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AI Governance and Observability in Bifrost

AI Governance and Observability in Bifrost

Most teams that run AI in production eventually get the same question from finance. Spending went up last month, and someone wants to know which team, feature, or model caused it. Often there is no clear answer. The numbers exist, but they sit in two systems that were never built

LLM Cost Optimization in Bifrost: Part 1

LLM Cost Optimization in Bifrost: Part 1

Introduction AI costs at enterprises can become expensive quickly. Recent examples show this clearly - Uber reportedly exhausted its annual AI budget only four months into 2026, while Pylon’s CEO said the company’s Anthropic bill was set to jump from $400K to $1.4M a year after crossing 150

Shadow AI Is the Insider Threat You Already Hired

Shadow AI: The Insider Threat You Never Noticed

The most damaging data leak in your organization this year probably won't come from an attacker. It'll come from an employee trying to hit a deadline. Right now, across nearly every company, people are pasting source code, customer records, contracts, and clinical notes into AI tools

Beyond latency-based routing: Adaptive load balancing in Bifrost

Beyond latency-based routing: Adaptive load balancing in Bifrost

Most teams running multi-provider LLM stacks have moved past pure round-robin for traffic distribution across providers and API keys. They've adopted latency-based routing: measure response times, penalise slow backends, shift traffic toward the fast ones. The approach is reasonable, but it has a failure mode

Your Semantic Cache Has a Grey Zone Problem

Your Semantic Cache Has a Grey Zone Problem

If you're running LLMs in production, you've probably built (or thought about building) a semantic cache. The pitch is simple: embed incoming prompts, find near-duplicates, serve the cached response, skip inference. It works. Except when it doesn't. A new paper from Apple introduces