A Series D SaaS Company Was Bleeding Cloud Spend. We Cut Costs 60% and Delivered Real-Time Analytics — in 10 Weeks.
Industry
Technology (SaaS)
Service
Data Analytics & Real-Time Platform
Duration
10 weeks
Team
Agilityx consultants + AI agents
Scale
Multi-terabyte streaming
The Situation
A high-growth Series D SaaS company was facing significant scaling challenges as its user base tripled in eighteen months. While their product was market-leading, their internal analytics infrastructure on a legacy cloud setup was failing. Dashboards were consistently 24 to 48 hours behind reality, making real-time product decisions and customer usage monitoring impossible.
Simultaneously, their platform costs were spiraling out of control due to inefficient query patterns and a lack of proper resource management. They needed a modern data platform on Snowflake that could support high-volume streaming telemetry at multi-terabyte scale with elastic scaling for future petabyte growth while slashing infrastructure spend by over half.
The objective was to achieve sub-second query speeds for self-service dashboards that the entire executive team could trust.
The Approach
Audit(Phase 1)
AI Agents
Identified 'hot spots' in query usage and resource bottlenecks programmatically.
Consultants
Aligned analytics roadmaps with product engineering goals for the IPO track.
Design(Phase 2)
AI Agents
Modeled event-driven streaming architectures to replace legacy batch processes on Snowflake.
Consultants
Co-designed an event-driven streaming architecture with the client's engineering team.
Build(Phase 3)
AI Agents
Generated optimized dbt models and Snowflake clustering strategies.
Consultants
Managed the migration of real-time telemetry pipelines using Kafka and Snowflake.
Optimization(Phase 4)
AI Agents
Autonomous agents continuously monitored compute utilization to right-size clusters.
Consultants
Established cost-governance guardrails to prevent future cloud spend spikes.
Transfer(Phase 5)
AI Agents
Auto-produced technical handbooks and API documentation for internal developers.
Consultants
Mentored the SaaS data team on real-time observability and site reliability.
Traditional vs. Agilityx
| Dimension | Traditional | Agilityx |
|---|---|---|
| Data Freshness | 24-hour batch delay | Real-time (< 60 seconds) |
| Cloud Spend | Unmanaged growth | 60% cost reduction |
| Query Speed | Seconds to minutes | Sub-second latency |
| Deployment | 6+ months | 10 weeks |
Data Freshness
Traditional
24-hour batch delay
Agilityx
Real-time (< 60 seconds)
Cloud Spend
Traditional
Unmanaged growth
Agilityx
60% cost reduction
Query Speed
Traditional
Seconds to minutes
Agilityx
Sub-second latency
Deployment
Traditional
6+ months
Agilityx
10 weeks
The Outcomes
60%
Cost Reduction
Eliminated wasted compute and optimized storage through agent-driven tuning.
Real-Time
Product Insights
Decision-makers now act on data as events occur, not the next day.
Sub-Second
Performance
High-volume analytical queries now return in sub-second latency for executive dashboards.
50%
Less Downtime
The client reduced analytics downtime through proactive monitoring.
"What stood out about Agilityx was how practical and transparent they were. They helped us cut development cycles significantly while giving our teams the confidence to run sub-second analytics."
VP of Engineering
Series D SaaS Company
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