Find and compare the best Artificial Intelligence software for Splunk User Behavior Analytics in 2025
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Use the comparison tool below to compare the top Artificial Intelligence software for Splunk User Behavior Analytics on the market. You can filter results by user reviews, pricing, features, platform, region, support options, integrations, and more.
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Google Cloud Platform
Google
Free ($300 in free credits) 55,297 RatingsGoogle Cloud Platform offers a wide range of Artificial Intelligence and Machine Learning tools that are designed to streamline data analytics. GCP provides pre-trained AI models and APIs such as Vision AI, Natural Language and AutoML to help businesses integrate AI into their applications. New customers also receive $300 worth of free credits for running, testing, and deploying workloads. This allows them to explore AI capabilities and implement advanced machine-learning solutions without any initial costs. GCP's AI toolkits also integrate seamlessly with the other services to create end-to-end machine-learning pipelines, from data processing to model implementation. These tools are also designed to be highly scaleable, allowing businesses to experiment with AI, and grow their AI powered solutions as their requirements expand. These resources allow businesses to quickly leverage AI, from automation to predictive analytics. -
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Splunk Cloud Platform
Splunk
1 RatingSplunk is a secure, reliable, and scalable service that turns data into answers. Our Splunk experts will manage your IT backend so you can concentrate on your data. Splunk's cloud-based data analytics platform is fully managed and provisioned by Splunk. In as little as two days, you can go live. Software upgrades can be managed to ensure that you have the most recent functionality. With fewer requirements, you can tap into the data's value in days. Splunk Cloud is compliant with FedRAMP security standards and assists U.S. federal agencies, their partners, and them in making confident decisions and taking decisive actions at rapid speed. Splunk's mobile apps and augmented reality, as well as natural language capabilities, can help you increase productivity and contextual insight. Splunk solutions can be extended to any location by simply typing a phrase or tapping a finger. Splunk Cloud is designed to scale, from infrastructure management to data compliance. -
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Splunk Enterprise
Splunk
2 RatingsSplunk makes it easy to go from data to business results faster than ever before. Splunk Enterprise makes it easy to collect, analyze, and take action on the untapped value of big data generated by technology infrastructures, security systems, and business applications. This will give you the insight to drive operational performance, and business results. You can collect and index logs and machine data from any source. Combine your machine data with data stored in relational databases, data warehouses, Hadoop and NoSQL data storages. Multi-site clustering and automatic loads balancing scale can support hundreds of terabytes per day, optimize response time and ensure continuous availability. Splunk Enterprise can be customized easily using the Splunk platform. Developers can create custom Splunk apps or integrate Splunk data in other applications. Splunk, our community and partners can create apps that enhance and extend the power and capabilities of the Splunk platform. -
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Amazon SageMaker
Amazon
Amazon SageMaker, a fully managed service, provides data scientists and developers with the ability to quickly build, train, deploy, and deploy machine-learning (ML) models. SageMaker takes the hard work out of each step in the machine learning process, making it easier to create high-quality models. Traditional ML development can be complex, costly, and iterative. This is made worse by the lack of integrated tools to support the entire machine learning workflow. It is tedious and error-prone to combine tools and workflows. SageMaker solves the problem by combining all components needed for machine learning into a single toolset. This allows models to be produced faster and with less effort. Amazon SageMaker Studio is a web-based visual interface that allows you to perform all ML development tasks. SageMaker Studio allows you to have complete control over each step and gives you visibility. -
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Amazon Sumerian
Amazon
Amazon Sumerian brings a new dimension and depth to your web and mobile apps. 3D immersive experiences bring new life to user experiences on the internet, increasing customer engagement and productivity at work. Amazon Sumerian allows you to create engaging 3D front end experiences. It is integrated with AWS services, allowing you to access machine learning, chatbots and code execution. Your immersive experiences can be accessed via a browser URL. They are compatible with most hardware for AR/VR. -
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Amazon Augmented AI (A2I)
Amazon
Amazon Augmented AI (Amazon A2I), makes it easy to create the workflows needed for human review of ML prediction. Amazon A2I provides human review for all developers. This removes the undifferentiated work involved in building systems that require human review or managing large numbers. Machine learning applications often require humans to review low confidence predictions in order to verify that the results are accurate. In some cases, such as extracting information from scanned mortgage applications forms, human review may be required due to poor scan quality or handwriting. However, building human review systems can be costly and time-consuming because it involves complex processes or "workflows", creating custom software to manage review tasks, results, and managing large numbers of reviewers. -
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Dashboards can be used to monitor service health, troubleshoot alarms, and conduct root cause analysis. Reduce MTTR by integrating ITSM and orchestration tools with real-time event correlation and automated incident prioritization. Advanced analytics such as adaptive thresholding, predictive health scores and anomaly detection can be used to monitor KPI data and prevent problems up to 30 minutes before they occur. Pre-built dashboards allow you to monitor performance and visually correlate services with the underlying infrastructure. Side-by-side comparisons of multiple services can be used to identify root causes. Machine learning algorithms and historical service scores can be used to predict future incidents. You can automatically update your rules using adaptive thresholding or anomaly detection based on historical and observed behavior. This will ensure that your alerts never go out of date.
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