Nearly every SaaS platform now ships a generic AI feature — a chatbot widget, an auto-summarize button, a copilot sidebar. Sometimes that's enough. Other times it's a thin layer that doesn't touch your actual data or workflow, and a custom build is what actually moves the metric you care about.
Start with off-the-shelf tools for generic tasks — there's no reason to build what already exists and works. Move to custom development when the AI needs to touch your specific data, enforce your specific rules, or become a differentiator rather than a convenience feature. The two aren't mutually exclusive: many teams use both, generic tools for commodity tasks and a custom layer for what actually matters to the business.
Upfront, yes — a subscription is far cheaper than a development project. But subscription costs scale with usage and seats indefinitely, while a custom solution has a one-time build cost and lower marginal cost afterward. For high-volume or long-term use cases, custom development can be cheaper over time.
Only as far as the vendor's integrations allow. If your data lives in a standard tool the AI feature already supports, it can work well. If it depends on internal APIs, legacy systems, or business logic specific to you, generic tools typically can't reach it — that's where custom development comes in.
If the task is common — answering FAQs, drafting standard text, summarizing documents — a generic tool is usually good enough. If the task requires understanding your specific data, enforcing your business rules, or taking actions in your systems, generic tools tend to fall short.
Most mature setups combine both — off-the-shelf tools for commodity tasks like meeting summaries or email drafts, and a custom AI layer for the workflows that are specific to the business and drive real value. We help teams figure out where that line should sit.
Not sure if a plugin will do or you need something custom? Tell us about your workflow and we'll give you a straight answer.