TypeLLM vs Klippa
Side-by-side comparison of features, pricing, and ratings
TypeLLM or Klippa: which should you choose?
| Dimension | TypeLLM | Klippa |
|---|---|---|
| What it is | Type-safe generation extension for self-hosted open LLMs | IDP/OCR + verification + spend-management platform (now Doxis) |
| Buyer | Backend/ML engineers running SGLang | Finance, HR, logistics, legal, fintech teams |
| Delivery | Python client against an SGLang HTTP endpoint (SKILL.md agent setup) | Web interface, APIs, SDKs, HITL workflow builder |
| Core capability | JSON Schema fields in, guaranteed typed values out | OCR extract, classify, anonymize, verify docs and identities |
| Integrations | SGLang, vLLM, Hugging Face Transformers, PyTorch, OpenAI-compatible endpoints | Exact, AFAS, NetSuite, Microsoft Dynamics, SAP, Visma, Odoo… |
| Pricing | Contact (self-hosted; you pay your own GPU) | Contact sales |
TypeLLM turns text and images into typed, schema-conformant values in one workflow API call — no JSON repair loops.
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Klippa (now Doxis) automates invoice and document processing with OCR, identity verification and Peppol e-invoicing.
Visit WebsiteWho should pick TypeLLM, and who should pick Klippa?
- Finance team automating APPick: Klippa
Invoice extraction, validation workflows and push into Exact, AFAS, NetSuite, Twinfield or SAP is exactly the documented core focus.
- HR onboarding at scalePick: Klippa
Identity verification with liveness, NFC and selfie checks plus proof-of-identity output is listed for employee onboarding.
- Fintech KYC/AML opsPick: Klippa
Fraud detection, document verification API and identity verification SDK are built for KYC/AML pipelines.
- Backend engineer self-hosting SGLangPick: TypeLLM
Define schema fields, get guaranteed typed values, and use depends_on ordering without parsing free text.
- ML engineer building a decision pipelinePick: TypeLLM
Enum fields with permutation-invariant decision probabilities and parallel field generation fit classification and decision work on open models.
Frequently Asked Questions
Can I use TypeLLM to extract invoice data the way Klippa does?
TypeLLM can constrain a field to an enum, integer or boolean, but it provides no OCR pipeline, no document classification, no fraud or identity checks, no HITL workflow builder and no ERP connectors — the parts that make Klippa a document-processing product rather than a generation constraint.
Can Klippa replace TypeLLM in my model stack?
No. Klippa consumes documents through APIs, SDKs and a web interface; it does not sit inside an SGLang server constraining autoregressive token generation, and its integrations list is accounting/ERP software, not vLLM or PyTorch.
Does TypeLLM have a no-setup hosted UI?
Its own not-for list says non-technical users wanting a hosted, no-setup UI are not the audience, and it is explicitly designed for open LLMs you serve yourself.
What changed in TypeLLM 0.2.2?
Thinking became a per-field setting rather than a client- or run-level argument, and the default text_max_tokens dropped from 512 to 128 — check your per-field thinking flags if you upgrade from 0.1.x.
Is Klippa only OCR?
No. The Doxis AI.dp suite also covers anonymization/redaction, document and identity verification, a prompt builder and a SpendControl expense/corporate-card product, with recent content focused on Peppol e-invoicing conversion and automated invoice workflows.
Which one needs GPU infrastructure?
TypeLLM, because it targets open autoregressive models on SGLang. Klippa is a commercial platform you access through its interface, APIs and SDKs.
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