Hosted Docling API

Docling is great until you try to deploy it to Vercel, Lambda, or any serverless platform. ParseBridge runs Docling as a service so you can ship production workloads without managing GPU infrastructure.

Powered by the open-source Docling engine

Try Docling API Free

50 free pages, no credit card required

Honest take: if you're running Docling locally, you probably don't need us

Docling is excellent open-source software. If you're parsing a handful of PDFs on your laptop, running a script on your own machine, or experimenting with RAG pipelines in a notebook, just use Docling directly. It's free, it's great, and there's no reason to pay for a hosted version.

Where things get painful is production.

The moment you try to deploy Docling to the platforms most developers actually ship on (Vercel, AWS Lambda, Cloud Run, Netlify) you hit a wall. These serverless platforms don't support the GPU requirements, the package size, or the model downloads Docling needs. You can force it to work on Docker or a dedicated VPS, but you'll spend hours on setup, pay for GPU instances 24/7, and inevitably run into scaling issues as traffic grows.

That's the gap ParseBridge fills. Docling for people who need it in production.

What production deployment actually looks like

Docling works beautifully on your laptop. Here's what happens when you try to ship it.

Vercel, Lambda, and Cloud Run can't run Docling at all

The platforms most teams deploy to don't support GPU workloads, and Docling's models are too heavy to load within serverless cold-start windows. If your stack lives on Vercel or Lambda, self-hosting Docling isn't a slower path. It's a dead end.

ParseBridge gives you a simple REST API. Call it from any platform, any language, any runtime.

Docker and VPS hosting works, but the overhead is real

You can get Docling running on a dedicated Docker host or a VPS, but you're now paying for a GPU instance 24/7, downloading 500MB+ of models on every cold start unless you build caching, and managing a service you didn't set out to build.

No Docker, no GPU provisioning, no model downloads. One HTTP request, structured Markdown back.

Python dependency hell

PyTorch, transformers, specific CUDA versions, model downloads. Getting Docling working in a container is a half-day of yak shaving before you even parse a single page.

One HTTP request. No Python, no Docker, no CUDA.

Model storage and updates

The models are hundreds of MB, downloaded on first run. Every deploy or cold start re-downloads them unless you build caching.

Models are pre-loaded and kept up to date. We track Docling releases so you don't have to.

Scaling is your problem

If you do get Docling running in Docker, concurrent requests need a queue, retries, and timeouts. You end up building infrastructure instead of shipping product.

# What scaling Docling yourself looks like
services:
  docling:
    image: your-docling-image
    deploy:
      resources:
        reservations:
          devices:
            - capabilities: [gpu]
  redis:
    image: redis:7
  worker:
    build: ./worker
    depends_on: [redis, docling]
    environment:
      - CELERY_BROKER=redis://redis:6379
      - MAX_RETRIES=3
      - TIMEOUT=300
  nginx:
    image: nginx
    # ... load balancing config
    # ... health checks
    # ... auto-scaling rules
    # ... monitoring
    # You get the idea.

ParseBridge handles concurrency, retries, and scaling automatically. Process one page or a hundred thousand.

See it on your own document

Paste a PDF URL or upload a PDF to get a live Markdown preview. No account needed.

Preview mode parses page 1 only.

1
Choose your source

We’ll fetch the PDF and return Markdown for the first page only.

or
2
First-page Markdown Preview

Page 1 Markdown will appear here

No signup required · PDF only · Page 1 preview only

Self-hosted Docling vs. ParseBridge

Same Docling engine underneath. One version you manage, the other you call.

Not sure which you need? If your workload runs locally or on a dedicated server you already manage, Docling is probably all you need. If you're deploying to Vercel, Lambda, Cloud Run, or any serverless platform, keep reading.
Self-hosted DoclingParseBridge
Setup time❌ Hours to days✅ One API call
GPU required❌ Yes✅ No
Works on Vercel / Lambda❌ Not possible✅ Yes
Works on Docker / VPS⚠️ With significant setup✅ Yes
Scaling❌ You build it✅ Handled
OCR for scanned PDFs❌ Separate setup✅ Built in
Data privacy✅ Full control✅ Zero retention
Cost❌ GPU instance 24/7✅ Pay per page
Maintenance❌ Your team✅ Ours

Replace your Docling stack with one API call

No Python environment, no Docker, no model downloads. Send a URL, get Markdown back.

Request
const res = await fetch("https://api.parsebridge.com/v1/parse/url", {
  method: "POST",
  headers: {
    Authorization: "Bearer pb_your_api_key",
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
    url: "https://example.com/annual-report.pdf",
  }),
});

const { markdown } = await res.json();
console.log(markdown);
Response
# Annual Report 2024

## Financial Overview

The company reported strong growth across all segments.
Revenue increased by 15% year-over-year.

| Quarter | Revenue | Net Income | Growth |
|---------|---------|------------|--------|
| Q1 2024 | $2.4M   | $340K      | 15.3%  |
| Q2 2024 | $2.8M   | $420K      | 16.7%  |

## Risk Factors

Market volatility and regulatory changes present
ongoing challenges to sustained growth...

Cheaper than a GPU instance

Self-host Docling on a GPU

$300+/mo

before you write a line of code

vs

ParseBridge Basic plan

$17/mo

5,000 pages included

$17

Basic

5,000 pages/month

Start free
Most Popular

$79

Growth

60,000 pages/month

Start free

$259

Scale

300,000 pages/month

Start free

All plans include 50 free pages to start. See full plan details

Frequently asked questions

Should I just self-host Docling?

If you're running workloads locally or on a dedicated server you already manage, yes. Docling is excellent open-source software and you don't need us. ParseBridge is specifically built for teams deploying on Vercel, Lambda, Cloud Run, and other serverless platforms where Docling can't run natively, or teams who don't want to manage GPU infrastructure themselves.

Is ParseBridge the same as Docling?

ParseBridge runs Docling under the hood. You get the same parsing engine and the same output quality, without managing the infrastructure yourself. Think of it like Neon for Postgres or Vercel for Next.js.

What happens to my documents after parsing?

They're deleted immediately after processing. We do not store, log, or train on your data. Zero retention, no exceptions.

Which version of Docling do you run?

We track the latest stable Docling release and update regularly. You always get the newest models and parsing improvements without changing your integration.

Stop managing Docling infrastructure

Get the same Docling output with a single API call. No GPUs, no dependencies, no maintenance.

Try Docling API Free

Start with 50 free pages, no credit card required

Questions? support@parsebridge.com