{"slug": "i-gave-an-ai-agent-the-same-deployment-on-four-clouds", "title": "I gave an AI agent the same deployment on four clouds.", "summary": "A developer tested an AI agent (Claude Code with Claude Opus) on four cloud platforms—redu.cloud, AWS, Vercel, and Railway—to deploy a stateful, privacy-first infrastructure. redu.cloud completed the task fastest at ~110 minutes, while AWS took 45% longer and provisioned paid resources without cost confirmation. Vercel failed due to platform limitations, and Railway succeeded but required external services, breaking privacy constraints.", "body_md": "Full disclosure up front: I run one of the four platforms in this test (redu.cloud), so take my framing with that in mind. I recorded every run raw and published every session transcript, so you can check the work instead of trusting me on it.\n\nI wanted an honest answer to a question I actually care about: **how well can an AI agent operate real cloud infrastructure today?** Not a to-do app on a serverless host, real stateful infrastructure. So I gave the same job to the same agent on four clouds and watched what each one let it do.\n\nOne real, stateful, privacy-first deployment. The same outcomes on every platform:\n\nThe rule underneath all of it: **nothing on third-party services.** A private git host, a private database, everything on one network I control. It's a fair stress test because it needs real primitives, not just a place to run a container.\n\nHeld constant across all four: the agent (Claude Code), the model (Claude Opus), the exact prompts, in the same order. Variable: the platform and whatever its tooling and primitives make available. Where a platform reached an outcome by a different mechanism, I counted it as a pass on its own terms.\n\nEvery run was screen-recorded raw, only dead time cut, every mistake left in. This is **n = 1**: a reproducible demonstration of what the agent did, not a statistical study. To repeat it, connect each platform's agent or MCP and issue the same prompts.\n\nCompleted, with everything on one private network the whole way. It wasn't clean: the first backup attempt failed, and the agent rebuilt it live onto durable storage. That's in the video and I left it in. This is the run the others are measured against.\n\nCompleted. AWS has the primitives for all of it, and with a root key its own agent tooling can drive them. Two things stood out. It was slower than the reference run: 45% longer and 29% more tokens. And it **provisioned paid resources three times without asking me to confirm**: a second EC2 instance, a larger build instance, and the VPN node all came up billable with no cost prompt. It asked about scope; it never asked about spend.\n\nCouldn't do it, and that's fair. Vercel is a serverless and frontend platform, built for static sites and short-lived functions. A self-hosted, stateful stack with its own database and a VPN isn't what it's for. Worth including because it marks the edge: this class of platform can't host this class of work, no matter how good the agent is.\n\nCompleted too, and it's the one I'd credit most on developer experience. The interface is the nicest of the four, and it asked before spending, repeatedly, including a cost prompt. Two honest marks against it on this task: it took almost five hours (the reference run took about two), because a one-container-per-service model fought a stack that wasn't designed for it. And it couldn't keep everything private: to finish, the agent had to push custom images to Docker Hub and stand the VPN up on Tailscale, both outside the private network. That's a platform limit, not a failure, and it's the real difference on a privacy-first job.\n\nAgent-active time is measured from first action to verified end state, excluding human latency and usage-cap waits, and including all platform time (boot, build, self-heal). Tokens are reported alongside because they're independent of typing speed.\n\n| Metric | redu.cloud | AWS | Railway |\n|---|---|---|---|\n| Agent-active time | ~110 min | ~160 min (+45%) | ~283 min (+157%) |\n| Output tokens | ~1.11M | ~1.43M (+29%) | ~2.07M (+86%) |\n| Model | Claude Opus | Claude Opus | Claude Opus |\n\nSame model every time, so the gaps aren't the agent being smarter on one platform. They're how hard the platform made the work. Railway needed the most of both because the workload fought its service model at every step. (Vercel is out of this table because it didn't complete the task.)\n\nOn cost, at a 24/7 run-rate:\n\n| redu.cloud | AWS | Railway | |\n|---|---|---|---|\n| Run-rate | ~£84/mo (~$106) | ~$146/mo | idle ~$90 to $100, load $150 to $230 |\n| Billing | flat, provisioned | flat, provisioned | per-minute of usage |\n\nA fair reading: redu.cloud is flat and cheaper than AWS. Railway is cheaper than everyone when it sits idle, and the most expensive under sustained load. For a stack that's actually running, redu.cloud was more affordable in most situations, with no surprise bill. The instances aren't spec-matched either (redu.cloud's box was 4 vCPU / 80 GB, AWS's was 2 vCPU / 30 GB, a gap that favors redu.cloud), so I'm stating it rather than hiding it.\n\nAgents can operate real cloud infrastructure now. But the platform underneath decides how far they get, and the failure modes differ in kind:\n\nSame agent every time, so none of that is the model. It's what the infrastructure lets the agent do.\n\nThe full write-up, all four raw videos, and the exact session transcripts (credentials removed) are here:\n\n[https://redu.cloud/compare/the-proof](https://redu.cloud/compare/the-proof)\n\nWe're a small team, and our own product is one of the four named subjects here, which is exactly why I published every raw session. If I got something wrong, it's all in the transcripts.", "url": "https://wpnews.pro/news/i-gave-an-ai-agent-the-same-deployment-on-four-clouds", "canonical_source": "https://dev.to/zzmilos/i-gave-an-ai-agent-the-same-deployment-on-four-clouds-4g1h", "published_at": "2026-07-13 17:16:46+00:00", "updated_at": "2026-07-13 17:47:32.302241+00:00", "lang": "en", "topics": ["artificial-intelligence", "ai-agents", "developer-tools", "ai-infrastructure"], "entities": ["Claude Code", "Claude Opus", "redu.cloud", "AWS", "Vercel", "Railway", "Tailscale", "Docker Hub"], "alternates": {"html": "https://wpnews.pro/news/i-gave-an-ai-agent-the-same-deployment-on-four-clouds", "markdown": "https://wpnews.pro/news/i-gave-an-ai-agent-the-same-deployment-on-four-clouds.md", "text": "https://wpnews.pro/news/i-gave-an-ai-agent-the-same-deployment-on-four-clouds.txt", "jsonld": "https://wpnews.pro/news/i-gave-an-ai-agent-the-same-deployment-on-four-clouds.jsonld"}}