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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 Recognized as an Honorable Mention in the 2026 Gartner® Magic Quadrant™ for Cloud-Native Application Platforms

Harper’s inclusion highlights a broader shift toward integrated cloud-native platforms that unify data, caching, messaging, and application logic. The article explains how Harper’s distributed runtime and managed infrastructure reduce operational complexity, improve performance, and support modern applications and AI agents that require fast, reliable access to state, events, and tools.

Harper 5.2: More Throughput per Node, Fewer Systems Around It

Harper 5.2 helps architecture and platform leaders improve performance, control infrastructure costs, secure production workloads, and reduce operational complexity by bringing faster data access, agentic capabilities, scheduling, backup, routing, and protection into one unified runtime.

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

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

Download

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Explore Recent Resources

The Topic Is the Record: Real-Time Fan-Out Without a Broker, a Bridge, and a Cache

Most real-time apps need a broker, a bridge, and a cache to keep state in sync. This is a live MQTT scoring demo on Harper, where the topic and the database record are the same write.

Blog

Most real-time apps need a broker, a bridge, and a cache to keep state in sync. This is a live MQTT scoring demo on Harper, where the topic and the database record are the same write.

Austin Akers

Head of Developer Relations

The Topic Is the Record: Real-Time Fan-Out Without a Broker, a Bridge, and a Cache

Most real-time apps need a broker, a bridge, and a cache to keep state in sync. This is a live MQTT scoring demo on Harper, where the topic and the database record are the same write.

The Topic Is the Record: Real-Time Fan-Out Without a Broker, a Bridge, and a Cache

Most real-time apps need a broker, a bridge, and a cache to keep state in sync. This is a live MQTT scoring demo on Harper, where the topic and the database record are the same write.

The Topic Is the Record: Real-Time Fan-Out Without a Broker, a Bridge, and a Cache

Most real-time apps need a broker, a bridge, and a cache to keep state in sync. This is a live MQTT scoring demo on Harper, where the topic and the database record are the same write.

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Cross-region latency comes from distance, not slow queries. Why centralized data paths cost you latency, uptime, and money, with a benchmark you can run.

Blog

Cross-region latency comes from distance, not slow queries. Why centralized data paths cost you latency, uptime, and money, with a benchmark you can run.

Austin Akers

Head of Developer Relations

The Distance Problem: Why Centralized Data Paths Cost You Latency, Uptime, and Money

Cross-region latency comes from distance, not slow queries. Why centralized data paths cost you latency, uptime, and money, with a benchmark you can run.

The Distance Problem: Why Centralized Data Paths Cost You Latency, Uptime, and Money

Cross-region latency comes from distance, not slow queries. Why centralized data paths cost you latency, uptime, and money, with a benchmark you can run.

The Distance Problem: Why Centralized Data Paths Cost You Latency, Uptime, and Money

Cross-region latency comes from distance, not slow queries. Why centralized data paths cost you latency, uptime, and money, with a benchmark you can run.

Harper Argues Against the Multi-System Stack and Releases 5.2

InfoQ examines benchmark results comparing co-located and serverless architectures, highlighting faster personalized-data paths, serverless advantages under heavy fan-out, and the performance implications of eliminating network hops between services at scale.

Media Coverage

InfoQ examines benchmark results comparing co-located and serverless architectures, highlighting faster personalized-data paths, serverless advantages under heavy fan-out, and the performance implications of eliminating network hops between services at scale.

Renato Losio

InfoQ Staff Editor

Harper Argues Against the Multi-System Stack and Releases 5.2

InfoQ examines benchmark results comparing co-located and serverless architectures, highlighting faster personalized-data paths, serverless advantages under heavy fan-out, and the performance implications of eliminating network hops between services at scale.

Harper Argues Against the Multi-System Stack and Releases 5.2

InfoQ examines benchmark results comparing co-located and serverless architectures, highlighting faster personalized-data paths, serverless advantages under heavy fan-out, and the performance implications of eliminating network hops between services at scale.

Harper Argues Against the Multi-System Stack and Releases 5.2

InfoQ examines benchmark results comparing co-located and serverless architectures, highlighting faster personalized-data paths, serverless advantages under heavy fan-out, and the performance implications of eliminating network hops between services at scale.

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Harper 5.2 attacks database performance on three fronts: a per-worker record cache validated through lock-free atomic version slots, isolated commit scheduling to keep database writes off the application thread, and query planner improvements that route through the shortest available data path.

Blog

Harper 5.2 attacks database performance on three fronts: a per-worker record cache validated through lock-free atomic version slots, isolated commit scheduling to keep database writes off the application thread, and query planner improvements that route through the shortest available data path.

Kris Zyp

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Faster by Doing Less: How Harper 5.2 Engineers Database Performance

Harper 5.2 attacks database performance on three fronts: a per-worker record cache validated through lock-free atomic version slots, isolated commit scheduling to keep database writes off the application thread, and query planner improvements that route through the shortest available data path.

Faster by Doing Less: How Harper 5.2 Engineers Database Performance

Harper 5.2 attacks database performance on three fronts: a per-worker record cache validated through lock-free atomic version slots, isolated commit scheduling to keep database writes off the application thread, and query planner improvements that route through the shortest available data path.

Faster by Doing Less: How Harper 5.2 Engineers Database Performance

Harper 5.2 attacks database performance on three fronts: a per-worker record cache validated through lock-free atomic version slots, isolated commit scheduling to keep database writes off the application thread, and query planner improvements that route through the shortest available data path.

Harper Recognized as an Honorable Mention in the 2026 Gartner® Magic Quadrant™ for Cloud-Native Application Platforms

Harper’s inclusion highlights a broader shift toward integrated cloud-native platforms that unify data, caching, messaging, and application logic. The article explains how Harper’s distributed runtime and managed infrastructure reduce operational complexity, improve performance, and support modern applications and AI agents that require fast, reliable access to state, events, and tools.

News

Harper’s inclusion highlights a broader shift toward integrated cloud-native platforms that unify data, caching, messaging, and application logic. The article explains how Harper’s distributed runtime and managed infrastructure reduce operational complexity, improve performance, and support modern applications and AI agents that require fast, reliable access to state, events, and tools.

Aleks Haugom

Senior Manager of GTM

Harper Recognized as an Honorable Mention in the 2026 Gartner® Magic Quadrant™ for Cloud-Native Application Platforms

Harper’s inclusion highlights a broader shift toward integrated cloud-native platforms that unify data, caching, messaging, and application logic. The article explains how Harper’s distributed runtime and managed infrastructure reduce operational complexity, improve performance, and support modern applications and AI agents that require fast, reliable access to state, events, and tools.

Harper Recognized as an Honorable Mention in the 2026 Gartner® Magic Quadrant™ for Cloud-Native Application Platforms

Harper’s inclusion highlights a broader shift toward integrated cloud-native platforms that unify data, caching, messaging, and application logic. The article explains how Harper’s distributed runtime and managed infrastructure reduce operational complexity, improve performance, and support modern applications and AI agents that require fast, reliable access to state, events, and tools.

Harper Recognized as an Honorable Mention in the 2026 Gartner® Magic Quadrant™ for Cloud-Native Application Platforms

Harper’s inclusion highlights a broader shift toward integrated cloud-native platforms that unify data, caching, messaging, and application logic. The article explains how Harper’s distributed runtime and managed infrastructure reduce operational complexity, improve performance, and support modern applications and AI agents that require fast, reliable access to state, events, and tools.

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Reduce API latency under load with a caching alternative to Redis: an in-process cache that cut response time 100x without adding a new tier.

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Reduce API latency under load with a caching alternative to Redis: an in-process cache that cut response time 100x without adding a new tier.

Austin Akers

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How to Reduce API Latency Under Load Without Adding Another Cache Tier

Reduce API latency under load with a caching alternative to Redis: an in-process cache that cut response time 100x without adding a new tier.

How to Reduce API Latency Under Load Without Adding Another Cache Tier

Reduce API latency under load with a caching alternative to Redis: an in-process cache that cut response time 100x without adding a new tier.

How to Reduce API Latency Under Load Without Adding Another Cache Tier

Reduce API latency under load with a caching alternative to Redis: an in-process cache that cut response time 100x without adding a new tier.

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