KobiGPT vs ChatGPT
One general assistant, or several assistants with scope split by department? That is the axis this decision turns on.
TL;DR
Choose KobiGPT when you need specialist assistants with citations from your files, TR/EN support, and predictable Kobi Kredi usage. ChatGPT may win on ecosystem lock-in—validate with your IT checklist.
Quick facts
- Citations
- KobiGPT: document snippets
- Deployment
- SaaS + self-hosted path
- Pricing model
- Published SME tiers + Kobi Kredi
- Languages
- Turkish and English
Why teams choose KobiGPT
- Compare total cost including indexing and support hours.
- Test answer quality on your own PDFs and policies.
- Check data residency and KVKK alignment.
- Use our compliance checklist tool before rollout.
Product facts
- Ücretsiz plan
- 100 doküman · 2 departman · 120 Kobi/ay(PLAN_CONFIG)
- Starter
- 1000 doküman · 5 departman · 1000 Kobi/ay(PLAN_CONFIG)
- Pro
- 12500 doküman · 25 departman · 12500 Kobi/ay(PLAN_CONFIG)
- Doküman yükleme maliyeti
- 0.1 Kobi(packages/types/src/plan-config.ts → TOKEN_COSTS.DOCUMENT_UPLOAD)
- AI mesajı asgari maliyeti
- 0.1 Kobi(packages/types/src/plan-config.ts → KOBI_MIN_TURN)
The structural difference: one assistant vs scoped assistants
ChatGPT is a general-purpose assistant: a single chat surface, broad world knowledge, and optional file attachment. KobiGPT is built from the opposite direction — its starting point is department-scoped assistants whose document scope is defined in advance. The HR assistant sees only HR files; the accounting assistant sees only accounting files.
That distinction is not cosmetic. As scope narrows, retrieved context becomes more precise and the chance of answering from an irrelevant document drops. The trade-off is real: a narrowly scoped assistant is weak on questions requiring general knowledge. KobiGPT does not compete on world knowledge; it competes on answering from your own documents.
Access management: who can see what
In enterprise use the real question is not "how good is the model" but "who can get an answer from which document". In KobiGPT that is handled by role-based access (super_admin, admin, manager, viewer) and per-assistant document scope; if the payroll file is attached to the HR assistant, the general assistant cannot cite it.
General-purpose assistants can be configured this way, but it is not at the centre of the product — it is usually arranged through separate workspaces or external authorisation. When evaluating, test your own scenario: check whether a viewer-level user can obtain an answer from a file they should not reach.
Cost model and a verification note
The pricing models differ structurally. With per-seat subscriptions cost rises linearly with user count; KobiGPT combines a plan quota with usage-based Kobi Kredi metering — a document upload is metered at 0.1 Kobi and an AI message at a minimum of 0.1 Kobi. A small team using it heavily and a large team using it rarely do not produce the same cost.
This page describes category-level structural differences. For current features and pricing, treat the vendor's own page as the source of truth. And do not decide without testing against your own documents — answer quality largely reflects document quality.
FAQ
Which is faster to pilot?
KobiGPT targets same-week pilots with direct document upload.
Can we use both?
Some teams keep Copilot for M365 and KobiGPT for SME docs.
Hallucinations?
RAG reduces but does not eliminate—always verify citations.
Enterprise SLA?
Contact sales for enterprise; cite public pages only.
Can we use both?
Yes, and it is a common arrangement: a general-purpose assistant for research and drafting, KobiGPT for internal document questions.
Which model does KobiGPT use?
The default chat model is from the Gemini family. The product's value sits in the indexing, scoping, and authorisation layer rather than in the model itself.
Comparison
| Feature | KobiGPT | Alternative |
|---|---|---|
| Document RAG | Core | Varies |
| Assistant templates | Yes | Varies |
| SME pricing page | Public | Often sales-led |
| Self-hosted | Optional | Varies |