Most mid-market companies solve the internal software problem one of three ways: license a packaged platform, contract an agency or an outsourcing partner, or use a software factory. Each pitch is a version of the same promise, which is working software without building an engineering department. What separates them is not quality, and often not even headline price. It is which risk the model takes off your desk, and which one it leaves there quietly.
This is a comparison of methods rather than vendors. Within each model there are careful providers and careless ones, so the first decision is which model matches the shape of the problem, and the second is who to buy it from.
Two numbers frame the choice. Median SaaS spend now runs at $9,455 per employee, and the average organisation uses 54% of the licences it pays for. On the project side, analysis of 1,471 IT projects found an average cost overrun of 27%, with one in six overrunning by 200% and falling almost 70% behind schedule. Neither model is broken; both price risk in ways the invoice does not show.
The four models
Packaged SaaS
Licensed software configured to fit, with the vendor owning the roadmap, hosting, and upgrades.
Traditional agency
An external firm scopes, bids, and delivers a fixed project over a fixed contract term.
Offshore outsourcing
Contracted engineering capacity, billed by time, directed day to day by the client's own managers.
Software factory
AI-augmented delivery team building custom tools on a weekly subscription, client-owned.
Head-to-head
| Dimension | Packaged SaaS | Traditional agency | Offshore outsourcing | Software factory |
|---|---|---|---|---|
| Time to first working tool | Days to switch on, months once configuration and data migration are counted | 2-4 months typical, after scoping and contracting | Weeks, after sourcing, onboarding, and ramp-up | 5-7 days for the first tool |
| Fit to existing workflow | Bounded by configuration. The business usually adapts to the software | High, if the scope was accurate at signing | High in principle. Depends on how precisely the client can specify | High. Built around the top pain points as they exist today |
| Cost structure | Recurring licence per seat or usage, plus implementation services | Fixed bid or day rate, agreed before the build starts | Hourly or monthly rate per engineer, billed whether or not work ships | $3,000 per active build week, plus a platform fee from deployment |
| Who carries delivery risk | The buyer. Licence cost continues whether or not adoption follows | Shared, and defined by the contract. Overruns are renegotiated | The buyer. Capacity is purchased; outcomes are supervised internally | Discovery is free and billing starts at deployment, not at signing |
| Cost of a change after go-live | Free where configuration allows, otherwise a request on someone else's roadmap | A change order, priced and scheduled separately | Continuing hourly cost, subject to team availability and retained context | A new sprint at a known rate. Bug fixes sit under the platform fee |
| Data and code ownership | Data sits on the vendor's platform. The software is licensed, not owned | Varies by contract and is not always granted by default | Usually assigned to the client, subject to contract terms | Full, from day one. Export, self-deploy, or cancel at any time |
| Best fit | Processes that are standard across most companies in the sector | Large, clearly scoped programmes with fixed requirements | Sustained volume of work with a strong technical manager in-house | Operations teams with several unclear-priority workflow problems |
Each model prices a different risk rather than removing it. SaaS takes away build risk and charges for it in fit and in pricing you do not control; agencies take away uncertainty and charge for it in change orders; outsourcing takes away labour cost and charges for it in supervision; a software factory takes away time and charges for it in the quality of the discovery conversation.
A simple decision framework
Most operations leaders choose under the same constraints: no spare engineering headcount, a backlog of workflow problems nobody has ranked, and pressure to show something inside the quarter. These four questions narrow the field faster than a feature comparison does.
What the factory model actually changes
The AI-augmented part of a software factory compresses build time, not judgment. Deciding which of five pain points to solve first, what the tool should refuse to do, and how it fits an existing workflow still requires someone who understands the business. What changes is how quickly that judgment becomes working software, and how cheaply the model absorbs the next change once priorities shift.
The trade-off is specific and worth stating. Code produced quickly can be maintained badly: an analysis of 623 million code changes between 2023 and 2026 found refactoring down 70% and duplicated code blocks up 81% as AI authorship rose. That is an argument for owning the source from day one and keeping the same team accountable for maintenance, not an argument against the model.
This framework holds regardless of sector or company size. No delivery model removes risk; each one moves it somewhere else, so the question is never which model is best in the abstract, but which relocation your business can absorb.