Transparent AI development pricing for startups. Learn how Hal9's $2K/month plan helps founders launch AI products in 30 days with predictable costs and clear ROI.
Most founders I talk to have the same frustration. They want to build an AI product, so they start shopping around for pricing. Here's what they find:
AWS SageMaker says "contact us for pricing."
Google Vertex AI shows a rate card with 47 different variables.
H2O.ai talks about "enterprise value."
None of this helps when you're trying to figure out if you can afford to launch next quarter.
The reality? AI development pricing is a mess because most platforms are optimized for one of two extremes: either you're a solo founder gluing together API calls, or you're a Fortune 500 company with a data science team. There's barely anything in between.
At Hal9, we built our pricing for the gap most platforms ignore: founders who need to go from validated idea to working product without spending six months hiring engineers or burning through their seed round.
Here's what I've learned after working with dozens of AI startups. Most founders don't need what the big platforms are selling.
You don't need infinite compute. You need enough to validate with real users.
You don't need 200 microservices. You need a working MVP that does one thing well.
You don't need enterprise SLAs. You need help when things break.
The gap between "I have an idea" and "I have paying customers" is where most AI startups die. Not because the idea was bad. Because they spent months configuring infrastructure instead of talking to users.
Our startup plan starts at $2,000 per month. That includes two things:
Hal9 Onboarding : Our team handles design, development, infrastructure, and maintenance. This is capped by hours per month, which means you get dedicated engineering time without hiring a full team.
Hal9 Platform : Compute, storage, and LLM access. This is capped by compute seconds per month, so you know exactly what you're paying for and when you'll hit limits.
The caps matter because they give you predictability. You can plan your runway. You can forecast when you'll need to scale up. You're not going to wake up to a surprise bill because your app went viral overnight.
I like concrete examples, so here's what this looks like in practice.
Your product analyzes contracts and pulls out key terms. You're targeting small law firms and corporate legal teams.
With $2K/month, you get:
Your first paying customer signs up at $500/month. That's a 4-month payback period to break even on development costs. By month six, you have five customers paying $500 each. You're at $2,500 in revenue, covering your Hal9 costs and generating profit.
ROI calculation : $15K in year-one revenue from a $24K investment = 62% gross margin after platform costs.
You're building an AI shopping assistant that helps people find products based on conversational prompts.
With $2K/month, you get:
You monetize through affiliate revenue at 5% commission. Average order is $100, and you convert 3% of users. That's 150 transactions per month at $5 each = $750 in month one, scaling to $2,250 by month three as you grow users.
ROI calculation : Even at conservative conversion rates, you hit breakeven by month four and start generating positive cash flow.
Let's be honest about alternatives, because you're probably weighing a few paths.
Hiring a full-time engineer : You're looking at $120K-$180K per year in salary, plus recruiting time, benefits, and equity. That's $10K-$15K per month before they write a single line of code. And you still need to manage infrastructure, DevOps, and LLM costs separately.
Using no-code tools : Great for validation, but they break down fast when you need custom logic or want to own your data. You'll spend $200-$500/month on tools like Bubble or Zapier, then hit a wall when you need to scale or customize. Eventually, you rebuild everything anyway.
Going with AWS/GCP directly : You'll spend less upfront, maybe $500-$1,000/month in infrastructure costs. But you're on your own for everything else. No design, no development help, no maintenance. Most founders end up spending 60-80% of their time on infrastructure instead of their product.
Big agency builds : You'll get quoted $50K-$200K for an MVP. They'll take 3-6 months to deliver. You own the code, but you're stuck managing it yourself or paying ongoing retainers for changes.
Hal9 sits in the middle. You get dedicated engineering support without hiring a team. You get infrastructure without managing it. You get predictable costs that scale with your usage.
Most startups outgrow the base plan after 6-12 months. That's a good thing. It means you've found product-market fit and you're generating revenue.
Here's what scaling looks like:
At 10-20 customers : You might bump up to $3K-$4K/month to increase compute capacity and add more onboarding hours for feature requests.
At 50+ customers : You're looking at $5K-$8K/month with more infrastructure and possibly dedicated resources for reliability and performance optimization.
At 200+ employees : You move to our Enterprise plan, which includes on-premise deployment, custom integrations with your data warehouse, and enterprise SLAs.
The key is that scaling is gradual. You're not jumping from $2K to $20K overnight. You're adding capacity as revenue grows, which means you're always staying ahead of your costs.
Here's what most ROI calculators miss: time.
The average founder spends 6-9 months building an AI MVP from scratch. That's 6-9 months of runway burned before you have anything to show investors or sell to customers.
With Hal9, you're launching in 30 days. That's 5-8 months of runway saved. If you're burning $15K/month, that's $75K-$120K in preserved capital. That's the difference between making it to Series A or shutting down.
And there's another ROI that matters: focus. When you're not debugging infrastructure at 2am, you're talking to customers. You're iterating on product. You're doing the work that actually moves your business forward.
This model is built for a specific type of founder:
You have a validated idea and you need to build fast.
You want to own your product roadmap but don't want to manage infrastructure.
You're willing to pay for speed and expertise instead of doing everything yourself.
This isn't right for everyone. If you're a technical founder who loves DevOps, you might prefer going direct with AWS. If you're building something that needs custom ML models from scratch, you might need a different kind of partner. If you're pre-idea and still figuring out what to build, you should start with no-code validation.
But if you're in that gap between "I know what I need" and "I have a product," Hal9's pricing is designed to get you there without burning through your runway or spending a year learning Kubernetes.
If you're trying to figure out what your AI product will actually cost to build, let's talk. I can walk you through how our pricing works for your specific use case, what the scope would look like, and what kind of ROI you can expect based on your business model.
Building an AI startup is hard enough. Pricing shouldn't be one of the hard parts.