KobiGPT vs generic RAG stacks

A prototype takes a week: an embedding model, a vector database, an LLM API, and a simple interface. At that stage "we can build this ourselves" looks reasonable — and for a single-person use case, it often genuinely is.

Moving to production lengthens the list: document parsing (PDF, DOCX, XLSX, scanned content), chunking strategy, embedding queue and retry logic, vector collection management, multi-tenant isolation, role-based authorisation, usage metering and quotas, audit logging, error tracking, multilingual interface. Each item is small on its own; together they add up to months of engineering.

The real cost comes after the initial build. The embedding model version changes and the whole archive needs re-indexing. The LLM provider changes its API. The queue backs up and document statuses hang. A new file format arrives. A customer asks about data isolation and wants evidence.

None of this is product work; all of it is maintenance load. At SME scale there is no dedicated platform team, so the load usually falls on one person — and when that person leaves, the system is orphaned. In a build-versus-buy decision, that risk can weigh more than licence cost.

The honest answer: sometimes building your own is correct. If data can never leave your premises under any circumstance, if your workflow is more specific than standard products cover, or if RAG is the core function of your own product, keeping control makes sense.

If the need stems from data residency, there is a third option: KobiGPT supports self-hosted deployment, running on your own infrastructure. To decide by measurement, you can pilot on the Ücretsiz plan plan with 100 documents and compare real cost against your own stack estimate.