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Choosing data and development AI tools — platforms through APIs
This category has the most candidates and the widest price spread. What has to be separated first is not features but form — adopting a platform and attaching an API are entirely different decisions.
Platform vs API Korea conditions Real product list
The short answer
AI tooling here comes in three forms: running AI on top of a data platform, an API you attach to your own app, and tools that do the development itself. Depending on which you need, the candidate lists barely overlap.
For Korean companies this category most often stalls on data terms. Since the subject of analysis is internal data, storage location and training use effectively decide adoption.
Three forms — decide which you need first
| Form | When it fits | Real products |
|---|---|---|
| Data platform | Running analytics, ML and generative AI in one place | Databricks · Snowflake |
| RAG over internal documents | Answering from your own policy and documents | Allganize · Upstage |
| Speech and document APIs | Adding a capability to your own service | Deepgram |
| App generation | Building a prototype quickly | Lovable |
What to check — what matters most in this category
Common misjudgements
The most common is "adopt the platform and everything is solved". Platforms pay off when the data is already organised. Where it is scattered, clean-up cost is added on top — and the clean-up is the bigger number.
The second is underestimating attaching an API yourself. The call is easy; error handling, retries, cost ceilings and logging are the actual work. A prototype and a production system are different scales.
The third is excluding Korean products from the shortlist. Where Korean document handling, domestic storage and local contracting are requirements, Korean products frequently win on conditions.
Frequently asked questions
Does on-premise solve the data problem?
It solves egress, but build and run costs rise sharply and access control and audit logging remain. Check first whether your data sensitivity justifies that cost.
What should we trial first?
Fix one small real task. Having each candidate answer 20 questions from 30–50 internal documents separates them clearly.
A lot of these say "contact us" for pricing.
Usage-based quoting is normal in this category. Prepare your expected data volume and monthly throughput and you will get comparable quotes.
How do we compare Korean and foreign products?
On features alone, foreign products often look stronger. Add domestic storage, Korean processing, local billing and KST support to the table and the conclusion frequently changes.
Product list
Filter the candidates
The AI product directory filters data and development products by pricing type and Korean support, and every product page states data location and tax invoice availability.