PDF Vector vs Eden AI: 2026 Comparison

PDF Vector and Eden AI solve different problems. Eden AI is an AI gateway: one API key and one bill for around 1,000 models from 80+ providers, covering chat models and "expert models" such as OCR, invoice parsing, speech, translation and image analysis. PDF Vector is a document and academic API: it runs its own pipeline to parse documents into Markdown, extract JSON that matches your schema, answer questions about a file, and search scholarly papers. Choose Eden AI when documents are one of several AI tasks and you want provider choice. Choose PDF Vector when documents are the core of your product and you want one pipeline with your own output schema. Facts below were checked in November 2026 against Eden AI's documentation and PDF Vector's API.

Quick comparison

	PDF Vector	Eden AI
What it is	Document and academic API with its own processing pipeline	AI gateway that routes each request to a third-party provider
Document operations	Parse (Markdown, HTML on max), Extract (JSON per your schema), Ask (answer in Markdown)	OCR, multipage OCR, table extraction, financial parser, ID parser, resume parser, document translation
Output shape	You define it with a JSON Schema per request	Fixed format per feature (e.g. extracted_data with customer, merchant and line-item fields for financial parsing)
Specialized documents	Invoices, identity documents, bank statements	Invoices/receipts (financial parser), IDs, resumes
Engine choice	One pipeline; you pick a tier (nano, mini, pro, max, auto)	You pick the provider per request (Amazon, Google, Microsoft, Mistral, Mindee, Veryfi, Affinda and others)
Other AI	Academic search, paper fetch and parse, citations, paper graph, similar papers, grants	Chat models, embeddings, speech, translation, image, video, web search
Pricing	Monthly plans with credits; per-page credit cost by tier	Provider price with no markup + 5.5% platform fee; prepaid credits; no subscription
Free option	Free plan, 100 credits/month	Sandbox token with mock responses
Regions	Shared instance in the US; Enterprise dedicated instances in UK, EU, US, Japan, Australia or Asia Pacific	Global endpoint and an EU endpoint that rejects models not cleared for European processing
No-code and agents	Zapier, Make, n8n actions; MCP server	Make, Zapier, n8n, Bubble, LangChain; MCP server

How Eden AI works

Eden AI exposes two main endpoints: an OpenAI-compatible chat endpoint for language models, and a "Universal AI" endpoint for expert models. You choose the engine in the model string, for example ocr/financial_parser/google or ocr/resume_parser/affinda. Every response includes the cost of that call in USD. A financial parsing request from Eden AI's docs looks like this:

import requests

response = requests.post(
    "https://api.edenai.run/v3/universal-ai",
    headers={"Authorization": "Bearer YOUR_EDEN_AI_KEY"},
    json={
        "model": "ocr/financial_parser/google",
        "input": {"file": "YOUR_FILE_UUID_OR_URL", "language": "en"},
    },
)
print(response.json())

What Eden AI does well, according to its own pages (checked November 2026):

• Provider choice and switching. The same request shape works across providers, so you can benchmark engines on your documents and switch without rewriting the integration. Automatic fallback to another provider works for both chat and expert (Universal AI) model requests (fallback docs).
• Transparent per-call prices. Each feature page lists provider prices. Financial parsing, for example, costs $0.01 per page through Amazon, Google or Microsoft, $0.10 per page through Mindee and $0.16 per file through Veryfi. Plain OCR costs $1 to $1.50 per 1,000 pages from Amazon, Google, Microsoft or Mistral; async table extraction $10 to $65 per 1,000 pages (OCR, tables).
• Simple billing. No markup on provider prices, a 5.5% platform fee when you buy credits, no subscription and no minimum (plans and pricing). Budgets and attribution per API key, member or project.
• EU processing. A dedicated EU endpoint refuses, with HTTP 451, any model not cleared for European processing, before a provider is contacted.
• Data handling. By default Eden AI does not store request or response content and does not use data for training; async job results are kept for 7 days (data retention). Eden AI states it is SOC 2 and ISO 27001 certified.
• Breadth. If your product also needs chat, embeddings, speech-to-text or translation, they come with the same key and bill.

How PDF Vector works

PDF Vector has one pipeline per operation and lets you trade cost for quality with a model tier. The default, auto, picks a tier per document. Every document family (general documents, invoices, identity documents, bank statements) has the same three operations:

• Parse returns the whole document as Markdown, with optional per-page output, for LLM, search and RAG pipelines.
• Extract takes a prompt and a JSON Schema and returns data in exactly that shape.
• Ask answers a question about one document in Markdown.

The same invoice as above, with the fields you choose:

import requests

response = requests.post(
    "https://global.pdfvector.com/api/invoice/extract",
    headers={"Authorization": "Bearer YOUR_API_KEY"},
    json={
        "url": "https://example.com/supplier-invoice.pdf",
        "prompt": "Extract vendor, invoice number, invoice date as YYYY-MM-DD, currency, total and line items",
        "schema": {
            "type": "object",
            "properties": {
                "vendor": {"type": "string"},
                "invoiceNumber": {"type": "string"},
                "date": {"type": "string"},
                "currency": {"type": "string"},
                "total": {"type": "number"},
                "lineItems": {
                    "type": "array",
                    "items": {
                        "type": "object",
                        "properties": {
                            "description": {"type": "string"},
                            "quantity": {"type": "number"},
                            "amount": {"type": "number"},
                        },
                    },
                },
            },
            "required": ["vendor", "invoiceNumber", "total"],
        },
        "model": "auto",
    },
    timeout=300,
)
result = response.json()
print(result["data"], result["pageCount"], result["credits"])

The response contains data, pageCount, the model tier that ran, credits and a requestId. Files can be sent as a url, a multipart file or base64. For long jobs, add callback: { url } and the API returns 202 and posts the result to your webhook.

Formats and limits. PDF, Word, Excel, PowerPoint, CSV, HTML, RTF, OpenDocument, EPUB and text on every tier; images on pro, max or auto. Up to 40 MB and 30 pages on nano, mini and pro; up to 500 MB and 2,000 pages on max and auto.

Academic APIs. Academic Search queries Semantic Scholar, PubMed, arXiv, Google Scholar, ERIC, Europe PMC, OpenAlex and Crossref and merges the results; other endpoints fetch papers by DOI or other IDs, parse papers to Markdown, suggest citations for sentences, build citation graphs, find similar papers and search grant databases in the US, EU and UK. Eden AI's documentation index lists web search for language models but no scholarly search feature (checked November 2026).

Cost: a worked example

Take 1,000 single-page supplier invoices a month.

Eden AI, financial parser:

• Google or Microsoft: 1,000 × $0.01 = $10, plus the 5.5% fee = $10.55.
• Mindee: 1,000 × $0.10 = $100, plus 5.5% = $105.50.
• Veryfi: 1,000 × $0.16 = $160, plus 5.5% = $168.80.

PDF Vector, Invoice Extract (6, 10, 14 or 18 credits per page on nano, mini, pro, max):

• nano: 1,000 × 6 = 6,000 credits. pro: 1,000 × 14 = 14,000 credits.
• Both exceed the Basic plan's 3,000 credits, so this volume needs the Pro plan: $97 a month for 100,000 credits. At that plan's rate ($97 ÷ 100,000 = about $0.001 per credit), 14,000 credits are worth about $13.58, and the rest of the allowance stays available for parsing, extraction on other document types or academic search.
• A lower-volume team processing 300 one-page invoices on nano uses 300 × 6 = 1,800 credits, which fits the $25 Basic plan.

If invoices are your only workload, Eden AI through Google or Microsoft is cheaper in cash at this volume: about $10.55 a month against $97 for PDF Vector's Pro plan. The plan pays off when you use the rest of the allowance, or when the output in your own schema saves mapping work.

Two differences matter more than the headline numbers:

1. Plans vs pay-as-you-go. Eden AI bills only what you use; PDF Vector plan credits reset monthly and unused credits do not roll over. For spiky or very low volume, pay-as-you-go can be cheaper; for steady volume with several document types, a plan is easier to budget.
2. Output you can use. Eden AI's financial parser returns a fixed structure. If you need fields it does not have, or a different shape for your database, you map them yourself or move to a language model with structured output. PDF Vector returns your schema directly, which removes the mapping layer.

Where Eden AI is the better choice

• Documents are one of many AI tasks. A support platform that needs chat, translation, transcription and occasional OCR gets everything on one key and one invoice.
• You want to benchmark or switch engines. Running the same invoices through Google, Microsoft, Mindee and Veryfi takes a change of model string.
• Very cheap plain OCR at low volume. At $1 to $1.50 per 1,000 pages with no subscription, Eden AI's OCR providers are hard to beat for raw text.
• Strict EU processing. The EU endpoint enforces European-only providers per request. PDF Vector's shared instance runs in the US; EU processing requires an Enterprise dedicated instance.
• Bounding boxes. Eden AI's OCR output includes word- and line-level bounding boxes with confidence scores, useful for highlighting values on the page. PDF Vector returns text and data, not coordinates.
• Resume parsing. Eden AI routes to dedicated resume parsers (Affinda, Klippa, Senseloaf and others). PDF Vector can extract resumes with a schema, but has no resume-specific endpoint.

Where PDF Vector is the better choice

• Documents are the product. Contracts, reports, statements and forms of any layout, extracted into your own schema with one request, without choosing and comparing engines.
• LLM-ready Markdown. Parse returns clean Markdown with tables, per page if you want, for RAG and search. Eden AI's OCR features return plain text with word and line bounding boxes rather than Markdown.
• Specialized finance and KYC endpoints. Invoice, identity and bank statement APIs with Parse, Extract and Ask, priced per page.
• Research products. Academic search across eight sources, citations, paper graphs and grant search in the same account.
• One API, one output schema. The same API and your own output schema across documents; you tune a tier instead of comparing providers.
• No-code teams. Zapier, Make and n8n actions for parse, extract and ask (see the n8n integration), and an MCP server for Claude, Cursor and other MCP clients.

Can you use both?

Yes, and some teams do. A common split is Eden AI for chat, speech and translation across providers, and PDF Vector for document ingestion and extraction. Because both are plain HTTPS APIs with bearer tokens, they sit side by side in the same backend. Keep document-specific validation (totals, dates, required fields) in your code regardless of which service produced the data.

How to decide in an afternoon

1. List your AI tasks. If documents are less than half of them, start with Eden AI.
2. Write the JSON you need for your main document type. If no Eden AI feature returns it without remapping, test PDF Vector's Extract.
3. Run 20 real documents through both: Eden AI with two or three providers, PDF Vector on auto. Compare fields one by one.
4. Price your monthly volume with the arithmetic above, including the 5.5% fee and plan allowances.
5. Check your region and data requirements against the EU endpoint and PDF Vector's dedicated instances.

If documents turn out to be your core workload, compare PDF Vector with other document APIs as well: Azure Document Intelligence and other options are covered in our comparisons. If you are weighing Affinda too, see Best Affinda Alternatives in 2026.