Latest

Top 5 RAG Observability Platforms in 2026

Top 5 RAG Observability Platforms in 2026

TL;DR RAG systems require specialized observability platforms to monitor retrieval quality, generation accuracy, and production performance. This guide covers the five leading platforms in 2026: Maxim AI (full-stack platform with simulation, evaluation, and observability), Langfuse (open-source observability with prompt management), LangSmith (LangChain-focused tracing), Arize (enterprise ML monitoring with LLM

Top 5 AI Gateways for Scaling and Managing Your LLM Apps

Top 5 AI Gateways for Scaling and Managing Your LLM Apps

TL;DR AI gateways are becoming critical infrastructure for production LLM applications, providing unified access to multiple providers, cost control, and enterprise features. This guide covers the top 5 AI gateways: Bifrost for high-performance production deployments with zero-config setup, LiteLLM for multi-provider abstraction with extensive observability, OpenRouter for model marketplace

Top 5 AI Gateways for Optimizing LLM Performance Through Intelligent Routing

Top 5 AI Gateways for Optimizing LLM Performance Through Intelligent Routing

TL;DR AI gateways solve the complexity of managing multiple LLM providers by providing unified interfaces, intelligent routing, and production-grade reliability. This guide compares five leading solutions: Bifrost, Cloudflare AI Gateway, Vercel AI Gateway, Kong AI Gateway, and LiteLLM. Each addresses multi-provider access while offering distinct capabilities for cost optimization,

Top 5 AI Agent Evaluation Tools in 2026

Top 5 AI Agent Evaluation Tools in 2026

TL;DR AI agent evaluation has become critical as autonomous systems move to production. This guide compares the five leading agent evaluation platforms in 2026: Maxim AI for comprehensive simulation, evaluation, and observability; Langfuse for open-source tracing; Arize for ML monitoring with agent support; LangSmith for LangChain-native debugging; and Galileo

Retries, Fallbacks, and Circuit Breakers in LLM Apps: A Production Guide

Retries, Fallbacks, and Circuit Breakers in LLM Apps: A Production Guide

TL;DR: Building production LLM applications requires robust failure handling strategies. Retries automatically recover from transient errors like rate limits and network timeouts. Fallbacks seamlessly switch between providers when the primary option fails. Circuit breakers prevent cascading failures by temporarily blocking requests to unresponsive services. Together, these patterns form the

Top 5 Best LLM Orchestration Platforms in 2026

Top 5 Best LLM Orchestration Platforms in 2026

Table of Contents * TL;DR * Quick Comparison Table * Overview > What is an LLM Orchestration Platform * Detailed Feature Matrix * Platform Profiles * Platforms > Bifrost by Maxim AI * Platforms > LangChain * Platforms > LlamaIndex * Platforms > Haystack * Platforms > Semantic Kernel * Selection Guide > How to Choose * Use Case Matcher * Conclusion

Top 5 Enterprise AI Gateways in 2026

Top 5 Enterprise AI Gateways in 2026

As enterprises move from AI experimentation to production deployment in 2026, the infrastructure layer managing LLM access has become mission-critical. With 40% of enterprise applications now integrated with task-specific AI agents, organizations can no longer afford makeshift solutions for managing multi-provider AI infrastructure. The shift from prototyping to production has

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.

Building a Research Assistant Agent with Bifrost: A Complete Guide to Tool Calling

Building a Research Assistant Agent with Bifrost: A Complete Guide to Tool Calling

Introduction Tool calling transforms static AI models into dynamic, action-capable agents. Instead of just generating text, AI models can interact with external systems - search the web, query databases, read files, and execute business logic. In this comprehensive guide, you'll learn how to build a production-ready Research Assistant

Building Better AI Applications with Bifrost: A Complete Technical Guide for AI Engineers

Building Better AI Applications with Bifrost: A Complete Technical Guide for AI Engineers

Introduction Bifrost by Maxim AI is an open-source, high-performance LLM gateway built in Go that helps AI engineers build production-ready AI applications. This technical guide walks you through Bifrost's architecture, features, and practical implementation patterns to help you leverage its full capabilities. What is Bifrost? Bifrost is an

Best Practices in RAG Evaluation: A Comprehensive Guide

Best Practices in RAG Evaluation: A Comprehensive Guide

Retrieval-augmented generation (RAG) has become the foundation of modern AI applications, powering everything from customer support chatbots to internal knowledge bases. According to industry estimates, RAG systems now power approximately 60% of production AI applications. However, the complexity of RAG pipelines (combining retrieval, context assembly, and generation) makes evaluation critical

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