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speedscale

ROI of Digital Twin Testing: Cut Testing Costs by 50%

Jan 20, 2026 By Ken Ahrens In Speedscale
When engineering leaders review their cloud bills, they often focus on production costs—the infrastructure serving real users, processing real transactions, generating real revenue. But there’s a shadow cost lurking in every cloud environment that often goes unnoticed until it becomes painful: non-production infrastructure.
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keploy

How VPS Architecture Solves The Problem

Jan 20, 2026 By Keploy In Keploy
Virtual private servers approach this differently. They tackle contention head-on through dedicated resource allocation. Instead of letting resources slosh around freely between all sites like shared hosting does, VPS technology carves a physical server into isolated virtual machines. Each one operates independently. Each gets its own slice of CPU cores, RAM, and storage that nobody else can touch.
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speedscale

Mock vs Stub: Essential Differences

Jan 16, 2026 By Nate Lee In Speedscale
When discussing the process of testing an API, one of the most common sets of terms you might encounter are “mocks” and “stubs.” These terms are quite ubiquitous, but understanding exactly how they differ from one another - and when each is the correct method for software testing - is critical to building an appropriate test and validation framework. In this blog, we’re going to talk about the differences and similarities between mocks and stubs.
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speedscale

The CES Hangover: 3 Expensive Hardware Fails That Were Actually Software Problems

Jan 15, 2026 By Alan Mon In Speedscale
The dust has settled on Las Vegas. We saw transparent TVs, cars that drive sideways, and enough “AI-powered” toothbrushes to confuse a dentist. CES is incredible at selling the dream of hardware. The demos are slick, the lighting is perfect, and everything works on the showroom floor. But as engineers, we know the dirty secret of CES: The hardware is the easy part.
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Supercharge your LLM Using Production Data Context

Jan 12, 2026 By Speedscale In Speedscale
Are your LLM coding agents (like Cursor or Claude Code) hallucinating fixes because they don't know what's actually happening in production? In this video, Matt from Speedscale shows you how to bridge the gap between your local IDE and live production traffic using the Model Context Protocol (MCP). Most observability tools just give you telemetry. Speedscale’s MCP server gives your agent the "inner workings" of actual API calls and payloads, so it can check its assumptions against reality. No more "vibe-coding" and hoping it works; let your agent find the 500 errors and rate limits for you.
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