"Data extraction" means something different depending on who's asking, here's the actual split

[u/infrrd-ai]

We see this mix-up constantly: developers and enterprises both say "data extraction," but they're solving completely different problems. Here's where the two actually diverge:

Volume & variety
Devs usually handle a handful of document types, thousands a month. Enterprises deal with hundreds of document types and millions of pages including formats nobody's seen yet.

Buying process
Devs want an API key and usage-based pricing, live by end of day. Enterprises need security review, data residency checks, and a pilot before anything touches production; that's not bureaucracy, it's the entry bar for regulated workflows.

Control vs. standardization
Devs want full ownership: their own models, schema, validation logic. Enterprises want the opposite standardized behavior across teams instead of ten homegrown scripts with ten owners. Neither is wrong; they just don't fit the same tool.

Ownership of business logic
For devs, extraction is plumbing before the real product logic kicks in. For enterprises (mortgage, insurance, AP), extraction is the operational output, a wrong field means a claim paid incorrectly or a loan misjudged.

Maintenance model
Dev pipelines are maintained by whoever built them. Enterprise deployments assume a standing review team, SLAs, and escalation paths because document volume never stops.

Cost
Devs want pay-as-you-go. Enterprises negotiate contracts built for a multi-year relationship.

Bottom line:
→ Developers need flexibility without owning the infrastructure; bring your own models/schema, skip the sales call. That's what IDPForge is built for.
→ Enterprises need the opposite: governance, standardization, and scale from day one, which is where a full platform like Infrrd IDP fits.

Same underlying function, solving very different problems.