What is the fastest way to test RAG on our data?

A scoped pilot on one or two high-value document sets, with an evaluation set of real questions your staff actually ask. Platforms and custom pilots can both do this in weeks; what matters is measuring answer accuracy against known-correct answers, not demo impressions.

Is a custom RAG build more expensive than a platform?

Up front, usually yes. Over time, usually no. Platform per-seat fees scale with headcount forever, while a custom build is an engineering cost you own. The crossover commonly arrives within the first couple of years for teams beyond a few dozen users.

Can either approach keep data fully private?

Only architectures you control can guarantee it. A custom build can run entirely on your hardware with open-weight models, so no prompt or document leaves your network. Platform privacy depends on the vendor's terms, hosting, and training policies, which you should read before any pilot.

RAG platform vs custom RAG build: which fits your business data?

TL;DROff-the-shelf RAG platforms win on speed-to-demo; custom RAG builds win on messy real-world data, permission boundaries, deployment control, and long-run cost. The deciding factors are data sensitivity, source complexity, and whether per-seat pricing survives contact with your headcount.

What actually separates the two approaches?

A RAG platform is someone else’s retrieval pipeline with your data poured in: fast to start, opinionated about sources, priced per seat or per query. A custom build is the same architectural pattern (ingestion, indexing, retrieval, generation, citations) engineered around your specific sources, permissions, and deployment constraints. The technology inside is broadly similar; ownership, fit, and economics are not.

Where platforms genuinely win

Speed and packaging. If your content is clean, lives in one or two mainstream systems, has no hard privacy constraint, and your user count is small, a platform demo can become a working tool in days. Platforms also bundle interface polish that custom pilots earn later. When discovery shows this profile, the honest recommendation is a platform plus light integration work, and a good engineering partner should say exactly that.

Where custom builds win

Real business data. Mixed file quality, scanned PDFs, legacy formats, permission boundaries that must be respected per user, questions that span systems, and industries where documents cannot leave the building. Platforms struggle at precisely these seams because they were built for the average case. A custom build also controls the deployment story completely: on-premises with local models, private cloud tenant, or API-backed with retrieval kept on your side. And it removes the platform-risk tax: no per-seat escalation, no feature deprecations, no vendor pricing change deciding your knowledge system’s future.

The cost math nobody puts on the pricing page

Platform economics are rental economics. Per-seat fees look small until multiplied by headcount and years, and query-based pricing punishes success: the more your team uses it, the more it costs. Custom economics are ownership economics: engineering up front, modest run costs after, and the asset appears on your side of the ledger. Neither is universally cheaper; run the multiplication for your team size and a three-year horizon before deciding.

The deciding question

Ask where your risk lives. If the risk is “we never get started,” a platform pilot de-risks fastest. If the risk is “our data is sensitive, our sources are messy, and this becomes load-bearing,” build on foundations you own. Triton Foundry runs both plays and tells you which one discovery supports, in writing, before you commit to either.