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Dynamic DNS Optimization

Manually configuring DDNS providers is suboptimal. Instead, automate rebalancing with an automated rebalancing service.

Solution

Dynamic DNS Optimization

Harper

at Harper

April 4, 2025

Harper

at Harper

April 4, 2025

Harper

at Harper

April 4, 2025

April 4, 2025

Manually configuring DDNS providers is suboptimal. Instead, automate rebalancing with an automated rebalancing service.

Harper

Suboptimal CDN utilization erodes profitability and hinders user experience. Traditional DNS load balancing methods like round-robin and least-connected fail to account for dynamic network conditions and cost optimization.

Better Experience, Lower Costs

Harper's Dynamic DNS Optimization is a distributed service that makes use of your CDN contract data, current CDN usage, and network performance to optimize traffic distribution across CDNs. By continuously monitoring CDN health, latency, and capacity, our system dynamically adjusts traffic distribution weights to improve user experience, lower costs, and inform CDN renewal commitments.

Add powerful DDNS rebalancing automation to your tech stack.

Getting Started

Ready to optimize your CDN investment and enhance user experience? Contact us to discuss your specific requirements and explore implementation options.

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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 Named Winner of Two 2026 Data Breakthrough Awards

Harper has been named the winner of two 2026 Data Breakthrough Awards: Open Source Data Platform of the Year and In-Memory Solution of the Year. Harper is the only company recognized in two categories this year, reflecting its unified runtime for agentic engineering that co-locates database, vector search, cache, and application logic into a single multi-threaded process.

Suboptimal CDN utilization erodes profitability and hinders user experience. Traditional DNS load balancing methods like round-robin and least-connected fail to account for dynamic network conditions and cost optimization.

Better Experience, Lower Costs

Harper's Dynamic DNS Optimization is a distributed service that makes use of your CDN contract data, current CDN usage, and network performance to optimize traffic distribution across CDNs. By continuously monitoring CDN health, latency, and capacity, our system dynamically adjusts traffic distribution weights to improve user experience, lower costs, and inform CDN renewal commitments.

Add powerful DDNS rebalancing automation to your tech stack.

Getting Started

Ready to optimize your CDN investment and enhance user experience? Contact us to discuss your specific requirements and explore implementation options.

‍

Manually configuring DDNS providers is suboptimal. Instead, automate rebalancing with an automated rebalancing service.

Download

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Manually configuring DDNS providers is suboptimal. Instead, automate rebalancing with an automated rebalancing service.

Download

White arrow pointing right

Manually configuring DDNS providers is suboptimal. Instead, automate rebalancing with an automated rebalancing service.

Download

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Explore Recent Resources

5 Architectures for Web Personalization

Personalization is a data-delivery problem. Every architectural choice reduces to two distances: compute to user, and compute to fresh data. This piece maps five real architectures against both axes, scored on a concrete retailer workload where stale or slow data breaks the business.

Blog

Personalization is a data-delivery problem. Every architectural choice reduces to two distances: compute to user, and compute to fresh data. This piece maps five real architectures against both axes, scored on a concrete retailer workload where stale or slow data breaks the business.

Aleks Haugom

Senior Manager of GTM

5 Architectures for Web Personalization

Personalization is a data-delivery problem. Every architectural choice reduces to two distances: compute to user, and compute to fresh data. This piece maps five real architectures against both axes, scored on a concrete retailer workload where stale or slow data breaks the business.

5 Architectures for Web Personalization

Personalization is a data-delivery problem. Every architectural choice reduces to two distances: compute to user, and compute to fresh data. This piece maps five real architectures against both axes, scored on a concrete retailer workload where stale or slow data breaks the business.

5 Architectures for Web Personalization

Personalization is a data-delivery problem. Every architectural choice reduces to two distances: compute to user, and compute to fresh data. This piece maps five real architectures against both axes, scored on a concrete retailer workload where stale or slow data breaks the business.

5x Faster HNSW Vector Search with int8 Quantization

Harper 5.1 adds int8 quantization, dropping index size by roughly 3x, search throughput improves roughly 5x, and p99 latency falls from ~9s to ~0.5s under load. The tradeoff is approximately 1% recall degradation before reranking.

Tutorial

Harper 5.1 adds int8 quantization, dropping index size by roughly 3x, search throughput improves roughly 5x, and p99 latency falls from ~9s to ~0.5s under load. The tradeoff is approximately 1% recall degradation before reranking.

Kris Zyp

SVP of Engineering

5x Faster HNSW Vector Search with int8 Quantization

Harper 5.1 adds int8 quantization, dropping index size by roughly 3x, search throughput improves roughly 5x, and p99 latency falls from ~9s to ~0.5s under load. The tradeoff is approximately 1% recall degradation before reranking.

5x Faster HNSW Vector Search with int8 Quantization

Harper 5.1 adds int8 quantization, dropping index size by roughly 3x, search throughput improves roughly 5x, and p99 latency falls from ~9s to ~0.5s under load. The tradeoff is approximately 1% recall degradation before reranking.

5x Faster HNSW Vector Search with int8 Quantization

Harper 5.1 adds int8 quantization, dropping index size by roughly 3x, search throughput improves roughly 5x, and p99 latency falls from ~9s to ~0.5s under load. The tradeoff is approximately 1% recall degradation before reranking.

Vercel vs. Harper Benchmark Test

Harper runs live, personalized apps 4–14x faster than Vercel by unifying data, logic, cache, and messaging in one runtime.

Comparison

Harper runs live, personalized apps 4–14x faster than Vercel by unifying data, logic, cache, and messaging in one runtime.

Harper

Vercel vs. Harper Benchmark Test

Harper runs live, personalized apps 4–14x faster than Vercel by unifying data, logic, cache, and messaging in one runtime.

Vercel vs. Harper Benchmark Test

Harper runs live, personalized apps 4–14x faster than Vercel by unifying data, logic, cache, and messaging in one runtime.

Vercel vs. Harper Benchmark Test

Harper runs live, personalized apps 4–14x faster than Vercel by unifying data, logic, cache, and messaging in one runtime.

Agentic Engineering Needs an Opinion: Why Scale Starts with Architecture

AI coding works in a sandbox because the environment is trivially narrow. Real systems have history, constraints, and blast radius. Coding agents make sound decisions only when the architecture is explicit and shared. Opinion isn't a constraint on agentic engineering, it's what makes it possible at scale.

Blog

AI coding works in a sandbox because the environment is trivially narrow. Real systems have history, constraints, and blast radius. Coding agents make sound decisions only when the architecture is explicit and shared. Opinion isn't a constraint on agentic engineering, it's what makes it possible at scale.

Stephen Goldberg

CEO & Co-Founder

Agentic Engineering Needs an Opinion: Why Scale Starts with Architecture

AI coding works in a sandbox because the environment is trivially narrow. Real systems have history, constraints, and blast radius. Coding agents make sound decisions only when the architecture is explicit and shared. Opinion isn't a constraint on agentic engineering, it's what makes it possible at scale.

Stephen Goldberg

Agentic Engineering Needs an Opinion: Why Scale Starts with Architecture

AI coding works in a sandbox because the environment is trivially narrow. Real systems have history, constraints, and blast radius. Coding agents make sound decisions only when the architecture is explicit and shared. Opinion isn't a constraint on agentic engineering, it's what makes it possible at scale.

Stephen Goldberg

Agentic Engineering Needs an Opinion: Why Scale Starts with Architecture

AI coding works in a sandbox because the environment is trivially narrow. Real systems have history, constraints, and blast radius. Coding agents make sound decisions only when the architecture is explicit and shared. Opinion isn't a constraint on agentic engineering, it's what makes it possible at scale.

Stephen Goldberg

Building a Cozy Sandbox Game on Harper

A nature-restoration game with six biomes, 150 animals, and a real food web — built with a single Harper component as the entire backend. One YAML file wires the database, API, content seeder, and static host. The same binary ships offline on itch.io.

Blog

A nature-restoration game with six biomes, 150 animals, and a real food web — built with a single Harper component as the entire backend. One YAML file wires the database, API, content seeder, and static host. The same binary ships offline on itch.io.

Bailey Dunning

Forward Deployed Engineer

Building a Cozy Sandbox Game on Harper

A nature-restoration game with six biomes, 150 animals, and a real food web — built with a single Harper component as the entire backend. One YAML file wires the database, API, content seeder, and static host. The same binary ships offline on itch.io.

Building a Cozy Sandbox Game on Harper

A nature-restoration game with six biomes, 150 animals, and a real food web — built with a single Harper component as the entire backend. One YAML file wires the database, API, content seeder, and static host. The same binary ships offline on itch.io.

Building a Cozy Sandbox Game on Harper

A nature-restoration game with six biomes, 150 animals, and a real food web — built with a single Harper component as the entire backend. One YAML file wires the database, API, content seeder, and static host. The same binary ships offline on itch.io.

Your Website was Built for Humans. AI Needs Something Cleaner.

The web spent a decade optimizing for browsers. JavaScript-heavy rendering, dynamic CMS templates, and client-side hydration made pages beautiful and machines blind. AI answer engines retrieve, parse, and cite content directly. If your best content is trapped behind a render cycle, a cleaner source wins.

Blog

The web spent a decade optimizing for browsers. JavaScript-heavy rendering, dynamic CMS templates, and client-side hydration made pages beautiful and machines blind. AI answer engines retrieve, parse, and cite content directly. If your best content is trapped behind a render cycle, a cleaner source wins.

Aleks Haugom

Senior Manager of GTM

Your Website was Built for Humans. AI Needs Something Cleaner.

The web spent a decade optimizing for browsers. JavaScript-heavy rendering, dynamic CMS templates, and client-side hydration made pages beautiful and machines blind. AI answer engines retrieve, parse, and cite content directly. If your best content is trapped behind a render cycle, a cleaner source wins.

Your Website was Built for Humans. AI Needs Something Cleaner.

The web spent a decade optimizing for browsers. JavaScript-heavy rendering, dynamic CMS templates, and client-side hydration made pages beautiful and machines blind. AI answer engines retrieve, parse, and cite content directly. If your best content is trapped behind a render cycle, a cleaner source wins.

Your Website was Built for Humans. AI Needs Something Cleaner.

The web spent a decade optimizing for browsers. JavaScript-heavy rendering, dynamic CMS templates, and client-side hydration made pages beautiful and machines blind. AI answer engines retrieve, parse, and cite content directly. If your best content is trapped behind a render cycle, a cleaner source wins.

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