CurlShot vs Qdrant: Which Is Better in 2026?

CurlShot gives your app, your scripts and your AI agents a clear look at any web page.

ONE REQUEST
Send a page address, HTML or Markdown to one endpoint and get back an image (PNG, JPEG, WebP), a PDF, or a scrolling video (MP4, WebM, GIF). No SDK: anything that can fetch a URL already works, and the docs have examples for cURL, Node.js, Python, PHP, Go and Ruby.

WHAT PEOPLE BUILD WITH IT
- Link previews and website thumbnails
- Open Graph images rendered from HTML templates
- Visual checks of pages across phones, tablets and desktops
- PDF archives of web pages for records and compliance
- Reports and certificates from Markdown or HTML
- AI agents that need to see a page, through MCP

DETAILS THAT SAVE TIME
Cookie banners and ads hidden before the capture. Lazy images loaded. Dark mode on request. Wait for a selector, click a button, inject CSS. Cache results and reuse them for free. Signed links so a key never appears in your HTML. Async jobs, webhooks and bulk runs of up to 100 pages.

WHO IT IS FOR
Developers adding previews or PDFs to a product, teams automating reports and checks in n8n or their own scripts, and builders giving AI agents a way to look at web pages.

PRICES
A free plan with 100 screenshots a month and no card. Paid plans have a fixed monthly price: $19, $79 or $249. Yearly billing gets 2 months free. 14-day money-back guarantee.

Try it without an account at https://curlshot.com

What problem it solves

Running headless browsers in production is fragile: cookie banners, lazy images, timeouts, memory and queues. Developers who need screenshots, PDFs or page videos in their apps, pipelines or AI agents get them from one HTTP request instead.

Alternatives & competitors

An alternative to running Puppeteer or Playwright yourself, and to other screenshot APIs.

Qdrant is a high-performance vector database and search engine, written entirely in Rust, that powers semantic and similarity search for AI applications. When an app needs to find "things like this" — relevant documents for a RAG system, similar products for recommendations, matching images — Qdrant is the engine that makes that fast and scalable, from startups to Fortune 500s.

What Qdrant does

Qdrant stores and searches vector embeddings — the numerical representations AI models produce for text, images and other data — so you can retrieve results by meaning rather than exact keywords. It combines dense and sparse (hybrid) search, filters on rich metadata, reranks results, and does it all at speed thanks to a purpose-built Rust core, with real-time indexing that doesn't require full rebuilds.

Who relies on it

Qdrant serves AI and ML engineers building retrieval systems, teams implementing RAG, and organizations that need recommendation engines or semantic search. Its users span from startups to enterprises — TripAdvisor, OpenTable, HubSpot, Canva and Deutsche Telekom among them — reflecting a database trusted for serious, production AI workloads.

What's under the hood

• Hybrid dense-sparse search combining keyword and vector matching
• Native multivector support for multimodal data
• One-stage filtering applied during traversal, with rich JSON/geo filters
• Full-spectrum reranking with score boosting and MMR
• Real-time indexing without full rebuilds
• Quantization reducing memory use by up to 64x
• REST and gRPC APIs with Python and JavaScript clients, plus a web UI
• Cloud Inference for generating embeddings

Fast because it's built for it

Qdrant's Rust foundation and custom Gridstore engine are optimized specifically for vector operations, which is why it performs well at scale where general-purpose databases struggle. Its quantization can cut memory use dramatically — up to 64x — meaning large vector datasets run on less hardware, and real-time indexing keeps results current without expensive rebuilds. For AI apps where retrieval speed directly shapes user experience, that engineering focus matters.

Search that's actually relevant

Raw vector search isn't always enough, so Qdrant layers on precision. Hybrid search blends semantic matching with keyword matching to catch both meaning and exact terms, one-stage filtering applies metadata constraints efficiently during the search itself, and full-spectrum reranking with MMR improves the final ordering and diversity of results. Together these turn "roughly similar" into genuinely relevant answers — crucial for good RAG and recommendations.

Deploy it however you need

Qdrant is flexible about where it runs: a fully managed Qdrant Cloud on AWS, GCP or Azure; Hybrid Cloud on your own Kubernetes; an Edge option for low latency; and the open-source core for full self-hosting. That range lets a team start managed and move toward self-hosted control — or keep sensitive data in their own environment — without changing databases. It's also SOC 2 and HIPAA compliant for regulated industries.

Friendly for developers

Qdrant is built to be pleasant to work with, not just powerful. It exposes both REST and gRPC APIs with official Python and JavaScript clients, ships a built-in web UI for exploring collections and testing queries, and offers Cloud Inference so you can generate embeddings without wiring up a separate service. For engineers, that means getting a working semantic-search prototype running quickly, then scaling the same setup to production without switching tools.

Why choose Qdrant

For teams building AI features that depend on fast, accurate similarity search — RAG, recommendations, semantic and multimodal search — Qdrant is a top-tier choice. Its Rust-powered performance, memory-saving quantization, hybrid search and reranking, flexible deployment and strong compliance make it a practical, production-ready foundation, backed by a 30k-star open-source community and adoption at companies operating at real scale.