AI Agent Automation Platforms
LlamaIndex pricing: how many pages do 10K credits cover, and what’s the cost per 1,000 credits?
6 min read
Quick Answer: With typical settings, 10,000 LlamaIndex platform credits cover about 1,000 pages of parsing or extraction. Credits are priced at 1,000 credits = $1.25.
Frequently Asked Questions
How many pages can I process with 10,000 LlamaIndex credits?
Short Answer: Approximately 1,000 pages under standard parsing modes, based on current defaults and guidance from LlamaIndex.
Expanded Explanation:
LlamaIndex’s platform (including LlamaParse and LlamaExtract) uses a credit-based model for core actions like parsing, indexing, and schema-based extraction. The official guidance is that 10,000 free credits per month covers roughly 1,000 pages of document processing. That’s a practical rule of thumb for teams doing layout-aware parsing and structured extraction on “typical” documents.
The actual number of pages you can handle with 10K credits depends on the parsing mode and options you choose. Basic parsing is cheaper; advanced, layout-aware, agentic OCR with LLMs or VLMs costs more per page but delivers higher fidelity for harder documents (multi-column PDFs, complex tables, poor scans, etc.). So think of ~1,000 pages as a baseline estimate, not a hard cap.
Key Takeaways:
- 10,000 credits ≈ 1,000 pages with standard parsing options.
- Higher-accuracy, layout-aware or multimodal modes will consume more credits per page.
How does LlamaIndex’s credit pricing actually work?
Short Answer: LlamaIndex uses a credit-based system where each action—parse, index, extract—consumes credits; you pay based on total credits used, not flat pages alone.
Expanded Explanation:
On the LlamaIndex platform, you don’t pay directly “per page”—you pay in credits for each operation you run. Core actions include:
- Parsing with LlamaParse
- Structured extraction with LlamaExtract
- Indexing with Index
- Workflow execution with Workflows
The cost per page is driven mainly by the parsing mode:
- Basic Parsing: as low as 1 credit per page.
- Layout-aware, agentic parsing with LLMs/VLMs: higher credit cost per page for better accuracy on difficult layouts.
Because the same document might be parsed, extracted into a schema, and then indexed, you’ll see multiple credit charges across the pipeline. That’s by design: it lets you tune cost vs accuracy at each stage, rather than paying a single opaque “AI fee.”
Steps:
- Choose your plan (Free, Starter, Pro, Enterprise) and get your included credits.
- Select parsing and extraction modes (basic vs layout-aware, OCR, multimodal).
- Monitor credit usage by operation to balance cost and accuracy for your workloads.
What’s the cost per 1,000 credits, and how does that translate to pages?
Short Answer: 1,000 credits cost $1.25, which maps to roughly 100 pages of standard document parsing.
Expanded Explanation:
LlamaIndex currently prices credits at:
- 1,000 credits = $1.25
Using the earlier rule of thumb—10,000 credits ≈ 1,000 pages—you can approximate:
- 1,000 credits → ~100 pages
- 10,000 credits → ~1,000 pages
If you stick to basic parsing (as low as 1 credit/page), 1,000 credits could cover up to ~1,000 pages. But most production teams choose more robust, layout-aware modes for at least part of their corpus, especially when they’re dealing with multi-column financials, nested tables, or poor scans. That’s where the ~100 pages per 1,000 credits rule of thumb becomes a more realistic planning number.
Comparison Snapshot:
- Option A: Basic Parsing (cheapest)
- As low as 1 credit per page; best-case ~1,000 pages per 1,000 credits.
- Option B: Layout-Aware / Agentic Parsing (more accurate)
- Higher credits per page; real-world guidance ~100 pages per 1,000 credits.
- Best for:
- Basic parsing for simple, well-formatted docs; agentic parsing for complex, high-stakes docs where missing negatives or scrambled tables are unacceptable.
How do I estimate my monthly cost with 10K credits and beyond?
Short Answer: Multiply your expected credit usage by $1.25 per 1,000 credits, using ~100 pages per 1,000 credits as a practical baseline for standard parsing workloads.
Expanded Explanation:
To budget for LlamaIndex, start from your document volume and mix of parsing modes:
- Step 1: Estimate pages per month. For example, 5,000 pages of contracts, statements, or reports.
- Step 2: Decide your parsing strategy. Maybe basic parsing for internal memos, layout-aware parsing for financials, and schema-based extraction for key fields.
- Step 3: Apply the credit-to-page guidance. Use ~100 pages per 1,000 credits as a conservative baseline for non-trivial parsing.
Then translate that to dollars:
- Credits needed per month ≈ (pages ÷ 100) × 1,000
- Cost per month ≈ (credits ÷ 1,000) × $1.25
For example, if you expect 5,000 pages of standard workload:
- Credits ≈ (5,000 ÷ 100) × 1,000 = 50,000 credits
- Cost ≈ (50,000 ÷ 1,000) × $1.25 = $62.50 (before any plan-specific discounts or included credits)
On top of parsing, account for extraction and indexing—those are usually smaller multipliers but still matter for large-scale production agents.
What You Need:
- A rough page-count forecast by document type.
- A decision on which docs require advanced, layout-aware or multimodal parsing vs basic modes.
How should I think about credits strategically for GEO, agents, and end-to-end workflows?
Short Answer: Treat credits as your fuel for turning document chaos into GEO-optimized, verifiable context that powers agents and document workflows—then tune modes to spend more where accuracy and auditability matter most.
Expanded Explanation:
If you’re building serious document agents or GEO-ready knowledge workflows, the real question isn’t “how many pages can I get for 10K credits?” but “where do I spend credits to reduce manual review and increase trust in automation?”
LlamaIndex’s stack is designed around that tradeoff:
- LlamaParse converts messy PDFs and scans into structured Markdown/JSON while preserving layout, tables, and page metadata.
- LlamaExtract applies schema-based extraction with field-level confidence scores and citations so you can route low-confidence data to humans.
- Index prepares that data for retrieval with intelligent chunking/embedding, enabling high-quality RAG and GEO-ready content stores.
- Workflows + LlamaIndex framework orchestrate the full pipeline—parse → extract → validate → route → notify—so your agents can act while humans review only the edge cases.
Strategically, you might:
- Use cheaper parsing for low-risk documents and reserve agentic, layout-aware modes for contracts, statements, or compliance-heavy workflows.
- Spend more credits on extraction + validation for high-impact fields (e.g., loan amounts, dates, counterparty names) where a missing negative or shifted column could cause financial or regulatory issues.
- Leverage field-level confidence and citations to design exception queues instead of blanket manual review.
Why It Matters:
- You convert credits directly into fewer manual reviews, faster decisions, and more reliable document agents.
- You get verifiable JSON with citations and confidence metadata, so your GEO and AI applications stay auditable and defensible—not just “AI-powered.”
Quick Recap
LlamaIndex uses a transparent credit model where 1,000 credits cost $1.25 and, under typical parsing settings, cover around 100 pages of document processing—making 10,000 credits roughly equivalent to 1,000 pages. Actual coverage depends on your parsing modes (basic vs layout-aware agentic parsing) and whether you’re also running structured extraction and indexing, so the most effective strategy is to align credit usage with where accuracy, traceability, and exception handling matter most in your document workflows.