June 20, 2024


Automated troubleshooting for Kubernetes

Best for:

  • DevOps Teams
  • Site Reliability Engineers (SREs)
  • IT Operations

Use cases:

  • Automated error resolution in Kubernetes clusters
  • Enhanced monitoring with Datadog integration
  • Proactive system health checks

Users like:

  • IT Operations
  • DevOps
  • Engineering

What is Matt?

###Quick Introduction
Matt is an advanced AI-driven reliability engineer tool designed specifically for automating the troubleshooting process in Kubernetes environments. It leverages machine learning algorithms to identify and rectify issues, making it an invaluable resource for DevOps teams, site reliability engineers (SREs), and IT operations. In essence, Matt simplifies and accelerates the troubleshooting process, saving time and reducing the risks associated with manual error diagnosis.

For anyone managing Kubernetes clusters, Matt is your go-to solution. It can identify potential issues before they escalate, effectively reducing downtime and enhancing system reliability. Whether you’re a seasoned professional or new to Kubernetes, Matt’s user-friendly interface and powerful capabilities ensure you can maintain the seamless operation of your Kubernetes clusters with minimal manual intervention. Its broad range of integrations and compatibility extends even further to Datadog, providing a comprehensive troubleshooting environment.

###Pros and Cons

  1. Automates complex troubleshooting, thus saving significant time.
  2. Supports unlimited users and clusters, making it scalable and practical for larger teams.
  3. Offers tailored advanced features and onboarding for enterprise clients.


  1. The pricing can escalate with the number of vCPUs, potentially making it expensive for large-scale applications.
  2. Dependency on cloud-based environments might not suit all organizations due to compliance concerns.
  3. Limited free-tier support options might not be sufficient for all users.


  • Automated troubleshooting for Kubernetes clusters.
  • Integrates with Datadog for comprehensive monitoring.
  • User-friendly interface designed for both beginners and professionals.

###Features and Functionality:

  • Automated Issue Detection: Utilizes AI to proactively identify and troubleshoot issues in Kubernetes clusters without human intervention.
  • Cross-Platform Integration: Seamlessly integrates with Datadog to extend troubleshooting capabilities beyond just Kubernetes.
  • Scalability: Supports an unlimited number of clusters and users, making it adaptable for organizations of various sizes.
  • Advanced Features for Enterprise: API access, post-change observability, and dedicated onboarding support for custom enterprise plans.
  • Detailed Diagnoses: Offers deep insights into system behavior, helping to pinpoint the root cause of issues to facilitate faster resolutions.

###Integration and Compatibility:
Matt excels in its integration capabilities, particularly with Kubernetes and Datadog. It is designed to work seamlessly within these environments, ensuring that users can leverage its full potential without needing extensive configuration. Additionally, Matt is compatible with various cloud providers, making it flexible and easy to implement in diverse IT setups. This helps to create a cohesive troubleshooting ecosystem that spans multiple platforms and tools.

###Benefits and Advantages:

  • Time Saving: Automates troubleshooting processes, drastically reducing the time spent on manual error diagnosis.
  • Improved System Reliability: Proactive issue detection helps in maintaining higher system uptime and performance.
  • Enhanced Productivity: Reduces the cognitive load on engineers, allowing them to focus on other critical tasks.
  • Comprehensive Monitoring: Integration with Datadog provides an in-depth view of system performance, enabling more granular diagnostics.
  • Scalability: Supports an unlimited number of users and clusters, adapting to the needs of growing organizations.

###Priving and Licensing:
Matt offers a tiered pricing model based on vCPUs, starting with a Free plan that covers one cluster and up to 10 nodes with limited support via Discord.

Do you use Matt?

The Teams-K8s plan, at $2 per vCPU per month, includes unlimited clusters and users with Discord support. The Teams-K8s & Datadog plan, at $5 per vCPU per month, extends troubleshooting to Datadog. The Enterprise plan offers custom pricing and includes all features from the Teams plans plus advanced functionalities and dedicated support.

###Support and Resources:
Support options for Matt are varied and comprehensive. Users have access to Discord community support across all plans, ensuring that there is always a platform for seeking help. For enterprise clients, Matt offers additional support through a dedicated organizational Slack channel, onboarding assistance, and priority troubleshooting assistance.

###Matt as an alternative to
Matt can be considered an effective alternative to manual troubleshooting processes, particularly those reliant on human intervention. Compared to traditional methods, Matt offers automated, quicker, and more reliable diagnoses, which drastically reduces operational overheads and minimizes downtime.

###Alternatives to Matt

  • StormForge: A tool that offers similar capabilities for optimizing Kubernetes performance but may be preferred for its specific focus on cost management.
  • New Relic Kubernetes: Provides comprehensive monitoring and diagnostics but may yield higher costs for smaller teams compared to Matt’s pricing model.
  • Sysdig: Known for its security and monitoring features, it could be favored by organizations prioritizing security compliance as well as performance monitoring.

Matt stands out as a powerful AI-driven tool designed to enhance Kubernetes troubleshooting through automation. Its well-rounded feature set, seamless integrations, and scalable solutions make it an excellent choice for organizations aiming to optimize their Kubernetes operations. Whether you are a small team or an enterprise, Matt’s robust capabilities offer the confidence and efficiency needed to maintain high levels of system reliability and performance.


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