LlamaParse alternative with JSON, Markdown and CSV in one call
PdfParse suits backend and AI teams better than LlamaParse when they want JSON, Markdown and CSV from one managed PDF parsing call, page allowances without mode multipliers, and extraction schemas with field confidence. It's a practical option for engineers whose difficult PDFs require agentic parsing and whose applications also need tables or structured fields. Stay with LlamaParse if you're deep in LlamaIndex, want its monthly credit allowance for a hobby project, or prioritize the lower page prices of its Fast and Cost-effective tiers.
Parse your own PDF before you switch
Start with a document that causes trouble in your current pipeline. Inspect the JSON, Markdown and CSV together, then compare the result with what your application needs. The browser preview helps you inspect page one before choosing an API plan; use your own files for the broader evaluation.
What one call returns
Page one rendered with every block outlined, next to the JSON your code receives.
- Reading text layer
- Detecting layout
- Extracting tables
- Building JSON
Headings, paragraphs, tables and totals detected
Portsmouth, NH 03801
| Description | Qty | Unit | Amount |
|---|---|---|---|
| Oak countertop, 2.4 m | 2 | 640.00 | 1,280.00 |
| Cabinet doors, shaker | 12 | 85.00 | 1,020.00 |
| On site fitting | 1 | 450.00 | 450.00 |
Quote the invoice number with your transfer.
Goods remain ours until paid in full.
Pages 2 and later are parsed after you create an account.
{ "document": { "pages": 2, "parsed_pages": 1, "language": "en" }, "pages": [ { "number": 1, "blocks": [ { "type": "heading", "text": "Invoice", "bbox": [48, 52, 132, 84] }, { "type": "paragraph", "text": "Harbor Street Joinery, 14 Dock Lane, Portsmouth" }, { "type": "key_value", "pairs": { "Invoice no.": "INV-2026-0418", "Issued": "Oct 14, 2026", "Due": "Nov 13, 2026" } }, { "type": "table", "columns": ["Description", "Qty", "Unit", "Amount"], "rows": [ ["Oak countertop, 2.4 m", 2, 640.00, 1280.00], ["Cabinet doors, shaker", 12, 85.00, 1020.00], ["On site fitting", 1, 450.00, 450.00] ] }, { "type": "total", "label": "Total due", "value": 2983.75, "currency": "USD", "confidence": 0.98 } ] } ], "fields": { "invoice_number": { "value": "INV-2026-0418", "confidence": 0.99, "page": 1 }, "total": { "value": 2983.75, "confidence": 0.98, "page": 1 }, "due_date": { "value": "2026-11-13", "confidence": 0.97, "page": 1 } } }
Downloads from the demo include a watermark field and cover page one only.
Page one is parsed in your browser, so your file never reaches our servers.
One demo per day from this network. Create an account to parse more pages.
$ curl https://api.pdfparse.ai/v1/parse \ -H "Authorization: Bearer $PDFPARSE_API_KEY" \ -F file[email protected] \ -F outputs=json,markdown,csv
LlamaParse and PdfParse side by side
Compare the parsing tier you actually use with the PdfParse plan that covers your volume. A credit rate and a subscription divided by its page allowance measure different things, so utilization matters. Both products provide managed cloud parsing. For LlamaParse features not covered here, check its current docs before making a purchasing decision.
| What matters | LlamaParse | PdfParse |
|---|---|---|
| Pricing model | Credits at $1.25 per 1,000; v2 Fast uses 1 per page, Cost-effective 3, Agentic 10, Agentic Plus 45 | Subscription page allowance; one page is one page on every plan, without mode multipliers |
| Price per page | Fast $0.00125; Cost-effective $0.00375; Agentic $0.0125; Agentic Plus $0.05625 | Growth billed yearly is about $0.0106 at full utilization, calculated as $74 / 7,000 pages |
| Free usage | Free plan at $0 includes 10,000 credits per month | Page one browser demo without a card; no free subscription plan |
| Output formats | Markdown, text and JSON | JSON, Markdown and CSV in one call |
| Custom field schemas | Check current LlamaIndex docs | Growth up to 10; Scale unlimited; confidence and source page per field |
| Chunks for RAG | Check current LlamaIndex docs | Growth and above; heading path, pages and bounding box |
| Webhooks and batch | Check current LlamaIndex docs | Growth and above; signed webhooks and batch jobs up to 500 files |
| Data retention | Check current LlamaIndex docs | Starter 7 days; Growth up to 30 days; Scale and Enterprise offer zero retention mode |
| Ecosystem fit | Built around the LlamaIndex ecosystem | Python and Node SDKs; outputs usable with LangChain, LlamaIndex or plain Python |
| Best for | LlamaIndex projects and lower cost Fast or Cost-effective parsing | PDF pipelines needing three output formats, field schemas and page based billing |
LlamaParse and LlamaIndex are trademarks of their owner, and LlamaParse pricing was checked against LlamaIndex's public docs in October 2026.
LlamaParse's paid plans include Starter at $50 with 40,000 credits and Pro at $500 with 400,000 credits; Enterprise pricing is custom. Pay-as-you-go overage costs $1.25 per 1,000 credits. Its Fast and Cost-effective tiers are cheaper per page than PdfParse, and LlamaParse is the natural choice inside LlamaIndex.
PdfParse's Growth yearly rate is lower than LlamaParse's Agentic and Agentic Plus rates when you use the full allowance. That comparison does not guarantee a smaller bill at low volume or equivalent results. Evaluate the same files and include unused subscription capacity in your cost calculation.
What makes switching worth evaluating
A parser change earns its place when it simplifies an actual document pipeline. These are reasons to test PdfParse against your current setup, rather than assume that a different parser handles every document better.
- Predictable page accounting when every page needs agentic parsing. PdfParse counts the page once without a mode multiplier, so your estimate starts with page volume instead of a mix of tier credits.
- Three formats in one response. Keep JSON for application logic, Markdown for model context and CSV for tables without making separate parse calls for each output.
- Field schemas with confidence and a source page. On Growth and above, map extracted values into your application's fields and use the confidence values to guide review.
- Signed webhooks and batch processing. Growth and above support job delivery to your endpoint and batches of up to 500 files for document backlogs.
- Retention controls. Choose Growth retention up to 30 days or zero retention mode on Scale and Enterprise. Files are never used to train models, and Enterprise offers an EU or US processing region.
- Framework choice. Feed the outputs into LangChain, LlamaIndex or plain Python code. Your parser choice doesn't require moving the rest of your application to a particular framework.
How to switch from LlamaParse in an afternoon
-
1
Replace the parsing call with POST /v1/parse through the Python SDK. Keep the change behind your existing document ingestion interface so the rest of the application has a stable entry point.
-
2
Map result.markdown to the text your retrieval or prompt code consumes. Compare document structure before changing your downstream processing.
-
3
Move field extraction prompts into a schema created with POST /v1/schemas on Growth or above. Map each returned value, confidence and source page into your application.
-
4
Point your job completion handler at a PdfParse webhook on Growth or above. Adapt the payload mapping and signature verification before enabling deliveries.
-
5
Run a side by side test on 50 of your own hard files. Review missing fields, table structure, Markdown and billed pages before routing your production workload through the new call.
The same parse call in Python
The Python SDK uses the same client and parse method as the SDK guide. Request all three outputs together and pass the Markdown to your existing text processing code. Read the API key from your environment rather than putting it in source code.
import os
from pdfparse_ai import PdfParse
client = PdfParse(api_key=os.environ["PDFPARSE_API_KEY"])
result = client.parse("document.pdf", outputs=["json", "markdown", "csv"])
print(result.markdown)When LlamaParse is the better pick
- You're deep in LlamaIndex and its parsing integration already fits your application. A lower integration burden can matter more than changing the output contract.
- You need the lowest per page price for simple digital PDFs. LlamaParse Fast and Cost-effective both cost less per page than PdfParse.
- You need many non-PDF formats already covered by your LlamaParse workflow. Check that coverage before replacing a working integration with a PDF focused service.
- You want the Free plan's 10,000 monthly credits for a hobby project. PdfParse's page one browser demo is a preview, not a recurring API allowance.
Choose a plan for your monthly PDF volume
Choose by page volume and required controls. Every plan returns JSON, Markdown and CSV; custom schemas, RAG chunks, webhooks and batch jobs start on Growth. Yearly billing prices below are monthly equivalents of an annual subscription.
| Plan | Yearly billing | Monthly billing | Pages per month | Fit |
|---|---|---|---|---|
| Starter | $24 per month | $49 per month | 2,000 | A smaller PDF workload needing the three standard outputs |
| Growth | $74 per month | $149 per month | 7,000 | A document pipeline needing schemas, RAG chunks and webhooks |
| Scale | $249 per month | $499 per month | 24,000 | Higher PDF volume with unlimited schemas and zero retention mode |
| Enterprise | $749 per month | $1,499 per month | 72,000 | Larger workloads requiring an EU or US processing region |
Compare allowances and billing terms on PDF extraction API pricing. Calculate effective page cost using the pages you expect to process, not just the plan's maximum allowance.
- Starter 2,000 pages, 1 API key, 1 seat $49 per month $24 per month, $288 billed yearly
- Growth 7,000 pages, 5 API keys, 3 seats $149 per month $74 per month, $888 billed yearly
- Scale 24,000 pages, 20 API keys, 10 seats $499 per month $249 per month, $2,988 billed yearly
- Enterprise 72,000 pages, unlimited API keys with per key limits, 25 seats $1,499 per month $749 per month, $8,988 billed yearly
FAQ
What is the best alternative to LlamaParse
PdfParse is a strong alternative for managed PDF parsing when you need JSON, Markdown and CSV together with page based billing. Growth adds custom schemas, field confidence and RAG chunks. LlamaParse remains the natural choice inside LlamaIndex and costs less per page on its Fast and Cost-effective tiers.
Is LlamaParse free
LlamaParse has a Free plan with 10,000 credits per month. Its v2 tiers consume different credits per page, so that allowance does not mean 10,000 pages in every mode. PdfParse offers a page one browser demo without a card, while ongoing API usage requires a subscription.
How much does LlamaParse cost per page
LlamaParse costs about $0.00125 per page on Fast, $0.00375 on Cost-effective, $0.0125 on Agentic and $0.05625 on Agentic Plus. Those figures use $1.25 per 1,000 credits and each tier's credit consumption. Compare the tier your documents need rather than treating all LlamaParse pages as one price.
Can I use PdfParse with LlamaIndex
Yes, you can use PdfParse output in a LlamaIndex pipeline. Call PdfParse through its Python SDK and pass the returned Markdown or structured data into your ingestion code. This requires mapping the response into your application; it does not imply a native LlamaIndex integration or affiliation between the products.
Is PdfParse cheaper than LlamaParse
PdfParse is cheaper per page than LlamaParse Agentic Plus when you fully use a paid plan's allowance, but it is not cheaper than LlamaParse Fast or Cost-effective. Growth billed yearly is about $0.0106 per page at full utilization. Your total bill also depends on volume and unused subscription capacity.
Run your hardest PDFs through PdfParse
Try page one in the browser, then choose a plan and compare the API outputs against the documents your application actually handles.