CloudGo.ai
CloudGo.ai is a cloud decision platform that reads your AWS, GCP or Azure environment in read-only mode, then returns audits, cost models, security findings and Terraform-ready migration plans your engineers can actually act on.
What is CloudGo.ai?
CloudGo.ai calls itself a cloud intelligence platform, and its own framing is worth taking literally: most cloud tools automate execution, while this one is built to make sure the plan is right before execution begins. It connects to an AWS, GCP or Azure account with read-only credentials, ingests the architecture it finds there, and combines that with whatever internal documentation, Terraform files and code you choose to upload. From that raw material it builds a Cloud Context Graph: the services and resources present in each environment, the relationships and dependencies between them, and the data flows and trust boundaries that matter for security. That graph is what separates its answers from a generic chatbot's, because recommendations are grounded in what actually runs rather than in what usually runs.
The output side is deliberately concrete. CloudGo.ai produces structured migration roadmaps, prioritised optimisation recommendations and continuous audit reports, along with diagrams, cost models and Terraform-oriented implementation guidance. The vendor groups the work under three headings: reduce cloud waste, de-risk cloud decisions and standardise best practices across teams. Typical jobs include architecture reviews and design validation, migration and modernisation planning, cost, security and SLA assessments, and the SOWs, estimates and executive reporting that consultancies and internal platform teams have to produce anyway.
Two usage modes exist. CloudGo Advisor is the decision layer you talk to directly, with in-product tools to validate code, estimate cost, visualise infrastructure, plan a deployment and push a repository to GitHub. CloudGo Scale is the enterprise context layer: instead of replacing the model your organisation already uses, it injects cloud context into Claude, GPT Enterprise and other LLMs over an API or MCP connection. The vendor's own benchmark, run twenty times on identical infrastructure-planning tasks, reports 46 percent fewer tokens than Claude paired with MCP servers, one conversational turn instead of 4.4 for ChatGPT and 8.8 for Claude, and three times more cited sources per recommendation. One limit is stated openly in the FAQ: this is an advisor, not a monitoring stack, and it will not watch your cloud or fix incidents on its own.
What it does
- Audit a live AWS, GCP or Azure estate read-only and rank the cost, security and reliability gaps it finds
- Produce a step-by-step implementation plan with migration sequencing, rollout strategy and risk-aware steps
- Generate Terraform guidance, module recommendations and code snippets for all three major providers
- Model cloud spend and estimate savings before any change is applied
- Compare services and providers side by side, with the trade-offs spelled out
- Build a Cloud Context Graph of resources, dependencies, data flows and trust boundaries
- Feed that infrastructure context into Claude, GPT Enterprise or any other model through an API or MCP connection
When to use CloudGo.ai / When not to
A quick filter to help you decide if CloudGo.ai is the right fit.
When to use CloudGo.ai
- Startup founders and CTOs who need senior cloud architecture judgement without hiring a cloud architect
- DevOps, platform and SRE teams preparing a migration, a re-architecture or a cost-reduction push
- Consultancies, systems integrators and MSPs that must turn client audits, SOWs and estimates into a repeatable process
- Engineering leaders who have to justify infrastructure trade-offs to finance, security or the board
- Teams already running Claude or GPT internally who want those models to answer with real infrastructure context
When not to use CloudGo.ai
- Teams looking for runtime monitoring or alerting: CloudGo.ai states plainly that it does not replace an observability stack
- Anyone who expects the tool to apply changes itself, since the cloud integration is deliberately read-only and cannot create, modify or delete resources
- Organisations outside AWS, GCP and Azure, including pure on-premise estates and smaller cloud providers
- Buyers with strict privacy or procurement requirements, as the vendor publishes no privacy policy, no DPA and no GDPR statement
- Occasional or personal users, for whom a 39.99 USD per user per month entry plan is hard to justify once the free credits are gone
How to use CloudGo.ai
A typical end-to-end flow, from setup to results.
- Create a free account on the CloudGo.ai web app; no credit card is required and ten credits are granted to start
- Start a new chat and open the Cloud Integration modal at the bottom right of the screen
- On AWS, create an IAM role with the ReadOnlyAccess managed policy, an AWS_ACCOUNT trusted entity and the account ID and external ID shown by CloudGo, then paste the role ARN back
- As a fallback on AWS, create a dedicated read-only IAM user and paste its access keys instead
- On GCP grant the Viewer role, on Azure register a service principal with the Reader role
- If you would rather not connect an account at all, upload Terraform files or architecture documents instead
- Add internal documentation such as contracts, ADRs and policies so the answers respect your own standards
- Ask the advisor your actual question: a cost review, a provider comparison, a migration sequence, an SLA assessment
- Use the in-chat tools to estimate cost, validate or visualise code, plan a deployment and push the result to GitHub
- Collect the deliverables (reports, diagrams, implementation plans, Terraform snippets) and share them internally
Pros & Cons
Pros
- The cloud integration is read-only by design and revocable at any time, with AssumeRole and an external ID on AWS and a documented least-privilege model on all three providers
- Deliverables are structured and shareable (audits, runbooks, implementation plans) rather than chat answers you have to rewrite
- One tool covers AWS, GCP and Azure, including cross-provider comparisons
- Recommendations come with citations, and the vendor's benchmark claims three times more cited sources than generic assistants
- It can be used as a context layer for an existing LLM through API or MCP instead of replacing it, with a claimed 46 percent token reduction
- The free tier needs no credit card, and Terraform files alone are enough to get a first answer without granting any cloud access
- The cloud integration documentation is unusually explicit about roles, policies, revocation and audit trails
Cons
- No privacy policy is published anywhere, and the site never mentions GDPR, CCPA, a DPA or a subprocessor list
- The terms of service exist only as a Word document opened in a modal, not as a readable web page
- No postal address is published, on the site or on the company's LinkedIn page
- No data retention period is stated, and the terms say nothing about what happens to your data after termination
- The terms allow aggregated anonymous data to be used to improve the vendor's algorithms and models, with no documented opt-out
- The vendor may suspend or terminate an account at any time, for any reason or none, with or without notice
- Public pricing is thin: the homepage shows Custom Pricing for two of its three offers, and the per-seat figure only appears inside the application
Pricing & Plans
A free entry point is available: the plan presented as Free Trial grants ten credits and requires no credit card. The cheapest paid tier is Advisor at 39.99 USD per user per month for 25 credits a month, or 399.90 USD per user per year on annual billing; larger credit allowances of 50, 75 or 100 per month raise the price proportionally. The CloudGo Scale and CloudGo Context offers, as well as the Enterprise tier inside the application, are quoted as Custom Pricing and require contact with the sales team.
- CloudGo credits
- Cloud Advisor agent
- core cloud planning workflows and CloudGo-generated recommendations
- everything in Free plus the Cloud Advisor agent
- cloud provider integration
- file upload and customer support
- cloud and documentation connections
- Cloud Context Graph
- AI-driven architecture
- migration
- cost and security planning
- reports
- diagrams and implementation plans
- continuous analysis and background workflows
- API or MCP connection
- live structured infrastructure context
- persistent resource and dependency understanding
- company documentation and standards
- context optimised per AI request
- usable across your existing agents and models
- custom third-party integrations
- company documentation integration
- Cloud Context Graph
- background jobs
- local hosting
- priority support with weekly calls
- team accounts and SSO
Data, GDPR & hosting
A consolidated view of how CloudGo.ai handles your data.
GDPR overview
There is no GDPR implementation to report. The word GDPR appears nowhere on the site, in the terms of service or in the JavaScript bundles, and neither does CCPA or any other data-protection regime. CloudGo.ai publishes no privacy policy at all: the /privacy, /privacy-policy and /legal addresses return the single-page application shell rather than a document. There is no data processing agreement, no subprocessor list, no Article 27 EU representative and no data protection officer. The only legal text is a terms-of-service file last revised on 6 March 2025, governed by Californian law, which promises reasonable technical, physical, administrative and organisational safeguards without naming a single certification. European buyers should treat this as an open point to raise with the vendor before any procurement decision.
Who owns the data?
Under the terms of service, everything CloudGo.ai collects about your profile, systems, code and users is defined as Customer Data and remains your confidential information. Cloudweaver Inc. undertakes not to disclose it to third parties and not to use it beyond providing the service, although named company employees may access it to understand your needs, make recommendations and deliver support. Third-party vendors and service providers, including the payment processor, may also handle Customer Data, personal data included. The company reserves the right to disclose it when the law or a law-enforcement request demands it, and to transfer all Customer Data to jurisdictions other than your own. No separate privacy policy expands on any of this.
Reuse rights
Your licence is limited, revocable, non-exclusive and restricted to internal business purposes, so the plans, audits and Terraform snippets you generate can be used inside your organisation without asking permission. Beyond that, the terms are restrictive: you may not resell, rent, host or otherwise commercially exploit the service or its content, you may not run it as a service bureau for third parties, and you may not reverse engineer or create derivative works from it. One clause deserves attention for AI teams: you are expressly forbidden from using CloudGo.ai outputs to train or otherwise improve machine-learning models. Nothing in the document states who owns the copyright in generated deliverables, and you remain solely responsible for whatever is stored in your own cloud.
Data retention & training
Hosting summary
CloudGo.ai publishes no hosting information. Neither the site nor the terms of service names a country, a region or a data centre, and there is no privacy policy or trust page to consult. The only contractual statement on the subject is a broad one: the company may transfer all Customer Data to jurisdictions other than your own, without saying which. Technical observation offers hints rather than commitments: the cloudgo.ai domain resolves to an anycast address operated by Fastly and geolocated in the United States, and the application relies on Firebase and the OpenAI API, all of which points to a US-centred stack. None of that is a contractual guarantee. The enterprise tier lists a local hosting option among its features, with no further detail on what it covers. Buyers with data residency requirements should treat hosting location as an unanswered question and raise it directly with the vendor before connecting a production account.
Things to keep in mind
Risks and trade-offs to weigh before adopting CloudGo.ai.
- Granting an account-wide read-only role exposes far more than cost data: configuration, IAM patterns and network topology all become readable by a third party, so scope and revocation should be planned before the first connection
- With no privacy policy, no DPA and no subprocessor list, you cannot tell a regulator or a client whose hands your infrastructure metadata passes through
- The terms let the vendor use aggregated anonymous data to improve its own algorithms and models, and no opt-out is documented
- The terms also allow Customer Data to be transferred to jurisdictions other than yours, which matters if your own contracts promise otherwise
- Plans that look authoritative can quietly deskill a team: the point of the tool is to speed up an architecture review, not to replace the engineer who has to defend the decision afterwards
- The performance figures (46 percent fewer tokens, three times more cited sources, single-turn answers) are the vendor's own unaudited benchmarks and should not be carried into a business case as measured facts
- An account can be suspended or terminated at any time, for any reason or none, so anything you rely on should be exported and kept outside the platform
Setup & Integrations
Technical difficulty
Two very different levels. Signing up is immediate, free and card-free, and you can get a first answer simply by uploading Terraform files or architecture documents, which anyone can do. Connecting a live account is a cloud administrator's task: an AWS IAM role with ReadOnlyAccess, a trusted entity and an external ID, or a dedicated read-only IAM user; a Viewer role on GCP; a Reader service principal on Azure. Access is revoked by deleting the identity. The guided free audit is quoted at about one hour, split between a walkthrough and a review.
Deployment
Integrations
Supported languages
Behind CloudGo.ai
Fundraising
Social
Resources
All the official URLs gathered for verification and reference.
Alternatives
Tools that compete with or complement CloudGo.ai.
Frequently asked questions
What does CloudGo.ai actually do?
Does CloudGo.ai need write access to my cloud?
Which cloud providers are supported?
Will it monitor my cloud and fix problems automatically?
How much does it cost?
Can I use it with the AI model my company already runs?
How technical do I need to be?
What does CloudGo.ai publish about privacy and GDPR?
Who is behind the tool?
Should you pick CloudGo.ai?
CloudGo.ai occupies a narrow but genuinely useful position: it does not run your infrastructure, it tells you what to do with it before anyone touches a console. For a startup without a cloud architect, or a platform team facing a migration it cannot afford to sequence badly, the combination of a read-only connection, a dependency graph and shareable deliverables is a credible substitute for a short consulting engagement. Consultancies and MSPs get something slightly different but equally practical: a way to make audits, SOWs and estimates repeatable. The read-only design is a real reassurance, the integration documentation is unusually candid about least privilege and revocation, and the option to feed cloud context into an existing Claude or GPT deployment rather than adopting yet another assistant is a sensible answer to tool fatigue.
The reservations are not about the product but about the paperwork around it. There is no privacy policy, no mention of GDPR, no data processing agreement, no subprocessor list, no stated retention period and no published postal address. The terms of service, available only as a downloadable Word file, are governed by Californian law, cap liability at twelve months of fees and allow the vendor to terminate an account without notice or reason. They also permit aggregated anonymous data to feed the vendor's own models, with no documented opt-out. Add a company founded in 2024, fewer than ten employees, no security certification and pricing that is only partly public, and the picture is of a promising young tool that a regulated or European buyer should question closely before granting it read access to a production cloud account. Try it on Terraform files first; connect the account later.
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