Edge AI Ops

Harper Named a Sample Vendor in the Gartner® Hype Cycle™ for Digital Commerce, 2026

Harper was named a 2026 Gartner Hype Cycle for Digital Commerce Sample Vendor, recognized in Front-End Cloud for its unified runtime combining data, application logic, messaging, and agent development together.

Announcement

News

Harper was named a 2026 Gartner Hype Cycle for Digital Commerce Sample Vendor, recognized in Front-End Cloud for its unified runtime combining data, application logic, messaging, and agent development together.

Harper

News

Harper Named a Sample Vendor in the Gartner® Hype Cycle™ for Digital Commerce, 2026

Harper was named a 2026 Gartner Hype Cycle for Digital Commerce Sample Vendor, recognized in Front-End Cloud for its unified runtime combining data, application logic, messaging, and agent development together.

Harper

Jun 2026

News

Harper Named a Sample Vendor in the Gartner® Hype Cycle™ for Digital Commerce, 2026

Harper was named a 2026 Gartner Hype Cycle for Digital Commerce Sample Vendor, recognized in Front-End Cloud for its unified runtime combining data, application logic, messaging, and agent development together.

Harper

News

Harper Named a Sample Vendor in the Gartner® Hype Cycle™ for Digital Commerce, 2026

Harper was named a 2026 Gartner Hype Cycle for Digital Commerce Sample Vendor, recognized in Front-End Cloud for its unified runtime combining data, application logic, messaging, and agent development together.

Harper

Blog

Rails Won Because It Had Opinions. AI-Native Apps Need the Same Thing.

Rails proved that opinionated frameworks make developers faster. Now AI-native, distributed applications need the same kind of defaults for data, logic, caching, and messaging.

A.I.

Blog

Rails proved that opinionated frameworks make developers faster. Now AI-native, distributed applications need the same kind of defaults for data, logic, caching, and messaging.

Aleks Haugom

Senior Manager of GTM

Blog

Event

Discover how Harper successfully transitioned to a sustainable commercial open source platform. Ethan Arrowood details their split core licensing strategy, engineering velocity, and practical framework for building true community trust.

Ethan Arrowood

Head of Open Source

Event

Building Sustainable Open Source: The Harper Story

Discover how Harper successfully transitioned to a sustainable commercial open source platform. Ethan Arrowood details their split core licensing strategy, engineering velocity, and practical framework for building true community trust.

Ethan Arrowood

Jun 2026

Event

Building Sustainable Open Source: The Harper Story

Discover how Harper successfully transitioned to a sustainable commercial open source platform. Ethan Arrowood details their split core licensing strategy, engineering velocity, and practical framework for building true community trust.

Ethan Arrowood

Event

Building Sustainable Open Source: The Harper Story

Discover how Harper successfully transitioned to a sustainable commercial open source platform. Ethan Arrowood details their split core licensing strategy, engineering velocity, and practical framework for building true community trust.

Ethan Arrowood

Comparison

Kafka-Centered Stacks vs. a Single Harper Cluster: Where Real-Time Latency Actually Comes From

End-to-end latency in real-time pipelines comes from coordination across systems, not from any single component. Four common workloads, tested two ways, show where multi-hop architectures compound delays and where collapsing storage, messaging, and compute into one runtime changes the math.

Cache

Comparison

End-to-end latency in real-time pipelines comes from coordination across systems, not from any single component. Four common workloads, tested two ways, show where multi-hop architectures compound delays and where collapsing storage, messaging, and compute into one runtime changes the math.

Aleks Haugom

Senior Manager of GTM

Comparison

Kafka-Centered Stacks vs. a Single Harper Cluster: Where Real-Time Latency Actually Comes From

End-to-end latency in real-time pipelines comes from coordination across systems, not from any single component. Four common workloads, tested two ways, show where multi-hop architectures compound delays and where collapsing storage, messaging, and compute into one runtime changes the math.

Aleks Haugom

Jun 2026

Comparison

Kafka-Centered Stacks vs. a Single Harper Cluster: Where Real-Time Latency Actually Comes From

End-to-end latency in real-time pipelines comes from coordination across systems, not from any single component. Four common workloads, tested two ways, show where multi-hop architectures compound delays and where collapsing storage, messaging, and compute into one runtime changes the math.

Aleks Haugom

Comparison

Kafka-Centered Stacks vs. a Single Harper Cluster: Where Real-Time Latency Actually Comes From

End-to-end latency in real-time pipelines comes from coordination across systems, not from any single component. Four common workloads, tested two ways, show where multi-hop architectures compound delays and where collapsing storage, messaging, and compute into one runtime changes the math.

Aleks Haugom

Tutorial

Your API cache is secretly a database

Most teams treat a cache as a black box: URL-keyed blobs with a TTL, useful for speed and nothing else. In Harper, cached data lands in a real table inside the same query engine. That means filtering, joining, real-time subscriptions, and vector search all work against it.

Cache

Tutorial

Most teams treat a cache as a black box: URL-keyed blobs with a TTL, useful for speed and nothing else. In Harper, cached data lands in a real table inside the same query engine. That means filtering, joining, real-time subscriptions, and vector search all work against it.