TypeLLM vs Resistant AI
Side-by-side comparison of features, pricing, and ratings
TypeLLM or Resistant AI: which should you choose?
| Dimension | TypeLLM | Resistant AI |
|---|---|---|
| Category | Python library for structured LLM output | Hosted fraud & document verification SaaS |
| Buyer | Backend/ML engineers self-hosting open LLMs | Banks, fintechs, lenders, insurers, marketplaces |
| Pricing | Contact | Contact sales |
| Deployment | Your own SGLang/vLLM endpoint, GPU required | Vendor-hosted (AWS Marketplace) |
| Core output | Schema-conformant string/int/number/bool/enum values | Fraud verdicts + explainable alerts in <20s / <100ms |
| Setup effort | Client code, schema definitions, GPU infra in-house | Vendor onboarding, explainability and review workflows |
TypeLLM turns text and images into typed, schema-conformant values in one workflow API call — no JSON repair loops.
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Resistant AI detects fake, tampered, and AI-generated documents in any PDF or image, from any country, in under 20 seconds.
Visit WebsiteWho should pick TypeLLM, and who should pick Resistant AI?
- Bank AML or fincrime leadPick: Resistant AI
Layering 80+ explainable transaction models onto an existing rule engine targets APP fraud, mule accounts and synthetic identities without replacing the current stack.
- Merchant onboarding / KYB manager at a marketplacePick: Resistant AI
Document verdicts in under 20 seconds across any country, with generative-AI-forged files flagged, directly cuts manual review queues during seller vetting.
- Loan underwriter or claims unitPick: Resistant AI
Auto-escalate fake documents and fast-track genuine ones so more loans and claims clear without adding headcount.
- Backend engineer extracting fields from receipts and invoicesPick: TypeLLM
JSON Schema fields with plain-English instructions return guaranteed integers, booleans and enums instead of text that needs parsing and validating.
- ML engineer self-hosting open models on SGLangPick: TypeLLM
Per-field thinking flags, dependency-graph ordering and permutation-averaged probabilities fit decision and classification pipelines already running on your own GPUs.
Frequently Asked Questions
Could a fraud team use both together?
Only if that team builds its own model pipeline. Resistant AI is a purchased decision layer over your existing monitoring; TypeLLM is a library you would use inside a self-built extraction or classification service. They do not integrate, and neither vendor lists the other.
Does TypeLLM require a specific model or vendor?
It is built for open autoregressive LLMs you serve yourself, with a Python client against an SGLang HTTP endpoint. Integration entries also name vLLM, Hugging Face Transformers, PyTorch and OpenAI-compatible endpoints.
What changed most recently in TypeLLM?
The 0.2.x line moved thinking from client/run level to a per-field "thinking": True setting, cut the default text_max_tokens to 128, added per-call timeout, cancel and seed, let one client serve many threads, reduced requests for mixed number-and-string fields, and removed typellm.numeric and numeric_cache_dir.
Is any of this self-hostable if compliance demands it?
TypeLLM is inherently self-hosted — it runs against your own endpoint and weights, though its stated not-for includes teams without GPU infrastructure. Resistant AI's not-for list explicitly excludes fraud teams needing on-premise-only or air-gapped deployment.
What is the smallest budget that makes sense?
TypeLLM's not-for list rules out prototypes that just need response_format plus a JSON validator, and Resistant AI's rules out small businesses wanting a free or low-cost document verification tool and operations with fraud volume too low to instrument meaningful ROI. Both vendors price by contact, so neither is a self-serve impulse buy.
Do either offer a hosted UI for non-technical staff?
Resistant AI delivers reviewable, explainable alerts intended for operations and risk teams. TypeLLM explicitly is not for non-technical users wanting a hosted, no-setup UI — it is a Python client for engineers.
What else are TypeLLM and Resistant AI compared with?
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Where can you read more about TypeLLM and Resistant AI?
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