About the Customer: Hussle Technology

Hussle Technology is a global leader in direct sales and field-sales enablement software, empowering brands and their distributed seller networks with tools to sell smarter, faster, and at scale. The platform centralizes content management, learning and onboarding, AI-driven task automation, live selling, real-time analytics, and engagement tools into a single configurable system designed specifically for direct selling and affiliate organizations.

Hussle’s solutions help field reps, affiliates, and sales teams increase conversion by delivering the right content at the right time, guiding sellers toward high-value actions, and simplifying follow-up and retention. With AI-powered features, such as in-app assistants and automated recommendations, Hussle turns data and seller activity into actionable insights, enabling teams to grow more efficiently without sacrificing usability or performance. The platform serves hundreds of brands worldwide and continues to innovate with features that enhance productivity, engagement, and revenue generation across the direct sales ecosystem.

As Hussle Technology’s direct sales enablement platform grew, so did the pressure on its MySQL infrastructure. Seasonal spikes in customer activity drove significant increases in queries, pushing the primary database toward saturation during peak revenue windows.

For Hussle, performance during peak season wasn’t optional. When sales activity surges, the database must keep up.

The Challenge: Unpredictable Peak Load on MySQL

During high-traffic periods, users were generating sustained database load on the primary instance, forcing Hussle to manually increase the number of read replicas, often manually, and at high-costs to ensure the database was able to handle the load.

Some insights from the usage patterns at Hussle

• Key customers consistently drove continuous Average Active Sessions (AAS) during peak windows (not shown above)
• This load was continuous, not bursty
• Headroom on the primary database was shrinking during critical seasonal demand

The sustained nature of the workload during peak revenue periods increased operational risk and reduced buffer capacity for unexpected spikes.

Scaling replicas further would have increased cost and operational complexity. Hussle needed a way to remove peak-time pressure from the primary without rewriting applications or re-architecting their stack.

The Solution: Automatic caching using QueryPilot

Hussle deployed Readyset QueryPilot alongside AWS RDS MySQL as a wire-compatible query acceleration layer. Deployed as a MySQL endpoint, Readyset allowed Hussle to redirect database traffic without modifying application code or rewriting queries while operating transparently alongside the primary database.

By automatically identifying read-heavy and repetitive query patterns, Readyset QueryPilot was seamlessly able to redirect query workloads to the cache.

This allowed the majority of the database traffic to be absorbed before reaching the primary, eliminating the need to scale additional read replicas for seasonal demand.

Over the course of four weeks, Readyset QueryPilot was able to identify and automatically cache high-impact workloads for almost 1700 queries spread across 11 different databases.

The Results: Database Load Reduced to Near Zero

The impact was immediate and measurable. Prior to deploying Readyset, Hussle users were generating high sustained load on the primary MySQL database during peak periods.

After deployment, those same workloads dropped to effectively zero AAS on the primary. Read-heavy traffic was almost entirely offloaded to Readyset, restoring critical headroom during peak-season demand. Across all 11 monitored customers, database load shifted from steady, continuous activity to near-zero utilization post-deployment.

As a result, Hussle eliminated the need to scale additional read replicas to support peak demand and materially reduced the risk of saturation during revenue-critical windows.

Scaling Without Re-Architecture

By placing Readyset QueryPilot transparently in front of MySQL, Hussle was able to:

• Reduced peak-season database pressure
• Avoided additional replica scaling
• Simplified operational management
• Preserved application compatibility

Readyset QueryPilot now operates as a transparent performance layer in Hussle’s stack, ensuring their MySQL infrastructure remains stable and predictable, even during the highest-demand periods.

Interested in Readyset? Please reach out to sales@readyset.io or Book a Demo!