OptScale AI Blog

For the second time this year, attackers slipped malware into LiteLLM releases on PyPI. Why it’s a process problem, not an open-source one, and what to ask your AI gateway vendor

Coding agents re-read the same build logs on every turn. OptScale Kompressor strips that repetition without breaking the prompt cache: 22.7% off the real bill on Terminal-Bench 2.1, about twice Headroom's savings

The AI ROI formula fits on one line. The inputs are where it breaks: hidden costs, missing baselines, self-reported time savings. What goes into the calculation, which KPIs count, and five checks before you present the number

80% of people say AI makes them more productive; only 37% of companies see it in EBIT. Where AI impact on business really shows up – compute, efficiency, output quality, and risk – how it differs by industry, and how to track it

In March 2026, a poisoned build pipeline turned the most trusted component in thousands of AI stacks into a credential harvester. What stolen gateway keys fetch on the resale market, why the bill arrives before the alert, and why the traffic an attacker runs on your keys is legally yours

A $0.02 cap admitted $0.225 of spend and reported it correctly. An org-wide MCP ban logged Passed and let the tool run. Six reproduced defects from one open-source tracker, what they say about the difference between monitoring and enforcement, and five questions to ask before a gateway carries either

Nothing errored and the average looks healthy, but the run was slow and nobody can say which part. A five-zone ledger for locating agent latency – and why the worst delays belong to no span at all.

Every gateway can tell you what AI spend cost. None can tell you what it produced. How to join token spend to merged PRs, features, and teams – with cohort framing and confidence labels that survive a budget review.

An open-source proxy costs nothing to license and a great deal to operate. What LiteLLM's 2026 CVEs say about who should own the gateway layer – and what to run instead.

Traditional chargeback assumes one resource, one owner. AI agents break that. How virtual keys make every model call traceable to a team, a department, or a single agent

An agent stuck in a loop doesn't crash. It returns 200s and quietly burns budget until someone opens the billing dashboard. Three named failure patterns, the four metrics that catch them, and where AI agent observability stops, and enforcement has to start.

Shadow AI agents are already in production – built by product teams, embedded by vendors, deciding at runtime. Here's why your existing controls miss them, and where governance starts.