CloudGo.ai connects cloud infrastructure, standards, and decision history to plan and audit AWS, GCP, and Azure. Free trial; custom Scale and Context pricing.
Researched and fact-checked by Bartu Cavusoglu · Updated October 26, 2026
CloudGo.ai is a cloud intelligence platform that describes itself as the cloud knowledge layer for people and AI. It connects a company's infrastructure, internal standards, and decision history so that planning, audits, and optimization work on AWS, Google Cloud, and Azure start with the right context.
The core problem it targets is fragmented knowledge. Architecture diagrams, runbooks, infrastructure code, and cost reports sit in different places, and decisions live in chats or in people's heads. CloudGo aims to keep that background connected so each new task does not begin by rebuilding it.
CloudGo builds two connected layers. The knowledge graph holds what the organization knows: infrastructure, dependencies, provider guidance, and internal standards. The context graph holds what the team learns: conversations, constraints, and recorded decisions. Users connect cloud environments and documentation, then ask questions such as how to modernize a platform safely.
Answers are tied to their evidence. In the example workflow on the site, a migration recommendation cites an architecture overview, a release runbook, and business priorities. CloudGo supports analysis, planning, and review only. Your team validates the output and controls any production change, and read-only audits avoid granting write access.
The Cloud Advisor agent handles planning questions across migration, cost, security, and architecture. The workspace produces technical implementation plans, executive business cases, migration waves, diagrams, and risk and rollback plans. Cost reviews flag likely waste such as overprovisioned compute and idle resources, and audits look for single points of failure, weak backups, and limited observability.
CloudGo also gives Terraform module guidance and snippets for all three clouds, with full project generation and repo-level validation reserved for higher tiers. For AI teams, CloudGo Context exposes live infrastructure context, company standards, and persistent dependency understanding through API or MCP, optimized for each AI request.
CloudGo.ai is a cloud intelligence platform that connects infrastructure, company standards, and decision history into one knowledge layer. Teams and AI tools use it to plan, audit, and optimize workloads on AWS, GCP, and Azure with evidence they can review.
The sources show no public dollar prices. A free trial includes 10 CloudGo credits, while CloudGo Scale and CloudGo Context are custom-priced. The vendor scopes the workflow, data sources, usage, and integrations, then provides a written proposal.
Yes. The Free Trial plan gives 10 CloudGo credits, access to the Cloud Advisor agent, and core cloud planning workflows. It is described as best for individual evaluation and is self-guided.
Both use the same cloud knowledge and decision context. Scale is the workspace for assessment, planning, and review. Context is the integration offering that brings the foundation into your own AI and internal tools through API or MCP.
No. CloudGo supports analysis, planning, and review, and your team validates recommendations and controls implementation. Read-only cloud audits let you assess an environment without granting write access.
CloudGo.ai works with AWS, Google Cloud, and Microsoft Azure. It also provides Terraform guidance and snippets for all three, with full project generation and repo-level validation in higher-tier plans.
It is aimed at CTOs and cloud or platform teams, consultancies, MSPs and systems integrators, and AI and engineering teams. The vendor says it is designed for both technical and non-technical users, including founders and product teams.
The vendor invites customers to bring data handling, access, isolation, and deployment requirements to the technical discussion. Availability and scope are agreed per engagement, and current details come from contacting the company.