Average Ratings 0 Ratings
Average Ratings 0 Ratings
Description
Open source continuous profiling allows you to identify and resolve your most critical performance challenges across code, infrastructure, and CI/CD pipelines. It offers the ability to tag data based on dimensions that are significant to your organization. This solution facilitates the economical and efficient storage of vast amounts of high cardinality profiling data. With FlameQL, users can execute custom queries to swiftly select and aggregate profiles, making analysis straightforward and efficient. You can thoroughly examine application performance profiles using our extensive suite of profiling tools. Gain insights into CPU and memory resource utilization at any moment, enabling you to detect performance issues before your customers notice them. The platform also consolidates profiles from various external profiling tools into a single centralized repository for easier management. Moreover, by linking to your OpenTelemetry tracing data, you can obtain request-specific or span-specific profiles, which significantly enrich other observability data such as traces and logs, ensuring a comprehensive understanding of application performance. This holistic approach fosters proactive monitoring and enhances overall system reliability.
Description
Security and observability tailored for Kubernetes environments. Implementing security and observability as code is essential for modern cloud-native applications. This approach encompasses cloud-native security as code for various elements, including hosts, virtual machines, containers, Kubernetes components, workloads, and services, ensuring protection for both north-south and east-west traffic while facilitating enterprise security measures and maintaining continuous compliance. Furthermore, Kubernetes-native observability as code allows for the gathering of real-time telemetry, enhanced with context from Kubernetes, offering a dynamic view of interactions among components from hosts to services. This enables swift troubleshooting through machine learning-driven detection of anomalies and performance issues. Utilizing a single framework, organizations can effectively secure, monitor, and address challenges in multi-cluster, multi-cloud, and hybrid-cloud environments operating on either Linux or Windows containers. With the ability to update and deploy security policies in mere seconds, businesses can promptly enforce compliance and address any emerging issues. This streamlined process is vital for maintaining the integrity and performance of cloud-native infrastructures.
API Access
Has API
No
API Access
Has API
No
Integrations
Calico Cloud
No
Calico Enterprise
No
FortiADC
No
Kubernetes
No
Integrations
Calico Cloud
Yes
Calico Enterprise
Yes
FortiADC
Yes
Kubernetes
Yes
Pricing Details
Free
Open source
Free Trial
No
Free Version
Yes
Pricing Details
No price information available.
Free Trial
No
Free Version
Yes
Deployment
Web-Based
Yes
On-Premises
Yes
iPhone App
No
iPad App
No
Android App
No
Windows
Yes
Mac
Yes
Linux
Yes
Chromebook
No
Deployment
Web-Based
Yes
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
No
Mac
No
Linux
No
Chromebook
No
Customer Support
Business Hours
No
Live Rep (24/7)
No
Online Support
No
Customer Support
Business Hours
Yes
Live Rep (24/7)
No
Online Support
No
Types of Training
Training Docs
Yes
Webinars
No
Live Training (Online)
No
In Person
No
Types of Training
Training Docs
Yes
Webinars
Yes
Live Training (Online)
Yes
In Person
Yes
Vendor Details
Company Name
Pyroscope
Founded
2021
Country
United States
Website
pyroscope.io
Vendor Details
Company Name
Tigera
Country
United States
Website
www.tigera.io
Product Features
Product Features
Cloud Security
Antivirus
No
Application Security
Yes
Behavioral Analytics
No
Encryption
No
Endpoint Management
No
Incident Management
No
Intrusion Detection System
No
Threat Intelligence
No
Two-Factor Authentication
No
Vulnerability Management
No
Cloud Workload Protection
Anomaly Detection
No
Asset Discovery
No
Cloud Gap Analysis
No
Cloud Registry
No
Data Loss Prevention (DLP)
No
Data Security
No
Governance
No
Logging & Reporting
No
Machine Learning
No
Security Audit
No
Workload Diversity
No
Container Security
Access Roles / Permissions
No
Application Performance Tracking
No
Centralized Policy Management
No
Container Stack Scanning
No
Image Vulnerability Detection
No
Reporting
No
Testing
No
View Container Metadata
No