You’ve got budget approval to build an AI team. The exec team is excited. Your CFO just signed off on $500K for Year 1. Seems like plenty, right?
That $500K budget covers maybe 2-3 people if you’re lucky. And that’s before infrastructure costs, recruiting fees, training, and all the hidden expenses that pop up once you actually start hiring.
Let’s walk through what the real cost of building an AI team looks like in 2025 in real numbers.
The Cost of Building an AI Team: Where Your Budget Actually Goes
We’ll start with salaries, because that’s where the sticker shock begins.
Your foundation hire is a Machine Learning Engineer. A senior ML engineer commands $140K-$180K in base salary based on November 2025 data. But base salary is just the beginning. About 60% of ML roles require signing bonuses in the $15K-$30K range just to close candidates. Benefits and overhead add another 30% of salary. And if you’re using recruiters to find talent (and you probably will be), that’s 20-25% of first-year comp in fees.
Do the math and a senior ML engineer actually costs you $260K-$355K in Year 1. That’s one person.
You’ll also need a Data Engineer, because your expensive ML engineers shouldn’t be wasting time building data pipelines. Senior data engineers run $125K-$155K base, which translates to $210K-$280K all-in for Year 1. This person makes sure your ML team has clean, accessible data to work with. Without them, your ML engineers spend half their time on infrastructure instead of models.
If you’re wondering about the complete breakdown of when to hire your first data engineer, I’ve written a detailed guide on that decision framework.
Depending on your situation, you might also need an ML Infrastructure Engineer to handle deployment, monitoring, and keeping models running in production. This role is optional for Year 1, but it becomes critical by Year 2 if your ML engineers are spending more than 40% of their time on ops work. Budget $190K-$275K if you hire one.
An AI Product Manager starts making sense once you have three or more ML engineers, or when multiple teams across the company are clamoring for AI features. If your CTO can play this role or your team is still small, skip it for now. But if you need one, expect $195K-$295K.
Three Real-World Budget Scenarios
Now let’s look at three realistic scenarios:
The first is what I call the Lean Start, which runs $475K-$570K. You’re hiring one senior ML engineer and one senior data engineer. This is your minimum viable AI team, and it’s enough to prove out one use case and build foundation infrastructure. Here’s the sobering reality though: even this “lean” approach pushes against a $500K budget. If your CFO thinks half a million dollars buys a team, they’re in for a surprise.
The Standard Build runs $730K-$900K. Now you’ve got one senior ML engineer, one mid-level ML engineer, and one senior data engineer. This configuration works for Series B+ startups or established companies with multiple AI initiatives on the roadmap. You can run two or three projects in parallel and start building real organizational capability.
The Full Build hits $1.5M-$1.8M. We’re talking a staff ML engineer leading the technical direction, two senior ML engineers doing the heavy lifting, a senior data engineer, an ML infrastructure engineer, and an AI product manager. This is for well-funded companies where AI is core to product strategy and there’s competitive pressure to move fast.
For more context on building a data team from scratch, including hiring structure and team dynamics, I’ve documented that complete process.
Infrastructure: The Costs That Sneak Up on You
Salaries are obvious. Infrastructure costs are where teams get blindsided.
Cloud compute for training and inference runs $36K-$180K annually, and that range is wide because scale matters enormously. GPU instances on AWS or GCP cost $2-$5 per hour, and training jobs typically take 8-48 hours each. When your team is actively developing models, monthly compute bills of $5K-$12K are normal. Once you’re serving predictions in production, inference costs scale with usage, and they can spike fast if you’re not watching.
Then there’s the ML platform stack. Experiment tracking tools like Weights & Biases or MLflow, feature stores like Tecton or Feast, MLOps platforms like Databricks or Snowflake, this layer adds $24K-$96K per year depending on what you need and how sophisticated you want to get.
Finally, if you’re building on foundation models rather than training everything from scratch, external APIs like OpenAI or Anthropic run $6K-$60K annually for typical usage. This can actually save you money compared to training custom models; more on that later.
The Hidden Costs Nobody Warns You About
Beyond salaries and infrastructure, there’s a whole category of expenses that catch first-time AI team builders off guard.
Recruiting is expensive. Agency fees run 20-25% of first-year compensation, so if you’re making two or three hires through recruiters, that’s $40K-$100K right there. Internal recruiting costs add up too; LinkedIn Recruiter subscriptions, job board fees, and the time your existing engineers spend interviewing. Each hire takes 40-60 hours of engineering interview time, which has real opportunity cost. Total recruiting spend for a small team: $40K-$120K.
For insights on what actually matters in hiring, check out my guide on what hiring managers look for in data engineers. Many of these principles apply to ML engineers too.
Training and development matters if you want to retain talent. Conferences like NeurIPS and ICML cost $3K-$5K per person including travel. Online courses, certifications, and books all add up to $12K-$35K annually for a team of three to five people. Skip this and watch your best engineers leave for companies that invest in their growth.
Onboarding productivity loss is the cost nobody talks about. New hires operate at about 25% productivity in their first month, 50% in month two, and 75% in month three. They don’t hit full speed until month four. For a senior engineer at $155K, that ramp-up period costs roughly $29K in lost productivity per hire. Add mentoring time from your existing team, and three hires cost you $110K-$125K in ramping expenses. This isn’t wasted money, it’s the cost of building a real team. But you need to budget for it.
Failed projects are inevitable. In my experience, 30-40% of initial ML projects get killed before reaching production. Another 20-30% ship but don’t deliver meaningful business value. Only about a third of Year 1 projects actually hit their ROI targets. This isn’t pessimism, it’s realistic planning. Budget two to three months of team time ($40K-$80K) for projects that don’t pan out. The learning from those failures is valuable, but pretending every project will succeed is a recipe for budget blowouts.
If you’re evaluating whether AI is even worth it for your situation, I’ve written about calculating AI ROI with a practical framework.
Before You Calculate the Cost of Building an AI Team: Do You Actually Need One?
Here’s a question worth asking before you hire anyone: Do you actually need an in-house AI team?
If your use case is standard NLP (sentiment analysis, classification, summarization), image recognition, basic recommendations, or chatbots, pre-built APIs are 10-100x cheaper than building in-house. OpenAI or Anthropic costs $6K-$60K per year for typical usage. A small internal team costs $520K-$880K. The math isn’t close.
Build in-house when your use case genuinely requires it: proprietary data that can’t leave your infrastructure, custom models that create competitive differentiation, extremely high-volume inference where API costs would be prohibitive, or deep integration with existing systems that off-the-shelf solutions can’t handle.
The break-even point typically hits when API costs would exceed $80K-$120K annually, or when AI is so core to your product that you need to own the capability. If neither of those applies, start with APIs and revisit the build decision in a year.
The Bottom Line: Cost of Building an AI Team in 2025
The real cost of building an AI team in 2025: a minimum of $520K-$700K for Year 1, and that’s for a lean two-to-three person team. A standard three-to-five person team runs $950K-$1.3M. A full-scale AI organization costs $1.5M-$2.2M or more.
These numbers are based on November 2025 salary data from Glassdoor, Indeed, PayScale, and Built In. They reflect real market rates, not inflated Silicon Valley fantasies.
The companies that succeed with AI don’t spend the most money. They spend smartly, prove value quickly, and scale based on results. That’s the playbook.