I built a DevOps platform because I was tired of explaining Dockerfiles to myself
How it started: I was on my third startup. The product was the easy part. Every time I needed to deploy something new — a new service, a new environment, a side project — I'd spend half a day reconstructing the same AWS setup I'd done four times before.
ECS cluster. Task definition. ECR repo. IAM roles. Load balancer. TLS cert. GitHub Actions YAML that slowly grew from 20 lines to 80 lines and became load-bearing infrastructure I was afraid to touch.
I'm not a DevOps engineer. I'm a product person who can write code. And I kept running into the same wall: the actual deployment stack required a completely different skillset from building the product.
I started NEXUS AI because I wanted to type one command and have a running app on AWS. That's it. That was the entire original requirement.
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What I built
NEXUS AI is a CLI-first deployment platform. You point it at a GitHub repo (or any source), it detects your runtime, builds a container, provisions the cloud infrastructure in your own AWS/GCP/Azure account, and returns a live URL.
```bash
nexus deploy source \
--repo https://github.com/you/app \
--name my-app \
--provider aws_ecs_fargate
```
No Dockerfile. No YAML. No cloud console. Under 5 minutes for the first deploy, 60–90 seconds for every deploy after.
The key architectural decision I made early: deploy into the customer's cloud account, not a shared NEXUS AI environment. This was harder to build. It required solving IAM, cross-account provisioning, and a lot of AWS-specific plumbing. But it meant customers keep data sovereignty — which opened up regulated industries (healthcare, fintech) that would never trust shared infra.
That decision also meant we couldn't be "just another Render or Railway." We're competing differently.
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The hardest part to build
Framework detection sounds trivial. It's not.
When someone points us at a repo, we need to figure out: what runtime, what version, what start command, what port, what build steps. package.json is straightforward. A monorepo with three services and a custom build script is not.
We got this wrong several times in ways that were silent and confusing. The container would build fine but start wrong. We added a build log stream early — nexus deploy logs --follow — mostly so I could debug our own detection failures faster.
The other hard part: making rollback feel safe. Our first version of rollback worked but required knowing the previous image tag. Nobody knows that. We rebuilt it to store versioned deployment state and let you roll back with just nexus deploy rollback --deployment-id <id>. That took two weeks of work that users now never think about. Which is the point.
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What's working
The MCP integration surprised me. We shipped 37 MCP tools that let Claude and other AI agents deploy, scale, and rollback apps through natural language. I expected this to be a novelty feature that a few power users would play with.
It turns out a meaningful portion of our users are building AI-native products — apps where Claude or another agent is orchestrating infrastructure as part of a larger workflow. The MCP integration went from "interesting experiment" to a real acquisition channel. Anthropic listed us in their MCP directory. That sent a wave of signups we weren't expecting.
The blog has also started compounding. We published SEO content targeting specific deployment queries — "deploy Node.js app without Dockerfile", "CI/CD alternatives", "deploy without DevOps" — and the organic traffic is now meaningful. AEO (optimizing for AI answers, not just Google) is something I've been thinking about a lot. When someone asks Claude or Perplexity "how do I deploy a Node.js app," I want NEXUS AI in that answer. We're getting there.
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What's not working (yet)
Enterprise is slower than I expected. We have the right features — customer-owned cloud, HIPAA-aligned controls, tenant isolation, audit logs — but the sales cycle is long and we don't have enough case studies yet. We've been doing founder-led sales which doesn't scale. This is the thing I'm thinking about most right now.
Pricing is also something I've revised twice. We started too low (bad signal, attracted the wrong users), went up, and are still figuring out where the Pro tier ceiling is. The $149/month Pro plan feels right for SaaS teams. Enterprise is custom. But the gap between those two is wide.
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What I'd do differently
Start with a tighter ICP. We initially said "any developer deploying to the cloud." That's everyone and no one. We've found our best customers are either:
1. Solo founders and small SaaS teams who know they need AWS/GCP but don't want to become AWS experts
2. AI product builders who want managed infra they don't have to think about
The compliance/regulated segment is real but takes longer to convert. If I were starting over, I'd spend the first six months exclusively on segment 1 and 2 before touching enterprise.
The other thing: I should have launched on Product Hunt earlier. We held back waiting until the product felt "ready." The Product Hunt audience is forgiving of rough edges and gives you feedback you can't get any other way. We're launching our MCP integration on Product Hunt soon — better late than never.
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Where we're at
We're post-launch, pre-Series A, growing mostly through content and word of mouth. The dev.to and Hacker News posts have driven more signups than anything we've paid for. The MCP integration is our biggest differentiator right now and I think we're early in what that category becomes.
If you're building something that needs a deployment layer — or if you've built a deployment layer yourself and want to compare notes — I'd genuinely love to hear from you in the comments.
And if you want to try NEXUS AI: [nexusai.run](https://nexusai.run). Free to start, no credit card required.
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What questions do you have? Happy to go deep on anything — the technical architecture, the pricing decisions, the compliance angle, or the MCP integration. Ask in the comments.
