In short
Build vs buy software is a choice between control and speed, and AI adds a third option: partnering. Build what sets the business apart, buy common functions, and partner for tailored work without a permanent team. The mistake that locks a business in for years is signing multi-year terms before securing data export and exit rights.
Key takeaways
- For AI, build or buy becomes build, buy or partner, usually decided component by component.
- Surveys show buying is the most common route, but no study proves which path suits a given business.
- Data export, price caps and termination assistance belong in the contract before signing, where lock-in starts.
The older custom software vs off the shelf question pitted a finished product against a codebase. AI blurs that line, because bought tools rely on models the vendor can change. This guide is part of the AI implementation playbook and reflects AI Solution & Technology Advisory work.
What does build vs buy software mean once AI is involved?
For AI, the make or buy decision has three paths: build with an internal team, buy a ready-made product, or partner with an outside team that designs a tailored solution and hands it over. Each path depends differently on models, data and people.
| Path | What it means | Main advantages | Main drawbacks |
|---|---|---|---|
| Build | An internal team designs and runs the system | Workflow fit, control of data and roadmap | Slower start, hiring, permanent maintenance |
| Buy | A vendor product, configured and used as delivered | Faster start, shared cost, vendor support | Less customization, vendor controls roadmap and price |
| Partner | An outside team delivers a tailored solution, then hands it over | Tailored work without a permanent team | Needs ownership terms and an internal owner |
The table sets out the build vs buy software pros and cons. The buy vs build answer can differ inside one program, so a sound build vs buy decision framework puts the build or buy software question to each component.
When does building make sense, and when is buying or partnering better?
Building makes sense when the capability is a competitive edge, the data is sensitive and the team can run it for years. Buying is enough for common functions, such as meeting notes or support triage. Partnering fits tailored work that the business lacks the skills to build alone.
Menlo Ventures' 2025 State of Generative AI in the Enterprise report, a venture investor's survey of 495 U.S. enterprise decision-makers, found 76% of AI use cases were purchased, up from 53% in its 2024 report, which may not measure identically. MIT NANDA's State of AI in Business 2025 (hosted copy) found external partnerships with customized tools reached deployment about 67% of the time, against about one-third for internal builds, a self-reported correlation.
Should we build or buy AI, then? Each leadership team answers from its own data and capacity, and in any AI outsourcing vs in house split, accountability for results stays with the business. For build vs buy AI agents, Gartner's June 2025 release, warning of relabeled products, estimated only about 130 of thousands of agentic AI vendors are real.
What is the full multi-year cost of each path?
AI total cost of ownership covers every cost across the system's life: usage or license fees, integration, data preparation, monitoring, security, staff time, change management and eventual replacement. Each path hides different items, so compare at least three years and include the cost of leaving.
Gartner forecast in a July 2024 press release that 30% or more of generative AI projects would be abandoned after proof of concept by the end of 2025, with escalating costs among its four reasons. It is a forecast, and many estimates break because pilots rarely price monitoring or security review.
What creates AI vendor lock-in and how can a business avoid it?
AI vendor lock-in arises when leaving a provider becomes slow or costly: data in proprietary formats, prompts tied to one model, integrations only the vendor can run and contracts that penalize exit. The mistake is agreeing to multi-year terms first and asking about portability later.
Design covers half of portability. Keep prompts, evaluation sets and configurations in the business's own repository, with a fallback model tested against the same evaluation set.
AI procurement secures the other half, before signing. Common requests, for counsel to review:
- 01Export of all data, prompts and configurations in a documented format.
- 02No training of vendor models on the business's data unless agreed.
- 03Advance notice of model, price and feature changes.
- 04Termination assistance with a stated period and cost.
- 05Caps on renewal increases and auto-renewal length.
- 06Written ownership of custom code and documentation.
Sarvadhi's AI Solution & Technology Advisory covers architecture, model and vendor evaluation, and build vs buy vs partner decisions, so a shortlist can be tested before committing. The seven questions to ask an AI consultant apply to choosing a partner.
Conclusion
Build vs buy software is rarely one permanent decision. For AI it is a set of per-component choices, each with a cost and exit path. The mistake that causes lock-in is usually made at signing.
Run the three-year stress test and secure exit terms. If it is unclear where AI belongs or whether the data is ready, Get an AI Readiness Assessment before choosing a path.
Filed under AI Strategy Consulting