Replace your Puppeteer or wkhtmltopdf setup with a single POST request. We run the Chromium instance, store the output, and return raw bytes or a signed URL. Works great for invoices, reports, and AI-generated summaries via MCP.
How it works
Pass your line items, totals, and billing details. Get back a pixel-perfect branded PDF — logo included — ready to attach and send. No design tool. No template editor. Just an API call.
Nine built-in templates ship out of the box. Browse and fork community-contributed templates from the template catalog to hit the ground running.
Acme Design Studio
340 Pine Street, Suite 800| Description | Qty | Unit Price | Amount |
|---|---|---|---|
| Brand Identity Design | 1 | $4,800.00 | $4,800.00 |
| UI Component Library | 1 | $6,200.00 | $6,200.00 |
| Prototype & Handoff | 8h | $150.00 | $1,200.00 |
| Subtotal | $12,200.00 |
| Tax (8%) | $976.00 |
| Total Due | $13,176.00 |
Thank you for your business. Payment due within 14 days via wire transfer.
curl -X POST https://docrenders.com/render \
-H "Authorization: Bearer dcr_live_YOUR_KEY" \
-H "Content-Type: application/json" \
-d '{"markdown": "# Invoice\n\nDue: **$1,200**", "output": "binary"}' \
--output invoice.pdf
# Or get a signed URL instead of raw bytes:
# -d '{"markdown": "# Invoice\n\nDue: **$1,200**", "output": "url"}'
You could. But here's what that actually means.
Send raw Markdown or full HTML. We handle the rendering pipeline — goldmark for Markdown, Chromium for pixel-perfect PDF output.
Typical renders complete in under 3 seconds. A4, Letter, and Legal paper sizes. Portrait and landscape. Custom margins.
Every request is authenticated with a scoped API key. Per-key rate limits enforced at the edge. Revoke anytime from the dashboard.
Every render is stored automatically. Request raw PDF bytes for immediate use, or a signed URL to deliver the file directly to your users — no proxying required.
Pass "template": "invoice" in any render request. Your Markdown flows into a professionally styled layout.
{
"template": "invoice",
"markdown": "# Invoice #INV-2025-089\n\n**Bill To:** TechCorp Inc. · Sarah Johnson, CTO\n**Invoice Date:** October 15, 2025\n**Due:** November 1, 2025\n\n| Service | Hours | Rate | Amount |\n|---------|-------|------|--------|\n| UX Research & Wireframes | 24h | $150/h | $3,600 |\n| Dashboard UI Design | 40h | $150/h | $6,000 |\n| Prototype & Handoff | 8h | $150/h | $1,200 |\n\n**Subtotal:** $10,800\n**Tax (8%):** $864\n**Total Due:** $11,664\n\n---\n*Wire transfer · First National · Account: 4521-8876 · Routing: 021000021*"
}
Bill To: TechCorp Inc. · Sarah Johnson, CTO
Invoice Date: October 15, 2025
Due: November 1, 2025
| Service | Hours | Rate | Amount |
|---|---|---|---|
| UX Research & Wireframes | 24h | $150/h | $3,600 |
| Dashboard UI Design | 40h | $150/h | $6,000 |
| Prototype & Handoff | 8h | $150/h | $1,200 |
Subtotal: $10,800
Tax (8%): $864
Total Due: $11,664
Wire transfer · First National · Account: 4521-8876 · Routing: 021000021
{
"template": "ai-summary",
"markdown": "# Q3 2025 Market Intelligence\n*Generated by GPT-4o · October 2025*\n\n## Executive Summary\n\n> SaaS market grew 28% YoY in Q3 2025, driven by AI-native applications. CAC decreased 15% as AI sales tooling matured.\n\n## Key Findings\n\n1. **Revenue Growth** — Top-quartile companies averaged 45% ARR growth\n2. **Churn Reduction** — AI onboarding cut 90-day churn by 31%\n3. **Expansion Revenue** — NDR exceeded 120% in 67% of companies\n\n## Competitive Landscape\n\n| Company | ARR | YoY | NPS |\n|---------|-----|-----|-----|\n| Acme SaaS | $42M | +67% | 72 |\n| CloudBase | $28M | +41% | 68 |\n| DataFlow | $19M | +89% | 81 |"
}
| Company | ARR | YoY | NPS |
|---|---|---|---|
| Acme SaaS | $42M | +67% | 72 |
| CloudBase | $28M | +41% | 68 |
| DataFlow | $19M | +89% | 81 |
{
"template": "resume",
"markdown": "# Alexandra Chen\nStaff AI Engineer · alex@chen.dev · San Francisco, CA\n\n## Experience\n\n### Staff Engineer — Anthropic *(2023–Present)*\nLed development of Claude's tool use capabilities. Reduced inference latency 40% through KV cache optimization. Managed team of 8 engineers.\n\n### Senior ML Engineer — OpenAI *(2021–2023)*\nCore contributor to GPT-4 fine-tuning pipeline. Designed distributed training infrastructure for 10K+ GPU clusters. Published 3 NeurIPS papers.\n\n### Software Engineer — Google DeepMind *(2018–2021)*\nBuilt production ML serving infra. Led TF→JAX migration, improving throughput 3x.\n\n## Skills\n**Languages:** Python, Go, Rust, C++\n**ML:** PyTorch, JAX, CUDA, Triton\n\n## Education\n**M.S. CS** — Stanford University *(2018)*\n**B.S. Math & CS** — MIT *(2016)* · Summa cum laude"
}
Led development of Claude's tool use capabilities. Reduced inference latency 40% through KV cache optimization. Managed team of 8 engineers across 3 time zones.
Core contributor to GPT-4 fine-tuning pipeline. Designed distributed training infrastructure for 10K+ GPU clusters. Published 3 papers at NeurIPS.
Built production ML serving infrastructure. Led migration from TensorFlow 1.x to JAX, improving training throughput 3x.
Languages: Python, Go, Rust, C++ · ML: PyTorch, JAX, CUDA, Triton · Infra: Kubernetes, Ray, SLURM