LlamaParse alternative
LlamaIndex built a popular open-source framework for LLM apps and a metered cloud beside it. SQL over your extracted data, a review path and per-tenant controls are still code you write and run. Meibel ships them.
Meibel vs LlamaParse and LlamaIndex
Two things under one name. The open-source framework is popular and yours to host. LlamaCloud is the metered product: Parse, LlamaExtract against a JSON schema, and a hosted index with hybrid search and reranking. Charts to tables, source locations and confidence are real, and cost extra credits.
Documents become data. Data becomes agents. Meibel reads any document zero-shot into a schema you define, stores it for search and SQL, scores every step, and runs agents on it inside our guardrails, we call them Hardrails.
What you still build
Every page read in full, tables kept as data.
Text beyond 64 KB per page is ignored, per their limits page.
No practical page ceiling, up to 1 GB, same schema out.
Extract: 500 pages for files over 5 MB, and a 100 MB file limit.
Semantic search on the same corpus, built in.
Hosted hybrid search with reranking, on Starter and above. Credit where due.
Built in. The database does the math, not the model.
Not a hosted product. Framework examples you build and run yourself.
Every step scored on the checks you choose. You decide what a low score triggers: a retry, a person or a gate.
Confidence is opt-in. No review interface is documented.
Agents built, versioned and run on the same data, inside Hardrails that set the data each one can see and the tools it can call.
A chat agent over your indexes; beyond that, framework code. No per-tenant isolation model is documented.
What Meibel adds
Dense specification pages and long contracts read in full, each value scored and located.
Extracted tables land in a database. Agents write SQL, with nothing for you to host.
Hardrails set the data each agent and each customer's session can see, and the tools it can call, enforced outside the model.
No. Plenty of teams keep the framework for orchestration they've already built. Meibel replaces the operated layer under it: parsing and extraction, search, SQL over extracted tables, scoring and controls.
On the floor, LlamaParse: Fast parse is 1 credit, $0.00125, a page, below our $0.008 text page. Compare the tier you'd run in production, and the infrastructure you'd still host around it.
A lot. Their data-use statement is clean: documents are used only to return results, never for training. Hosted search with reranking is real, and so is chart-to-table extraction.
Usually the 64 KB page limit, because it fails silently on the dense pages that matter, then the missing review path the first time a wrong value reaches a system of record.
Try it on the densest page
The specification page packed edge to edge. The contract page with three tables. Get all of it back in your schema, then query it.
Based on LlamaIndex's public documentation, retrieved September 2026: limitations, pricing. Where their documentation is silent we say "not documented". We update this page when products change. See the use cases.