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CloudByte PMS
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CloudByte PMS is the analytics and management layer for AI-assisted engineering teams. A lightweight sync agent captures every Claude Code, GitHub Copilot, and Cursor session โ prompts, responses, tokens, commits, and machine health โ and surfaces per-developer adoption, cost per session/PR, ghost-seat detection, and prompt governance in one org-scoped dashboard with RBAC. BYOK Anthropic keys, self-host and air-gap options. Free for teams up to 5 developers.
What it measures
Governance and security
Integrations
Claude Code ยท GitHub Copilot ยท Cursor ยท GitHub ยท GitLab ยท Bitbucket ยท Linear ยท Jira ยท Slack ยท Anthropic API (BYOK)
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CloudByte PMS's answer:
CloudByte PMS is the analytics and management layer for AI-assisted engineering teams. A lightweight sync agent captures every Claude Code, GitHub Copilot, and Cursor session โ prompts, responses, tokens, commits, and machine health โ and surfaces it in one org-scoped dashboard with RBAC.
What no generic engineering-analytics tool gives you:
CloudByte PMS's answer:
Platforms like Jellyfish measure engineering output broadly; native Copilot analytics only cover Copilot. CloudByte PMS is purpose-built for AI coding telemetry across tools โ Claude Code, GitHub Copilot, and Cursor in one dashboard.
CloudByte PMS's answer:
Engineering managers and CTOs at software teams of 10โ200 developers who have rolled out AI coding tools and now need to answer: who's actually using them, what are they costing, which seats are idle, and is delivery actually improving? Reviewers and platform teams also use it for prompt governance and auditing.
CloudByte PMS's answer:
CloudByte PMS's answer:
CloudByte PMS started when Pranav and Brijesh saw the same pattern across every engineering team adopting AI coding tools: shadow AI everywhere, costs nobody could track, and usage data trapped in silos โ one tool per vendor dashboard, no single picture of what developers were actually doing.
They believed this problem deserved more than a point solution. Their vision: a common platform for AI engineering โ telemetry, security, and governance in one place โ where every team, whatever tools they use, can see adoption, control costs, and set guardrails without slowing developers down.
That vision became CloudByte PMS: a lightweight sync agent capturing every Claude Code, GitHub Copilot, and Cursor session, prompt, and commit into one org-scoped dashboard โ built to make AI-assisted engineering visible, accountable, and safe for everyone.
Based on our record, CircleCI seems to be more popular. It has been mentiond 83 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
CircleCI is another popular and mature platform, with extensive support for plugins / reusable workflows in the form of "orbs". - Source: dev.to / 8 months ago
Everyone is free to use alternative CI/CD workflow pipelines. These are often better than Github Actions. There include - https://circleci.com/ - https://www.travis-ci.com/ - Gitlab Anyone can complain as much as they want, but unless they put the money where their mouth is, it's just noise. - Source: Hacker News / 8 months ago
CircleCI Account: You need an active CircleCI account connected to your GitHub repository where the application code resides. If you donโt have one, sign up at circleci.com. - Source: dev.to / about 1 year ago
In this guide, you will explore how to build a fully automated pipeline for processing and updating a vector database using AWS Lambda and CircleCI. The solution involves extracting text from PDFs, generating embeddings with OpenAI, and storing them in Zilliz Cloud, a managed vector database. You will also set up AWS infrastructure (S3, ECR, and Lambda) and implement a CI/CD pipeline with CircleCI to automate... - Source: dev.to / about 1 year ago
CircleCI: Still solid, but watch pricing and concurrency limits. - Source: dev.to / about 1 year ago
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