For iboss's containerized proxy technology, should we emphasize scalability improvements or concentrate on adding more granular policy controls?

Introduction

The trade-off we're examining today is whether to prioritize scalability improvements or focus on adding more granular policy controls for iboss's containerized proxy technology. This decision is crucial for the product's future direction and its ability to meet evolving customer needs. I'll analyze this trade-off by considering the product context, potential impacts, key metrics, and experimental approaches to guide our decision-making process.

Step 1

Clarifying Questions (3 minutes)

• Based on the current market trends, I'm thinking scalability might be a pressing concern. Could you share more about our current customer feedback regarding system performance under high loads?

Why it matters: Helps prioritize between scalability and granular controls based on immediate customer pain points. Expected answer: Customers are experiencing slowdowns during peak usage. Impact on approach: Would lean towards prioritizing scalability improvements.

• Considering our revenue model, I assume we charge based on user count or data volume. Is this correct, and how might more granular policy controls impact our pricing strategy?

Why it matters: Aligns product development with revenue generation potential. Expected answer: Pricing is tiered based on user count, with add-ons for advanced features. Impact on approach: Could justify focusing on granular controls if it allows for premium pricing.

• Looking at our user segments, I'm curious about the split between enterprise and mid-market customers. What percentage of our revenue comes from large enterprises that might require more granular controls?

Why it matters: Helps tailor the solution to our most valuable customer segment. Expected answer: 70% of revenue from enterprise, 30% from mid-market. Impact on approach: High enterprise revenue would support prioritizing granular controls.

• From a technical standpoint, I'm wondering about the current architecture's scalability limits. What's our current maximum throughput, and how close are we to reaching it?

Why it matters: Determines the urgency of scalability improvements. Expected answer: Current architecture can handle 100,000 concurrent users, nearing 80% capacity. Impact on approach: High utilization would prioritize scalability work.

• Regarding our development resources, what's our current team capacity and skill set distribution between backend optimization and policy control implementation?

Why it matters: Ensures we can execute on the chosen priority effectively. Expected answer: Team is split 60/40 between backend and policy control experts. Impact on approach: Team composition could influence which option is more feasible in the short term.