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cloudzero

How To Calculate Your OpenAI Cost Per API Call (And Why It Matters Now)

Jan 12, 2026 By Lyne Carolyne In CloudZero
OpenAI doesn’t bill per feature, per customer, or per transaction. It bills per token, across multiple models, with usage patterns that can change by the hour. As a result, two API calls that support the same feature can have very different costs. Without a clear way to translate token-level pricing into something product, engineering, and finance teams can reason about, AI spend becomes difficult to forecast and harder to control.
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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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Operational Risk Management in High-Stakes Decision Environments

Operational Risk Management in High-Stakes Decision Environments

Jan 12, 2026 By OpsMatters In OpsMatters
In high-stakes environments, every choice carries weight. Whether it is a complex financial process, a real-time cybersecurity response, or a tightly regulated operational workflow, small missteps can rapidly evolve into major failures. Organizations increasingly rely on integrated riskmanagement strategies that blend human judgment with technology. The goal is simple: reduce uncertainty before it becomes costly. But the path to that goal is rarely straightforward.
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speedscale

Let Your LLM Debug Using Production Recordings

Jan 11, 2026 By Matthew LeRay In Speedscale
Modern LLM coding agents are great at reading code, but they still make assumptions. When something breaks in production, those assumptions can slow you down—especially when the real issue lives in live traffic, API responses, or database behavior. In this post, I’ll walk through how to connect an MCP server to your LLM coding assistant so it can pull real production data on demand, validate its assumptions, and help you debug faster.
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Komodor

AI SRE in Practice: Resolving GPU Hardware Failures in Seconds

Jan 11, 2026 By Itiel Shwartz In Komodor
When a pod fails during a TensorFlow training job, the investigation usually starts with the obvious questions. The answers rarely come quickly, especially when the failure involves GPU hardware that most engineers don’t troubleshoot regularly. This scenario walks through an actual GPU hardware failure and shows how AI-augmented investigation changes both the time to resolution and the expertise required to handle it.
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Cloud Strategy for 2026: the Year of Repatriation, Resilience, and Regional Rebalancing

Cloud Strategy for 2026: the Year of Repatriation, Resilience, and Regional Rebalancing

Jan 10, 2026 By Pulsant In Pulsant
This year is set to be a pivotal year for cloud strategy, with repatriation gaining momentum due to shifting legislative, geopolitical, and technological pressures. This trend has accelerated, with a growing focus on data sovereignty. These challenges have set the stage for 2026 to be the year of repatriation, resilience, and regional rebalancing. Here, Rob Coupland, Chief Executive Officer at Pulsant, offers his insights.
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upsun

AI coding assistants are only as good as the context you give them

Jan 9, 2026 By Upsun In Upsun
AI coding assistants have quickly become part of everyday development. Teams now rely on them to explain unfamiliar code, suggest configuration files, debug errors, and accelerate delivery across the stack. But as these tools move from experimentation into real production workflows, a consistent pattern is emerging: AI breaks down at the platform boundary.
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germain ux

Beyond the Blue Link: UX Patterns for Google's AI Overviews, AI Mode & Answer Engines

Jan 9, 2026 By Germain UX Team In Germain UX
The blue link is dying—but not in the way we expected. When Google’s AI Overviews began appearing at the top of the search results page, the SEO community panicked. Publishers watched click-through rates plummet. The Pew Research Center confirmed their fears: searchers who encounter an AI summary are half as likely to click on traditional search results (8% vs. 15%).
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