes the guesswork out of our model predictions. The SHA-based feature attributions run fast and integrate directly into the vertex AI dashboard, no extra setup needed. It's easy enough for non technical stakeholders to follow, yet detailed enough for our data scientist to debug issues, and since it's built into Vertex AI, there's no added cost. On boarding took less than a day. It integrates smoothly with the rest of the Vertex AI pipeline, so explanations show up right alongside predictions without needing a separate tool. We also connect it with big query for data and it fits well into our existing workflow without much extra setup **What do you dislike about Vertex Explainable AI?** The learning curve for interpreting some of the more advanced attribution outputs took a little getting used to more in dashboard guidance for beginners would help. Explanation runs can also add slight latency on larger models, so faster processing at scale would be a nice improvement. **What problems is Vertex Explainable AI solving and how is that benefiting you?** It solves the black-box problem of not knowing why our models make certain predictions. This has sped up approval from non-technical stakeholders, since we can now back decisions with clear, feature-level reasoning instead of just trusting the output blindly. ### 7. Vertex Explainable AI: Clear, Trustworthy Predictions for Better Business Decisions **Rating:** 5.0/5.0 stars **Reviewed by:** Mr. P. | Technical Assistant & E-Comm Specialist Chyren Publication | National & International Book Company, Publishing, Small-Business (50 or fewer emp.) **Validated Reviewer:** Validated through LinkedIn **Source: Thank You page:** Invitation from G2. This reviewer was not provided any incentive by G2 for completing this review. **Reviewed Date:** August 13, 2026 **What do you like best about Vertex Explainable AI?** Vertex Explainable AI is a very useful tool for making AI-based predictions and understanding the reasons behind those predictions. It can help businesses analyze customer data, buying behavior, product demand, sales possibilities, and production-related factors. It can also help identify which customers are more likely to buy a product and which factors influence their decisions. Overall, Vertex Explainable AI is valuable because it not only provides predictions but also helps users understand why the AI made a particular prediction, making the results easier to analyze, trust, and use for better business decisions. **What do you dislike about Vertex Explainable AI?** Nothing to dislike because overall prediction and reason behind pridiction is good and accurate **What problems is Vertex Explainable AI solving and how is that benefiting you?** Vertex is good over all I am trying to use get best pridiction related some details accordingly me and they provide good and accurate answer with good reason behind and very trusty not froad it's really good and easy to use ### 8. Professional journey with vertex explainable ai product **Rating:** 5.0/5.0 stars **Reviewed by:** ANUJ J. | Analyst, Enterprise (> 1000 emp.) **Validated Reviewer:** Validated through LinkedIn **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **G2 Icon:** Our network of Icons are G2 members who are recognized for their outstanding contributions and commitment to helping others through their expertise. **Reviewed Date:** July 21, 2026 **What do you like best about Vertex Explainable AI?** There are several reasons of liking vertex explainable ai which is like it helped me in rapid model debugging, also In place of manually calculating shap values or running post-hoc scripts locally, Vertex Explainable AI allows to enable explanations with a simple configuration toggle when deploying models to an endpoint, and last but not the least ,it's features that it is not restricted to only tabular data is something attracts my attention as it allows not only tabular data but also image data as well as gives flexibility to the framework. **What do you dislike about Vertex Explainable AI?** Few things which I personally dislike about this product is Like most attribution frameworks built on game theory, Vertex explainable AI struggles when input features are highly correlated , also in post-hoc attribution methods in general is the feature attributions which shows correlation within the model's logic, not true physical causation **What problems is Vertex Explainable AI solving and how is that benefiting you?** There are certain problem related to hidden data leak which it solves in a way such that it's feature attributions highlight which exact input features or image pixels drove the decision. If a high-performing fraud model is secretly relying on an arbitrary timestamp field or a loan approval model is heavily weighting a proxy variable for demographic data, explainable AI exposes it immediately during testing. ### 9. Making AI Predictions Easier to Understand! **Rating:** 4.0/5.0 stars **Reviewed by:** rachel M. | Lead Hat Stylist, Small-Business (50 or fewer emp.) **Validated Reviewer:** Validated through a business email account **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **Reviewed Date:** August 15, 2026 **What do you like best about Vertex Explainable AI?** I like that it makes AI model predictions easier to understand by showing which features contributed to a particular prediction. It helps with debugging and gives me more confidence in the model’s results. **What do you dislike about Vertex Explainable AI?** The explanations can sometimes be difficult to interpret, and the accuracy of the attributions can depend on configuration choices like the baseline and number of steps. It would be nice if the explanations were more straightforward and required less tuning. **What problems is Vertex Explainable AI solving and how is that benefiting you?** It helps solve the problem of not knowing why a machine learning model made a particular prediction. By showing which features had the biggest impact on the result, it makes models easier to understand, troubleshoot, and improve. This benefits me by giving me more confidence in the model’s results and helping identify potential issues or biases more quickly. ### 10. Reliable Decision Support for Finance and Debugging **Rating:** 4.5/5.0 stars **Reviewed by:** Prem k. | Associate Technical Manager, Enterprise (> 1000 emp.) **Validated Reviewer:** Validated through a business email account **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **Reviewed Date:** September 21, 2026 **What do you like best about Vertex Explainable AI?** It helps me make reliable decisions in the finance industry, such as loan approvals, and it also helps with debugging. The downside is that there’s no global reasoning across models.it priced additionally to existing API cost **What do you dislike about Vertex Explainable AI?** Sometimes the reasoning in deep AI models is incorrect, which can leave us with error-prone reports. **What problems is Vertex Explainable AI solving and how is that benefiting you?** It solved the decision made by Vertex AI by explaining how it was done and what logic was used to derive the result, which helps me understand the model’s working for accounting purposes. ### 11. End-to-End AI Platform to Build, Train, Deploy, and Monitor Models **Rating:** 5.0/5.0 stars **Reviewed by:** AMOL J. | ASSISTANT PROFESSOR, Mid-Market (51-1000 emp.) **Current User:** The reviewer uploaded a screenshot or submitted the review in-app verifying them as current user. **Validated Reviewer:** Validated through a business email account **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **Reviewed Date:** September 15, 2026 At G2, we prefer fresh reviews and we like to follow up with reviewers. They may not have updated their review text, but have updated their review. **What do you like best about Vertex Explainable AI?** It provides an end-to-end AI platform where you can build, train, deploy, and monitor AI models all in one place. **What do you dislike about Vertex Explainable AI?** Using Vertex Explainable AI effectively ties your AI infrastructure to Google Cloud. **What problems is Vertex Explainable AI solving and how is that benefiting you?** We can build machine learning models without having to manage all of the underlying infrastructure ourselves. ### 12. Smooth Visual Feature Attribution and Easy Setup in Vertex Explainable AI **Rating:** 4.5/5.0 stars **Reviewed by:** Shailendra g. | Software Developer, Mid-Market (51-1000 emp.) **Validated Reviewer:** Validated through LinkedIn **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **Reviewed Date:** August 05, 2026 **What do you like best about Vertex Explainable AI?** What I like most about Vertex Explainable AI is its visual feature attribution, which works smoothly with Google Vertex AI. It performs well with tabular, text, and image data, and provides useful insights. The setup process is also simple, which makes implementation easier and reduces extra effort. **What do you dislike about Vertex Explainable AI?** Integrating tools that are outside the Google ecosystem can sometimes be time-consuming and a bit complex. **What problems is Vertex Explainable AI solving and how is that benefiting you?** Since I work with PII data, the transparency provided by this tool makes it easier to audit models and ensure they stay compliant with different standards. Its reliable performance also helps meet compliance requirements and improves overall ROI. ### 13. Seamless Visual Feature Attribution with Vertex Explainable AI **Rating:** 4.5/5.0 stars **Reviewed by:** Sushant S. | Senior Software Developer, Computer Software, Enterprise (> 1000 emp.) **Validated Reviewer:** Validated through LinkedIn **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **G2 Icon:** Our network of Icons are G2 members who are recognized for their outstanding contributions and commitment to helping others through their expertise. **Reviewed Date:** August 04, 2026 **What do you like best about Vertex Explainable AI?** The best about Vertex Explainable AI is its visual feature attibutions that integrate seamlessly with Google's Vertex AI. It is highly performant when it comes to dealing with tabular data, text data and image data to delivery intelligence. Smooth onboarding reduces implementation overhead. **What do you dislike about Vertex Explainable AI?** Integrating tools outside of Google ecosystem can be tedious and complex **What problems is Vertex Explainable AI solving and how is that benefiting you?** Because I work with PII data, the transparency this tool provides makes it easier to audit models and confirm they remain compliant with multiple standards. Its reliable performance also supports my compliance requirements, which in turn helps improve ROI. ### 14. Advanced Explainability with Sampled Shapley, Integrated Gradients, and XRAI **Rating:** 5.0/5.0 stars **Reviewed by:** Shilo J. | Article Writer, Education Management, Small-Business (50 or fewer emp.) **Current User:** The reviewer uploaded a screenshot or submitted the review in-app verifying them as current user. **Validated Reviewer:** Validated through LinkedIn **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **Reviewed Date:** August 26, 2026 **What do you like best about Vertex Explainable AI?** Uses advanced mathematical approaches like Sampled Shapley, Integrated Gradients, and XRAI. **What do you dislike about Vertex Explainable AI?** Certain tools (like specific prediction volumes, AutoML tasks, or trial limits) include small, recurring free tier allowances per month. **What problems is Vertex Explainable AI solving and how is that benefiting you?** It helps developers to build, scale, govern and optimize agents. ### 15. Improves Trust and Confidence in AI Models **Rating:** 5.0/5.0 stars **Reviewed by:** Manikandan V. | Graduate Engineer Trainee (DevOps / Software Engineer), Information Technology and Services, Enterprise (> 1000 emp.) **Validated Reviewer:** Validated through LinkedIn **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **Reviewed Date:** August 04, 2026 **What do you like best about Vertex Explainable AI?** It stands out for providing clear explanations of model predictions, which makes the model much easier to understand and interpret. **What do you dislike about Vertex Explainable AI?** Inference latency and cost, also initial configuration was complex **What problems is Vertex Explainable AI solving and how is that benefiting you?** Vertex Explainable AI solves the problem of understanding and trusting the prediction made by the machine learning models by providing clear insights on influencing prediction. ### 16. Best Cloud Experience with best services **Rating:** 4.5/5.0 stars **Reviewed by:** jay m. | Senior Associate Consultant, Enterprise (> 1000 emp.) **Current User:** The reviewer uploaded a screenshot or submitted the review in-app verifying them as current user. **Validated Reviewer:** Validated through LinkedIn **Source: G2 invite:** Invitation from G2. This reviewer was not provided any incentive by G2 for completing this review. **Reviewed Date:** April 27, 2025 At G2, we prefer fresh reviews and we like to follow up with reviewers. They may not have updated their review text, but have updated their review. **What do you like best about Vertex Explainable AI?** Vertex Explainable AI needs to be updated with Latest GenAI model explainability which is not there **What do you dislike about Vertex Explainable AI?** Vertex Explainable AI needs to be updated with Latest GenAI model explainability which is not there **What problems is Vertex Explainable AI solving and how is that benefiting you?** Implemented whole MLOps Framework on GCP using Vertex AI ### 17. AI that converts the Video or Audio or Image to text and summarizes **Rating:** 5.0/5.0 stars **Reviewed by:** Seerapu N. | Node JS/Nest JS Developer, Mid-Market (51-1000 emp.) **Current User:** The reviewer uploaded a screenshot or submitted the review in-app verifying them as current user. **Validated Reviewer:** Validated through LinkedIn **Incentivized:** This reviewer was offered a nominal gift card as thank you for completing this review. **Source: G2 invite:** Invitation from G2. This reviewer was offered a nominal gift card as thank you for completing this review. **Reviewed Date:** September 13, 2024 **What do you like best about Vertex Explainable AI?** I like the API support given by GCP as it offers quick response in combination with Google Cloud Storage **What do you dislike about Vertex Explainable AI?** It doesn't support the Streams of videos and that makes it challenging for stream based inputs and it offers Google Cloud Storage to be used. **What problems is Vertex Explainable AI solving and how is that benefiting you?** It summarizes the bulk content and thereby saves time. - [View Vertex Explainable AI pricing details and edition comparison](https://www.g2.com/products/vertex-explainable-ai/reviews?section=pricing&secure%5Bexpires_at%5D=2026-09-30+06%3A00%3A35+-0500&secure%5Bsession_id%5D=33949f56-6bcf-45cc-90fa-5b2108830968&secure%5Btoken%5D=3c4a040645cb62567a15abeff19d59e26d7446c83b090e9ce3a72d79c399195c&format=llm_user) ## Vertex Explainable AI Features **Additional Functionality** - Tagging - Natural Language Processing - Data Extraction - Multi-Language - Predictive Analytics - Drag & Drop - Speech Recognition - Reporting/Analytics - Data Storage Management - Virtual Personal Assistant (VPA) - AI Copilot - Customer Segmentation - Collaboration Tools - Data Import/Export - Generative AI - For eCommerce - Role-Based Permissions - Customizable Branding - Search/Filter - Monitoring - Document Management - API - Data Visualization - Trend Analysis - Machine Learning - Access Controls/Permissions - Alerts/Escalation - Performance Metrics - Real-Time Data - Third-Party Integrations - Mobile App - Multiple Data Sources - For Sales Teams/Organizations - Sentiment Analysis - Activity Dashboard - Chatbot - Workflow Automation **Deployment** - Language Flexibility - Framework Flexibility - Versioning - Ease of Deployment - Scalability **Deployment** - Language Flexibility - Framework Flexibility - Versioning - Ease of Deployment - Scalability **Management** - Cataloging - Monitoring - Governing - Model Registry **Operations** - Metrics - Infrastructure management - Collaboration **Management** - Cataloging - Monitoring - Governing **Generative AI** - AI Text Generation - AI Text Summarization ## Top Vertex Explainable AI Alternatives - [Databricks](https://www.g2.com/products/databricks/reviews) - 4.6/5.0 (1,342 reviews) - [SAS Viya](https://www.g2.com/products/sas-sas-viya/reviews) - 4.3/5.0 (778 reviews) - [Snowflake](https://www.g2.com/products/snowflake/reviews) - 4.6/5.0 (713 reviews)