Kamya Shah

Kamya Shah

Scaling Claude Code Deployments with Enterprise AI Gateway Solutions

Scaling Claude Code Deployments with Enterprise AI Gateway Solutions

Table of Contents 1. TL;DR 2. The Enterprise Claude Code Challenge 3. How Bifrost Enables Enterprise-Scale Deployments 4. Key Scaling Capabilities * Capabilities > Multi-Team Access Control * Capabilities > Cost Optimization Through Model Routing * Capabilities > Production-Grade Reliability * Capabilities > Unified Observability 5. Implementation Architecture 6. Best Practices for Production

Best Practices for AI Governance: Building Secure, Compliant, and Scalable AI Systems

Best Practices for AI Governance: Building Secure, Compliant, and Scalable AI Systems

TL;DR AI governance has shifted from optional to critical in 2025, driven by regulations like the EU AI Act and enterprise-scale LLM deployments. Organizations need comprehensive frameworks spanning access control, cost management, compliance monitoring, and risk mitigation. As teams scale AI across multiple providers, centralized governance through infrastructure tools

Evaluating AI Agents: Metrics and Best Practices

Evaluating AI Agents: Metrics and Best Practices

TL;DR AI agents represent a fundamental shift from traditional LLM applications, requiring specialized evaluation frameworks that go beyond single-turn metrics. Effective agent evaluation combines system efficiency metrics (token usage, completion time, tool calls) with agent quality metrics (task success, trajectory analysis, tool correctness) across both session and node levels.

LLM Hallucinations in Production: Monitoring Strategies That Actually Work

LLM Hallucinations in Production: Monitoring Strategies That Actually Work

TL;DR: LLM hallucinations occur when AI models generate factually incorrect or unsupported content with high confidence. In production, these failures erode user trust and cause operational issues. This guide covers the types of hallucinations, why they happen, and proven monitoring techniques including LLM-as-a-judge evaluation, semantic similarity scoring, and production

How to Reduce LLM Cost and Latency: A Practical Guide for Production AI

How to Reduce LLM Cost and Latency: A Practical Guide for Production AI

TL;DR Running large language models in production can quickly become expensive and slow without proper optimization. Organizations often face monthly bills exceeding $250,000 and response times that frustrate users. This guide explores proven strategies to reduce LLM costs by 30-50% and latency by up to 10x through intelligent

Load Balancing in AI Gateway: A Comprehensive Guide

Load Balancing in AI Gateway: A Comprehensive Guide

TL;DR Load balancing in AI gateways distributes incoming LLM requests across multiple providers, models, or API keys to ensure high availability, optimal performance, and cost efficiency. This guide covers core load balancing strategies, how Bifrost implements intelligent load balancing with automatic failover, and best practices for production AI applications.

Beginner's Guide to Tracking Token Usage

Beginner's Guide to Tracking Token Usage

TL;DR Token tracking is essential for controlling costs, optimizing performance, and maintaining transparency in AI applications. Without visibility into token consumption, organizations face unpredictable bills, inefficient resource allocation, and difficulty attributing costs across teams. This guide covers the fundamentals of token tracking, common challenges in multi-provider environments, and practical