Build vs Buy
Heed AI Solutions vs Building an In-House AI Team: The Real Math.
A loaded in-house AI team runs 580,000 to 900,000 dollars in Year 1 before any production output ships. Heed delivers production AI in 30 to 90 days for 30,000 to 150,000 dollars Phase 1 plus 5,000 to 25,000 dollars per month ongoing. Both have a place. Most companies do not need a permanent AI org, they need 1 to 3 production deployments.
Year 1, line by line.
The line items the budget memo never shows.
Ramp time. The hidden 6 figures.
The shortest time-to-first-hire for a senior ML engineer in 2026 is roughly 90 days from approved req to start date. Add 90 to 180 days of onboarding, environment setup, and stakeholder alignment before that engineer ships production code on your data. That is half a year of fully loaded salary on the books before the first model goes live. Heed ships a production POC in two to four weeks.
Attrition. The 18-month half-life.
The market for senior ML talent rotates fast. Median tenure in a senior ML role at SMB and lower mid-market companies is roughly 18 months. The sequence then is: hire, ramp, ship one project, lose the engineer to a Series C company, restart. Heed does not take a counter-offer. The team that scopes the work is the team that delivers it, and we are still here in Year 3.
Tooling and infra. Not free.
An in-house team needs vector databases, model API budgets, observability tools, MLOps platforms, and cloud compute. The first-year tool stack for a working AI engineering team runs 40,000 to 100,000 dollars before any production usage. Most budget memos either omit this or wave at a placeholder. The Heed monthly retainer wraps it all into one number.
Scope drift. The thing nobody plans for.
An in-house team is a permanent capability. Permanent capabilities create work to fill themselves. The ML engineer pitches a new model. The data engineer rebuilds a pipeline. Twelve months in, you have spent 700,000 dollars and have one production output and three half-finished initiatives. Heed is scoped to outcomes, not headcount. We finish, you pay, you decide what comes next.
2.1 million dollars vs 540,000 dollars.
A mid-market company commits to either path. Three-year all-in math:
In-house path. Year 1 at 720,000 dollars (mid of range), Year 2 at 650,000 dollars, Year 3 at 700,000 dollars. Three-year total: 2,070,000 dollars. Production output: 2 to 4 deployments depending on attrition. Capability is permanent, but so is the cost.
Heed path. Phase 1 build at 100,000 dollars, monthly retainer at 12,000 dollars per month. Three-year total: 532,000 dollars. Production output: 3 to 5 deployments because the team is fully ramped on day one. Capability is on demand. You can scale up, scale down, or stop.
For most SMB and lower mid-market companies, the second number is the right answer. The first number is what you choose when AI is becoming a core product and you need to own the engineering function long term.
An Encino-based estate and family law firm serving high net worth families.
A multi-attorney West San Fernando Valley estate planning practice considered hiring an in-house technologist plus an AI specialist to build the dashboard their associates needed. The loaded budget came in around 480,000 dollars in Year 1, with a 6 to 9 month ramp before any usable output. They also could not realistically recruit ML talent into a small law firm.
We took the project instead. The build is a unified employee dashboard that integrates Lawcus, RingCentral, Microsoft 365, Anthropic for agentic workflows, OpenAI for image generation, Perplexity for deep research, a secure client portal with e-signatures and document storage, QuickBooks accounting, messaging triage, and agentic workflows for common tasks. All under Cloudflare Zero Trust because their HNW client base means privacy and compliance are paramount.
Heed Phase 1 came in at a fraction of the in-house Year 1 budget, with the system live in 90 days rather than 9 months. The firm gets the capability without owning the engineering org. When the platform changes (new Anthropic models, new Microsoft Graph endpoints, new Lawcus features), we absorb that, not them. For role-specific framing, see Heed AI for CFOs and Heed AI for CEOs.
An honest look at when in-house wins.
In-house wins when:
- You have committed 10 million dollars or more per year to AI investment over a multi-year horizon and need a permanent function to spend it.
- The AI you are building is core IP. Proprietary models trained on data nobody else has, where the model itself is the product or the moat.
- You operate in a regulated environment where data physically cannot leave premises and the build, training, and inference all need to happen behind your own firewall.
- Your roadmap requires 5 plus production AI systems active concurrently with daily iteration on each.
- You are an AI-first company and engineering is part of how investors and customers value you.
Heed wins when:
- You need 1 to 3 production AI deployments rather than a permanent AI capability.
- You want a senior, experienced team on day one, not a 6 to 9 month ramp followed by attrition risk.
- You want predictable economics. Fixed-fee builds, modest monthly retainer, no surprise headcount or tooling line items.
- You want to start small with a proof of concept, validate the value, and only then expand. Hiring a team commits you before the math is proven.
- You want the option to stop, scale down, or change direction without an HR conversation.
Proof of concept first. Always.
Hiring a team is a commitment that compounds. Reversing it is expensive, slow, and visible. Engaging Heed is reversible. We start with a proof of concept that ships in two to four weeks on your data, with a fixed fee at the start. If the POC validates, we move into the production build. If it does not, you stop and you have lost weeks instead of a year.
If you are sizing the build versus the team right now, the Operations Diagnostic maps the highest-leverage AI opportunities in your business and gives you a fixed-fee proposal at the end. Many clients use the diagnostic specifically to make the build versus hire decision with real numbers in front of the board. To see what we actually deliver, see Custom Apps and Dashboards or the case studies at California's largest hillside structural engineering firm and a Southern California fresh produce distributor and exporter.
Sizing build vs hire right now?
Bring the in-house budget to the call. Twenty minutes. We will walk through it line by line and tell you honestly whether the build path or the hire path is the better answer for your specific situation. No pitch deck.