Best Anomaly Detection Software for Amazon S3
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Compare the Top Anomaly Detection Software that integrates with Amazon S3 as of October 2025
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This a list of Anomaly Detection software that integrates with Amazon S3. Use the filters on the left to add additional filters for products that have integrations with Amazon S3. View the products that work with Amazon S3 in the table below.
What is Anomaly Detection Software for Amazon S3?
Anomaly detection software identifies unusual patterns, behaviors, or outliers in datasets that deviate from expected norms. It uses statistical, machine learning, and AI techniques to automatically detect anomalies in real time or through batch analysis. This software is widely used in cybersecurity, fraud detection, predictive maintenance, and quality control. By flagging anomalies, it enables early intervention, reduces risks, and enhances operational efficiency. Advanced versions offer customizable thresholds, real-time alerts, and integration with analytics dashboards for deeper insights. Compare and read user reviews of the best Anomaly Detection software for Amazon S3 currently available using the table below. This list is updated regularly.
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1
Edge Delta
Edge Delta
Edge Delta is a new way to do observability that helps developers and operations teams monitor datasets and create telemetry pipelines. We process your log data as it's created and give you the freedom to route it anywhere. Our primary differentiator is our distributed architecture. We are the only observability provider that pushes data processing upstream to the infrastructure level, enabling users to process their logs and metrics as soon as they’re created at the source. We combine our distributed approach with a column-oriented backend to help users store and analyze massive data volumes without impacting performance or cost. By using Edge Delta, customers can reduce observability costs without sacrificing visibility. Additionally, they can surface insights and trigger alerts before data leaves their environment.Starting Price: $0.20 per GB -
2
Elastic Observability
Elastic
Rely on the most widely deployed observability platform available, built on the proven Elastic Stack (also known as the ELK Stack) to converge silos, delivering unified visibility and actionable insights. To effectively monitor and gain insights across your distributed systems, you need to have all your observability data in one stack. Break down silos by bringing together the application, infrastructure, and user data into a unified solution for end-to-end observability and alerting. Combine limitless telemetry data collection and search-powered problem resolution in a unified solution for optimal operational and business results. Converge data silos by ingesting all your telemetry data (metrics, logs, and traces) from any source in an open, extensible, and scalable platform. Accelerate problem resolution with automatic anomaly detection powered by machine learning and rich data analytics.Starting Price: $16 per month -
3
Amazon GuardDuty
Amazon
Amazon GuardDuty is a threat detection service that continuously monitors for malicious activity and unauthorized behavior to protect your AWS accounts, workloads, and data stored in Amazon S3. With the cloud, the collection and aggregation of account and network activities is simplified, but it can be time consuming for security teams to continuously analyze event log data for potential threats. With GuardDuty, you now have an intelligent and cost-effective option for continuous threat detection in AWS. The service uses machine learning, anomaly detection, and integrated threat intelligence to identify and prioritize potential threats. GuardDuty analyzes tens of billions of events across multiple AWS data sources, such as AWS CloudTrail event logs, Amazon VPC Flow Logs, and DNS logs. With a few clicks in the AWS Management Console, GuardDuty can be enabled with no software or hardware to deploy or maintain. -
4
Qualytics
Qualytics
Helping enterprises proactively manage their full data quality lifecycle through contextual data quality checks, anomaly detection and remediation. Expose anomalies and metadata to help teams take corrective actions. Automatically trigger remediation workflows to resolve errors quickly and efficiently. Maintain high data quality and prevent errors from affecting business decisions. The SLA chart provides an overview of SLA, including the total number of SLA monitoring that have been performed and any violations that have occurred. This chart can help you identify areas of your data that may require further investigation or improvement. -
5
Acryl Data
Acryl Data
No more data catalog ghost towns. Acryl Cloud drives fast time-to-value via Shift Left practices for data producers and an intuitive UI for data consumers. Continuously detect data quality incidents in real-time, automate anomaly detection to prevent breakages, and drive fast resolution when they do occur. Acryl Cloud supports both push-based and pull-based metadata ingestion for easy maintenance, ensuring information is trustworthy, up-to-date, and definitive. Data should be operational. Go beyond simple visibility and use automated Metadata Tests to continuously expose data insights and surface new areas for improvement. Reduce confusion and accelerate resolution with clear asset ownership, automatic detection, streamlined alerts, and time-based lineage for tracing root causes. -
6
Validio
Validio
See how your data assets are used: popularity, utilization, and schema coverage. Get important insights about your data assets such as popularity, utilization, quality, and schema coverage. Find and filter the data you need based on metadata tags and descriptions. Get important insights about your data assets such as popularity, utilization, quality, and schema coverage. Drive data governance and ownership across your organization. Stream-lake-warehouse lineage to facilitate data ownership and collaboration. Automatically generated field-level lineage map to understand the entire data ecosystem. Anomaly detection learns from your data and seasonality patterns, with automatic backfill from historical data. Machine learning-based thresholds are trained per data segment, trained on actual data instead of metadata only.
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