Cutting Cloud Costs for Mobile and Quant Platforms
Mobile / Quant ResearchMoving a mobile platform to the network edge and right-sizing always-on compute for a research operation, cutting infrastructure spend by more than half.
Overview
A consumer mobile platform and a separate quantitative research operation were both paying enterprise-scale cloud bills for consumer-scale usage, one serving every mobile screen load from origin servers, the other running always-on compute around the clock. Canvas Labs Partners moved the mobile platform to an edge-first CDN model and right-sized the research operation's compute onto serverless, pay-as-you-go workloads.
85% lower per-user infra cost
after moving to the network edge
90% faster client load time
serving from the edge instead of origin
60%+ lower cloud spend
across both platforms combined
Challenge
Two platforms were paying enterprise-scale cloud bills for consumer-scale usage. A consumer mobile platform was serving every mobile screen load from origin servers, and a separate quantitative research operation was running always-on compute around the clock, both carrying costs sized for far more traffic and activity than either platform actually had.
Solution
For the consumer mobile platform, Canvas Labs Partners re-architected the system onto an edge-first CDN model, built on Cloudflare Workers and Cloudflare R2. Compute and caching moved to the network edge, so a screen load no longer meant a round trip back to an origin server every single time.
For the quantitative research operation, running on GCP, Canvas Labs Partners right-sized the compute nodes and optimized the storage tiers, then migrated always-on databases to serverless, pay-as-you-go workloads. Capacity that used to run continuously regardless of load now scales with it instead.
The two platforms needed different fixes because their waste was different. The mobile platform's cost came from routing every request back to an origin server, which an edge-first CDN model solves by moving work to where the user already is. The research platform's cost came from infrastructure that ran whether or not it was needed, which serverless compute solves by billing for actual use instead of provisioned capacity.
Results & benefits
Per-user infrastructure cost on the mobile platform fell 85 percent, and client load time improved 90 percent, once requests stopped routing back to an origin server and started serving from the edge instead. The quantitative research operation's always-on compute, migrated to serverless pay-as-you-go workloads, now scales with actual demand rather than running continuously regardless of it. Across both platforms combined, overall cloud infrastructure spend came down by more than 60 percent, the result of matching cost to usage on two systems that had been paying enterprise-scale bills for consumer-scale traffic.
Customer
Mobile platform & quant research desk
Services
- Trading & quantitative infrastructure engineering
- Edge & CDN architecture
- Cloud cost optimization
Tech stack
About us
Canvas Labs Partners is a software consulting team that designs and ships production agentic AI systems: multi-agent pipelines, tool-calling architectures, and human-governed automation, together with the trading infrastructure, blockchain, mobile, and product engineering those agents plug into. We publish our work as a case ledger rather than a portfolio, so every claim on this site links to the engagement behind it. Engagements follow the same four-step discipline, Map, Bound, Instrument and Govern, whatever the stack, and every entry in the ledger carries a result we are willing to be measured against.
Delivered individually, in a prior role, by one of Canvas Labs Partners’ founding team members. Not by the firm itself.
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