Harper Use Cases
Harper is designed to cut out infrastructure complexity so you can move faster.
Here are some common ways developers use Harper in production today — each one showing how Harper’s architecture translates into real-world outcomes.
RESTful APIs for Distributed & Cached Data
Great for: web apps, mobile apps, data-heavy platforms.
Harper’s most common use case is exposing distributed, cached data over a RESTful interface.
This lets you serve complex or large-scale datasets efficiently, with built-in caching and global distribution.
- Define your schema with the
graphqlSchema
plugin. - Expose it instantly over REST using the
rest
plugin. - Take advantage of Harper’s caching layer to serve hot data without extra infrastructure.
- Power both web and mobile applications from the same API.
💡 Why it matters: Instead of bolting a cache or API layer onto a database, Harper gives you a unified system that scales for real-world apps.
Online Catalogs & Content Delivery
Great for: e-commerce sites, real estate listings, media & content platforms.
Harper’s distributed architecture makes your pages load fast worldwide, improving SEO and conversion rates.
- Host your frontend directly with the Next.js Extension.
- Support any framework using Harper’s extension system.
- Use Harper’s built-in caching + JavaScript layer to server-side render pages.
- Keep pages instantly fresh with built-in WebSocket connections.
💡 Why it matters: Instead of stitching together CDN + DB + API layers, you deliver catalog and content experiences from a single platform.
Data Delivery Networks
Great for: live sports updates, flight tracking, software updates.
Harper combines messaging, data storage, and application logic in one system. That means:
- Push real-time updates directly to clients.
- Process and store data without leaving Harper.
- Eliminate extra message brokers or caching systems.
Explore the real-time docs to see how it works.
💡 Why it matters: You can build real-time data services in hours, not weeks, with fewer moving parts to manage.
Edge Inference Systems
Great for: IoT pipelines, sensor networks, edge AI.
Normally, capturing and analyzing streams at the edge requires a patchwork of tools. Harper simplifies this with:
- Self-healing connections that keep data flowing even in flaky environments.
- The same Harper runtime running at both layers.
💡 Why it matters: One consistent stack across edge and cloud makes AI/ML inference faster, cheaper, and easier to scale.
✅ Want to explore more? Contact us and we’ll walk you through building your own use case.