Insights
Build vs buy for AI agents: a decision framework for operators
Buy an artificial intelligence (AI) agent when the job it does is the same at every company and a vendor already does it well; build one when the workflow, the data or the risk is specific to your business. Vantage Systems builds custom agents, and most of the systems we ship are hybrids: bought parts for the commodity layers, custom code for the decisions that make the business money.
Mohamed El Askary, Founder, Vantage Systems
Updated · 9 minute read
What does build versus buy actually mean for AI agents?
It means choosing who owns the logic that sits between a large language model and your business, because almost nobody trains their own model. The options are a finished software product with an agent inside, a platform you configure, or code written for you that calls a model provider directly.
Those three options behave very differently a year in. A finished product is fast to start and hard to bend. A configurable platform bends further but keeps your workflow inside someone else's builder. Custom code bends as far as you need and is yours, but someone has to maintain it.
The market is noisy about which is which. Gartner predicts more than 40 percent of agentic AI projects will be canceled by the end of 2027, and in the same release estimates that only about 130 of the thousands of vendors selling agents offer real agentic capability, calling the rest "agent washing". Check what a product actually decides on its own before you compare prices.
Which factors should decide whether to build or buy?
Six factors settle most decisions: how specific the workflow is, what data the agent must reach, how many transactions it handles, how much risk sits on each decision, how locked in you would be, and what the model calls cost at your volume. Score each honestly and the answer is usually obvious.
| Factor | Points toward buying | Points toward building |
|---|---|---|
| Workflow specificity | The steps match an industry standard, such as password resets or meeting notes | The steps encode how your company wins work, prices risk or serves a key account |
| Data access | The agent only needs data the product already holds | The agent must read and write your ERP (enterprise resource planning) system, CRM (Customer Relationship Management) system or document stores |
| Volume | Hundreds of tasks a month, where per-seat pricing is cheap | Thousands of tasks a day, where per-task fees compound |
| Risk | A wrong answer is an inconvenience someone catches | A wrong answer moves money, misses a deadline or breaks a regulation |
| Vendor lock-in | Leaving would cost little because the agent holds no logic you need | Your prompts, rules and history would be trapped in a proprietary builder |
| Model costs | Bundled into a subscription you can forecast | Large enough that choosing the model per task saves real money |
Workflow specificity and data access
These two carry the most weight. An agent that reads a government tender package and lists what a bid needs is only useful if it reflects how your team decides to bid, and only accurate if it can read every file in the package. A product built for the average buyer will summarize; it will not apply your judgment.
If the agent has to write back into systems of record, integration becomes the project. That work is the same whether you buy or build, so a purchase does not remove it, it just moves it to a vendor's professional services team.
Risk and lock-in
The more a decision costs when it is wrong, the more you need to see and test the logic behind it. Owning the code means you can add a rule the day an auditor asks, run the agent against last year's cases, and prove what changed between versions.
Lock-in is the factor buyers discount most. Gartner now predicts 70 percent of enterprises will abandon agentic AI built by vendor forward-deployed engineering by 2028, pointing to rising costs and customers who cannot change the systems themselves. Whoever builds for you, ask before signing who holds the code, the prompts and the test cases, and how they would move if the relationship ended.
How do model costs change the math at high volume?
They make model choice a design decision rather than a footnote, because the price gap between models is enormous. Anthropic's published application programming interface (API) pricing puts the input price of its largest model at one hundred times that of its smallest, offers batch processing at half price, and charges a tenth or less of the normal input rate when a cached prompt is reused.
A custom agent can route each step to the cheapest model that passes your tests: a small model to classify an incoming email, a large one only for the judgment call. It can batch overnight work and cache the long instructions every call repeats. A packaged product makes those choices for you and prices in a margin on top.
At low volume none of this matters and a subscription is simpler. Once an agent runs thousands of times a day, per-task pricing and model routing are worth modeling before you sign anything. Our pricing page explains how we scope that work in fixed-price phases.
What does a hybrid approach look like in practice?
A hybrid rents the layers that are the same for everyone and builds the layer that makes the agent yours. Telephony, speech recognition, document text extraction and the model itself are commodities with strong vendors. The workflow rules, the prompts, the evaluation set and the record of every decision are not.
Evidence on outcomes supports pairing up rather than going it alone. Coverage of the Massachusetts Institute of Technology report on generative AI in business found that tools bought from specialized vendors or built with partners succeeded about 67 percent of the time, while purely internal builds succeeded about one third as often. The lesson we take is narrower: build what is specific, with people who have shipped it before, and buy the rest.
- Buy: the model, the phone network, optical character recognition for scanned pages, and hosting.
- Build: the decision logic, the integration with your systems of record, the review screens and the audit trail.
- Settle in the contract: who holds the prompts, test cases, configuration and the data the agent produces, and where each is stored.
What should a total cost comparison include?
It should include every cost over three years, not the first invoice. Subscription quotes look cheap next to a build estimate because they leave out integration, per-task overages, and what it costs to leave. Build estimates look expensive because they include work a vendor would also bill you for.
- Setup: discovery, integration with your systems, data cleanup and user training, on either path.
- Run: licenses or seats, model usage by volume, hosting, monitoring and the people who review exceptions.
- Change: what it costs when a rule, a form or a regulation changes, and how long you wait for it.
- Exit: exporting your data and logic, rebuilding integrations, and running two systems during the switch.
Where the money usually goes
In our builds, integration and testing take more effort than the agent's reasoning. That is why we pair agent work with data infrastructure when records are scattered, and why a first phase is scoped around one workflow with a measurable finish line.
When is buying the right call?
Buying is right when the task is generic, the risk is low and a mature product already fits. Meeting transcription, inbox triage for a small team and first-line website chat rarely justify custom code. If a vendor's default workflow matches yours and leaving would cost little, sign the subscription and spend your engineering budget elsewhere.
Building is right when the agent touches the work that differentiates you. A tender agent, a claims reviewer or a loan file checker encodes judgment your competitors do not share; see our custom AI agents for the kinds of workflows where that holds.
Frequently asked questions
Is it cheaper to build or buy an AI agent?
Buying is cheaper at low volume and for generic tasks. Building tends to win when the agent runs thousands of times a day, needs deep integration, or would be expensive to migrate away from later. Compare three-year totals, including integration and exit costs, rather than year-one quotes.
Do we need to train our own model to build a custom agent?
No. Nearly every custom agent calls a commercial model through an API. What you build is the workflow around it: the instructions, the tools it can use, the checks on its output and the connection to your systems.
How do we avoid vendor lock-in with AI agents?
Know where the prompts, test cases, configuration and data live, and get that answer in writing before you sign. On the agents Vantage Systems builds, the client owns the code. Design the agent so the model provider can be swapped behind one interface. Put an exit plan in the contract, including how code and knowledge transfer if the relationship ends.
Can we start with a product and build later?
Yes, and it is often sensible. Use the product to learn which steps matter and where it falls short, but export your data and decision history from day one so a later build starts with real examples instead of guesses.
What is the biggest mistake in build or buy decisions for agents?
Treating it as a software purchase instead of a workflow decision. Teams compare feature lists and miss that the hard part, connecting the agent to messy systems and testing it on real cases, has to be done either way.
How long does a custom agent take to reach production?
A first phase usually targets one workflow and ships working software every week until it is live. The schedule depends far more on data access and integration than on the model. Our AI implementation approach scopes each phase at a fixed price before work starts.
Sources
- Gartner: over 40 percent of agentic AI projects will be canceled by end of 2027 (June 2025)
- Gartner: 70 percent of enterprises will abandon agentic AI built by vendor forward-deployed engineering by 2028 (September 2026)
- Anthropic: Claude plans and API pricing
- Fortune: Massachusetts Institute of Technology report finds 95 percent of generative AI pilots at companies are failing (August 2025)
About the author
Mohamed El Askary, Founder
Mohamed builds the systems Vantage ships, from data pipelines and models to the screens people use. He is a United States Air Force veteran and a Chapman University graduate in data science and business finance.