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PZ

Prǐz.ai

Prǐz automates cloud service level agreement (SLA) compliance by monitoring cloud service providers (CSPs) for outages, identifying issues, and submitting tickets for credit recovery. This technology enables businesses to efficiently reclaim over $5.8 million in cloud credits, significantly reducing their operational costs with minimal IT involvement.

Austin, United StatesFounded 2024310+ followers
Updated 4 months ago

Funding

Funding not disclosed

Funding rounds are not available yet.

Founders

Product

Problem

Enterprises face challenges in monitoring cloud service provider (CSP) performance against Service Level Agreements (SLAs). Manually tracking outages, identifying SLA violations, and submitting claims for credits is time-consuming and often results in missed opportunities for cost recovery.

Solution

Prǐz automates cloud SLA compliance by continuously monitoring CSP performance, detecting outages, and identifying eligible SLA violations across AWS, Azure, and GCP. The platform automatically generates and submits credit claims to CSPs, enabling businesses to efficiently recover cloud credits. By automating the SLA compliance process, Prǐz reduces the operational burden on IT teams and ensures that organizations receive the credits they are entitled to, leading to significant cost savings.

Target Audience

Prǐz targets enterprises that rely heavily on cloud services from AWS, Azure, or GCP and seek to optimize their cloud spending by automating SLA compliance and credit recovery.

Features

  • Automated monitoring of AWS, Azure, and GCP cloud services for SLA compliance
  • Real-time outage detection and identification of SLA violations
  • Automatic generation and submission of credit claims to CSPs
  • AI-driven verification of claim eligibility based on historical performance data
  • Integration with existing cloud infrastructure and monitoring tools
  • Customizable reporting dashboards for tracking SLA performance and credit recovery
This profile is AI-generated and may contain inaccuracies.