anyformat vs LlamaIndex (2026)

LlamaIndex sells a code-first document agent platform: an MIT-licensed framework plus LlamaCloud, where LlamaParse, LlamaExtract and LlamaAgents are commercial services. anyformat sells the operational layer around extraction. anyformat scored 78.1% on a 2026 parse benchmark of 1,000+ real documents, is ISO 27001 certified and EU-native; LlamaIndex is US-governed.

What LlamaIndex is: the framework, LlamaCloud and LlamaParse

LlamaIndex is three different things sold under one name, and the distinction decides what you are actually buying. llama_index is an open-source Python framework published under the MIT License, copyright Jerry Liu, with more than 300 integration packages for LLM, embedding and vector store providers. LlamaCloud is the commercial managed platform, and LlamaParse, LlamaExtract, LlamaSplit and LlamaAgents are closed commercial services served through it — LlamaParse is LlamaIndex's own parsing API, not a community project layered on top of the framework. The company is LlamaIndex Inc., incorporated in April 2023 and headquartered in San Francisco, founded by Jerry Liu (CEO) and Simon Suo (CTO), with $27.5M in disclosed funding: an $8.5M seed led by Greylock in June 2023 and a $19M Series A led by Norwest Venture Partners in March 2025, plus strategic minority investments from Databricks and KPMG in May 2025. For the parsing-level comparison — output formats, table geometry, per-page cost — see anyformat vs LlamaParse; this page compares the platforms.

LlamaExtract and custom schemas

LlamaExtract is genuinely zero-shot: you supply a JSON or Pydantic schema and it extracts against it with no labelled training data, and its documentation is explicit that field descriptions, not examples, are what steer the model (LlamaIndex docs, 2026). It offers three quality tiers (cost_effective, agentic, agentic_plus), three extraction targets (per_doc, per_page, per_table_row), schemas up to 3,200 fields on the highest tier, and two options that matter for audit: cite_sources, which traces a value back to its origin in the document, and confidence_scores, which returns per-field confidence signals. Schema-driven extraction is not a gap in either product. anyformat extracts against a user-defined schema in zero-shot across 100+ formats without templates, declares document-level and table-level fields in one schema and returns both in a single call, and scored 78.1% on a combined parse score across 1,000+ real documents spanning 30+ document types (anyformat benchmark, 2026).

Code-first orchestration versus a no-code platform

This is the real decision, and it is not about extraction quality. LlamaIndex's orchestration is code: Agent Workflows is an event-driven Python framework with branching, parallelism, human-in-the-loop hooks, durability and observability, and LlamaAgents — in open preview since November 2025 — packages it with a llamactl CLI that pulls template repositories of Python files you modify locally (LlamaIndex docs, 2026). LlamaCloud does ship an Agent Builder, a natural-language interface where you describe a workflow and an AI coding agent generates it, but what you get is a real Python project in your GitHub repository, so changing the flow later means changing code and redeploying. anyformat includes Studio, a no-code workflow canvas with branching, conditions, splitting, routing, classification, extraction operators, cross-referencing against internal data sources and review steps placed wherever the process needs them, and an operations user changes the flow without a deploy. The question in an evaluation is not whether a builder exists, but who is able to change extraction logic on a Tuesday afternoon.

Deployment and jurisdiction

LlamaCloud runs as SaaS in two regions — North America on AWS us-east-1 at cloud.llamaindex.ai and Europe on AWS eu-central-1 at cloud.eu.llamaindex.ai — with data stored in the region it was uploaded to, and its own documentation notes that NA is the primary region where new features ship first, so the EU region may receive them later (LlamaIndex docs, 2026). Self-hosting and bring-your-own-cloud are available: the full platform deploys on Kubernetes via Helm charts on AWS, Azure or GCP inside your own VPC, but it is an Enterprise-only feature that requires a license key, and it is not air-gapped — LlamaIndex documents that all LLM calls go directly from your cluster to providers using your own API keys. LlamaIndex Inc. is a US company, so an EU endpoint answers data residency without changing the jurisdiction of the vendor operating the software. anyformat is EU-native, a European company on European infrastructure, and deploys in cloud, private cloud or air-gapped on-premise environments.

Compliance

LlamaIndex publishes SOC 2 Type II, GDPR compliance and HIPAA on its enterprise pages, with custom BAAs available to enterprise customers. Its trust center at security.llamaindex.ai is JavaScript-rendered and returns no readable content to automated fetchers, and ISO 27001 — the certification European procurement teams most often treat as a hard requirement — is not publicly listed on the pages that are readable. Absence from a public trust center is not proof that the controls are missing, but procurement generally treats an unlisted certification as an absent one. anyformat is ISO 27001 certified with certification scope covering the document pipeline end to end, and GDPR compliance is an architectural constraint rather than a contractual annex.

Zero retention and data handling

Both support it, with different defaults. LlamaParse caches parsed documents for 48 hours before permanently deleting them, and setting do_not_cache=True disables caching for a given request (LlamaIndex docs, 2026). anyformat runs zero-retention processing as an account-level mode, so source files and extracted output are not persisted past the processing window. The practical difference is whether the safe setting is the default posture or a parameter a developer has to remember on every call.

Confidence and review

LlamaExtract returns per-field confidence signals and source citations when confidence_scores and cite_sources are enabled, so uncertain values can be surfaced to a caller. What LlamaIndex does not publish is a calibration figure — whether a reported 0.8 corresponds to being right about 80% of the time — and an uncalibrated score cannot carry a routing threshold, because no point on the scale is known to be safe to auto-approve. anyformat measures calibration and publishes it: 99.1% calibration accuracy with an Adaptive ECE of 0.009 (anyformat benchmark, 2026). That is what makes straight-through processing defensible to an auditor: high-confidence fields flow through, uncertain ones route to a reviewer inside the same workflow, and every value carries a visual citation pointing at its exact position on the page. In production, L'Oréal reports 99% extraction accuracy and a 60% reduction in processing time across 1,500+ monthly invoices.

Pricing

LlamaCloud bills in credits at 1,000 credits for $1.25, with a Free plan at $0 including 10,000 credits, Starter at $50 per month for 40,000 credits, Pro at $500 per month for 400,000 credits, and Enterprise custom-priced with volume discounts, 5x higher rate limits, SSO and self-hosted deployment (LlamaIndex pricing, 2026). anyformat bills in credits charged per page and per operator, where 10 credits cost €0.01: Parse is 25 credits per page (€0.025, published as $25 per 1,000 pages), Extract 35 credits per page, Classify 10, Split 25, and Validate 25 credits per rule (anyformat pricing, 2026). Pay As You Go is free to start with a one-time grant of 50,000 credits, Business is €499 per month (€399 billed annually) for 500,000 credits, and Enterprise is custom-priced with VPC or on-premise deployment and zero data retention. On both platforms the per-page fee is the smaller number; the engineering cost of the pipeline around it is the larger one.

Evaluation and monitoring

The open-source framework ships evaluation modules — correctness, faithfulness, context relevancy, answer relevancy, and retrieval metrics such as hit-rate and MRR — but they are libraries a developer imports and runs, oriented at RAG pipeline quality rather than field-level extraction accuracy against a maintained ground truth. LlamaCloud does not publicly document a ground-truth dataset manager, a regression suite or a production accuracy monitor as product surface. Measuring whether a schema change improved extraction, and noticing when live accuracy drifts away from your benchmark, is therefore work your team builds and keeps running.

What anyformat ships that LlamaIndex leaves to your team

Evaluations, available today. Every anyformat Extract and Classify workflow has a Health tab. You build a dataset with verified ground truth, either by promoting a document the workflow already processed and confirming its values in the review interface, or by uploading labelled documents with their expected JSON. Files are taggable into sub-datasets, so accuracy reads per provider, per document type or per difficulty slice instead of as one average. An evaluation re-extracts every in-scope document against a chosen workflow version and scores it field by field, with a result-versus-expected view on each failure; runs are numbered and immutable, so the effect of a change is measured rather than assumed. Health Overview then places the dataset benchmark next to live production accuracy, confidence and through rate, and reports the gap between them (Evals).

Optimizer, announced and not yet available. anyformat has announced Optimizer, a workflow that tunes itself against its own dataset using your evaluations as the target. It is on the roadmap, not in the product today (Evals).

The pipeline you do not have to build. LlamaCloud returns parsed content and schema-validated fields. Production also needs classification and routing, validation rules, retry handling, a review interface, accuracy monitoring and re-tuning whenever a layout or an underlying model changes. With LlamaIndex that layer is Python you own: Agent Workflows gives you the primitives, and the rest is your repository, your deployment and your on-call rota. anyformat ships it as product surface — no-code Studio workflows, per-field calibrated confidence with review routing, visual citations for audit, and the evaluation loop above. On tables running to 50 pages and roughly 2,400 rows anyformat held ~99% row recovery, and it extracted 94% of complex invoices with every field and line item correct (anyformat benchmark, 2026). Setting up a production workflow with the Annie assistant takes around 5 minutes against roughly 4 hours of manual configuration (anyformat internal benchmark, 2026).

When to choose LlamaIndex

Choose LlamaIndex when you are an engineering team building a custom RAG or agent application and you want a framework rather than a platform. The open-source core is MIT-licensed with more than 300 integration packages, so you can swap LLM, embedding and vector store providers freely and keep orchestration in your own repository; the ecosystem reach, connector breadth and developer mindshare are among the largest in the category, and LlamaCloud's free tier plus a $50 Starter plan makes it inexpensive to begin. If retrieval and indexing matter as much as extraction, if you want to own every step of the pipeline in code, or if the requirement is RAG ingestion rather than document operations, that control is the point. For the narrower question of parsing quality and per-page cost, see anyformat vs LlamaParse.

When to choose anyformat

Choose anyformat when the document pipeline has to run as a supervised business process rather than an application your team maintains: when EU jurisdiction and ISO 27001 are hard requirements rather than preferences, when calibrated confidence and visual citations are what justify straight-through processing to an auditor, when operations rather than engineering should own schemas and workflow logic, or when workflow changes need to be measured against a ground-truth dataset before they reach production. Teams moving from a framework to a platform generally cite the same three costs: the orchestration code they had to write, the review and monitoring layer nobody budgeted for, and US jurisdiction. Get started at anyformat.ai.