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Grafana CloudGrafana Labs provides the leading AI-powered observability platform, built around Grafana—the most widely adopted open source technology for dashboards and visualization. Recognized as a Leader in the 2025 Gartner® Magic Quadrant™ for Observability Platforms, Grafana Labs supports more than 25 million users and thousands of organizations worldwide, from startups to Fortune 500 enterprises. Grafana Cloud is the open observability cloud, delivering full-stack visibility across modern applications, infrastructure, and digital services. Built on open source, open standards, and open ecosystems, the platform unifies metrics, logs, traces, and profiles into a scalable observability experience that helps teams detect issues earlier, resolve incidents faster, and operate more efficiently. At the core of Grafana Cloud is the open-source LGTM stack: Grafana for dashboards and visualization, Mimir for scalable metrics, Loki for logs, and Tempo for distributed tracing. Native OpenTelemetry and Prometheus support make it easy to collect telemetry from any environment, while hundreds of integrations connect existing systems and tools—allowing organizations to extend observability without vendor lock-in. Grafana Cloud also introduces powerful AI-driven observability capabilities. Grafana Assistant helps teams explore data, investigate incidents, and troubleshoot faster through an intelligent interface built for engineers. Adaptive Telemetry identifies high-value signals and aggregates the rest, helping organizations reduce telemetry costs while maintaining operational insight. With solutions spanning Kubernetes monitoring, application and infrastructure observability, frontend monitoring, database observability, incident response, synthetic monitoring, and performance testing, Grafana Cloud delivers the clarity teams need to move faster and operate with confidence.
What is Amazon Lookout for Metrics?
To effectively detect irregularities in business metrics, it is crucial to minimize false positives through the application of machine learning (ML). By clustering similar outliers, one can delve into the root causes of these anomalies for a thorough examination. Summarizing these underlying issues and ranking them based on severity ensures that organizations can address the most critical problems first. The integration with AWS databases, storage solutions, and third-party SaaS applications enables ongoing monitoring of metrics and anomaly detection. Additionally, implementing customized automated alerts and responses when anomalies are detected boosts operational efficiency significantly. The Lookout for Metrics tool employs ML to automatically identify anomalies in both business and operational data, while also uncovering their root causes. Detecting unexpected anomalies poses a challenge, especially since conventional methods typically depend on manual processes that often introduce errors. Lookout for Metrics alleviates this complexity, empowering users to identify and analyze data inconsistencies without specialized knowledge in artificial intelligence (AI). Furthermore, this tool enables the monitoring of unusual variations in subscriptions, conversion rates, and revenue, promoting a proactive stance against sudden market shifts. By harnessing sophisticated machine learning approaches, businesses can greatly enhance the precision of their anomaly detection endeavors, ultimately leading to better decision-making and more resilient operations. This strategic application of technology thus not only improves detection but also fosters a culture of continuous improvement within organizations.
What is Actian Data Observability?
Actian Data Observability is a cutting-edge platform that utilizes artificial intelligence to continuously monitor, validate, and uphold the integrity, quality, and reliability of data within modern data ecosystems. This platform features automated Data Observability Agents that evaluate the data as it flows into data lakehouses or warehouses, allowing for the detection of anomalies, clarification of root causes, and support for problem-solving before these issues can disrupt dashboards, reports, or AI applications. By offering real-time insights into data pipelines, it ensures that data remains accurate, complete, and trustworthy throughout its lifecycle. In contrast to conventional techniques that rely on sampling, this system eliminates blind spots by overseeing the full spectrum of data, enabling organizations to identify hidden errors that could undermine analytics or machine learning outcomes. Additionally, its built-in anomaly detection, powered by AI and machine learning, facilitates the prompt identification of irregularities, such as schema changes, data loss, or unexpected distributions, which accelerates the diagnosis and rectification of issues. Ultimately, this forward-thinking methodology greatly increases the confidence organizations have in their data-driven decisions, fostering a culture of data reliability and integrity. Furthermore, as companies continue to depend on data for strategic planning, such a robust observability framework becomes indispensable in navigating the complexities of today’s data landscape.
Integrations Supported
Amazon Redshift
Amazon S3
Amazon CloudWatch
Amazon Simple Notification Service (SNS)
Amazon Web Services (AWS)
Apache Hudi
Apache Iceberg
Apache Spark
Delta Lake
Google Analytics
Integrations Supported
Amazon Redshift
Amazon S3
Amazon CloudWatch
Amazon Simple Notification Service (SNS)
Amazon Web Services (AWS)
Apache Hudi
Apache Iceberg
Apache Spark
Delta Lake
Google Analytics
API Availability
Has API
API Availability
Has API
Pricing Information
Pricing not provided.
Free Trial Offered?
Free Version
Pricing Information
Pricing not provided.
Free Trial Offered?
Free Version
Supported Platforms
SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux
Supported Platforms
SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux
Customer Service / Support
Standard Support
24 Hour Support
Web-Based Support
Customer Service / Support
Standard Support
24 Hour Support
Web-Based Support
Training Options
Documentation Hub
Webinars
Online Training
On-Site Training
Training Options
Documentation Hub
Webinars
Online Training
On-Site Training
Company Facts
Organization Name
Amazon
Date Founded
1994
Company Location
United States
Company Website
aws.amazon.com/lookout-for-metrics/
Company Facts
Organization Name
Actian
Date Founded
1980
Company Location
United States
Company Website
www.actian.com/data-observability/
Categories and Features
Machine Learning
Deep Learning
ML Algorithm Library
Model Training
Natural Language Processing (NLP)
Predictive Modeling
Statistical / Mathematical Tools
Templates
Visualization