Blog

SGLang Production Monitoring: Complete Guide for AI Engineers

Monitor SGLang in production: RadixAttention architecture, KV cache metrics, prefill/decode throughput, TTFT, Prometheus + Grafana instrumentation, and a frank comparison with vLLM and Ollama.

May 14, 2026 13 min read

LLM Hallucinations: Five Production Detection Methods

A practical guide to monitoring LLM hallucinations in production. Covers deterministic checks, LLM-as-a-judge evaluation, embedding-based drift detection, and the full hallucination monitoring pipeline with alerting thresholds.

May 12, 2026 12 min read

Open Source LLM Monitoring Stack in 2026 - A Practical Guide

Build a production-ready LLM observability stack with OpenTelemetry, Prometheus, Grafana, and Loki — no vendor lock-in, no per-token fees.

LLM Monitoring Dashboard Templates: Grafana + Prometheus

Production-ready Grafana dashboard JSON and Prometheus queries for LLM monitoring. Token throughput, TTFT/TPOT latency, cost attribution, error rates, and context window utilization — all in one template.

Build Your First LLM Monitoring Stack: OTel + Prometheus

A practical guide to instrumenting LLM applications with OpenTelemetry, scraping metrics with Prometheus, and visualizing token costs, latency, and quality signals in Grafana dashboards.

May 12, 2026 14 min read

Agentic AI Infrastructure for DevOps and Platform Engineers

From stateless LLM calls to autonomous multi-step agents — a practical guide to the infrastructure patterns that make agentic AI production-ready.

May 12, 2026 12 min read

LLM Context Window Optimization: Cut Costs Without Sacrificing Quality

A practical guide to reducing LLM inference costs by 40-70% using semantic truncation, context compression, dynamic sizing, and hybrid retrieval — with code examples.

Multi-Modal LLM Monitoring in Production: A Practical Guide

How to monitor vision, audio, and text inputs in multi-modal AI systems. Covers metrics unique to multi-modality, OpenTelemetry instrumentation patterns, and the monitoring stack for production MLLM applications.

May 12, 2026 14 min read

AI Model Monitoring vs Traditional APM in 2026

Four fundamental differences between AI and software monitoring — non-deterministic output, token-based cost, multi-component latency, and stateful context windows.

May 12, 2026 12 min read

LLM Evaluation Frameworks: RAGAS, TruLens, and the Stack

Evaluation is the gap between LLMs working in demos and LLMs working in production. Here is the complete framework stack: RAGAS for retrieval-grounded assessment, TruLens for causal attribution tracking, and the architecture patterns that make automated LLM evaluation reliable enough to gate deployments.

May 12, 2026 14 min read

Prompt Injection Attacks: Detection Methods and Prevention Strategies

Prompt injection is an active threat in production AI systems. Here are the detection methods, prevention strategies, and the defense-in-depth architecture you need to stay protected.

Monitoring LLM Hallucinations 2026: A Practical Guide for AI Engineers

Hallucinations are the blind spot of LLM monitoring. Here is how to detect, measure, and reduce them in production — with rule-based checks, LLM-as-a-judge, and embedding drift detection.

Agentic Observability: Multi-Agent LLM Monitoring

A practical guide to observability for agentic AI systems — step-level tracing, cost accounting, reliability monitoring, and the four-layer stack you need to debug production agents.

vLLM Production Monitoring 2026: A Practical Stack Guide

GPU cache utilization, KV cache hit rate, TTFT/TPOT metrics, and a complete Prometheus + Grafana monitoring setup for vLLM inference servers — updated for v0.19.

May 12, 2026 11 min read

LLM Latency Monitoring 2026: TTFT, TPOT, and the Metrics That Matter

Every millisecond your users wait for an LLM response, engagement drops. Here is how to measure, monitor, and fix LLM latency with TTFT, TPOT, and the metrics that actually matter in production.

May 08, 2026 12 min read

Observability

Prometheus vs Grafana 2026: The Practitioner's Guide

Your Kubernetes pods are eating through budget, your on-call is drowning in alert fatigue, and your dashboards show 'unknown' for half your services. Here is exactly how to fix your observability stack using Prometheus and Grafana — and when to use each one alone.

May 08, 2026 11 min read

LLMOps Observability: Latency, Hallucinations, and Drift

A blueprint for LLMOps observability: why HTTP 200 is a lie for LLM apps, the three pillars of LLM health (latency, quality, reliability), and how to implement an LLM Health Score for production AI systems.

Apr 11, 2026 5 min read

Multimodal LLM Cost Optimization 2026: Vision and Audio AI

Practical strategies for reducing multi-modal LLM costs. Covers vision token optimization, audio chunking, cross-modal batching, model routing, and real cost benchmarks for GPT-4V, Claude 3.5, Gemini Pro, and LLaVA in 2026.

AWS Savings Plans vs Reserved Instances: 2026 FinOps Guide

Save up to 72% on AWS GPU instances with Savings Plans vs Reserved Instances. Includes coverage analysis, Auto-Refit strategy, and GPU-specific recommendations for AI workloads.

Cutting GPU Costs 40% with KEDA Queue-Depth Autoscaling for vLLM

We switched our vLLM inference pods from CPU-based HPA to KEDA queue-depth scaling and dropped p99 latency 60% while cutting GPU spend 40%. The exact KEDA config and results inside.

vLLM vs Triton Inference Server in 2026: A Production Comparison

Compare vLLM and NVIDIA Triton Inference Server for production LLM inference. Covers throughput benchmarks, latency SLAs, quantization support, multi-model serving, and when to use each.

AI Incident Postmortem Template: Four-Question Framework

When your AI system fails, you need answers fast. This copy-paste postmortem template uses a proven four-question framework — with a real medical AI incident example and a production runbook checklist your team can use immediately.

Apr 11, 2026 10 min read

Kubernetes GPU Operator: A Production Setup Guide

GPU pods not scheduling? Device plugin failing at 2am? This guide walks through the complete NVIDIA GPU Operator stack — Device Plugin, DCGM Exporter, and GPU scheduling — with the troubleshooting commands that actually fix production issues.

Tooling

DevOps Supply Chain Security 2026: CPU-Z Compromise Lessons

The April 2026 CPU-Z/HWMonitor supply chain attack exposed how even trusted developer tools can become attack vectors. Here's what infrastructure and DevOps teams need to know about software provenance, SBOM, and supply chain hardening.

Observability

OpenTelemetry for AI Inference: Tracing LLM Pipelines in Production

How to instrument LLM inference pipelines with OpenTelemetry — from prompt injection to token streaming, from model serving to downstream tool calls, using OTel's AI semantic conventions.

AI Agent Reliability Monitoring 2026: Failure Modes + Observability

A practical guide to monitoring autonomous AI agents in production — covering CrewAI, AutoGen, LangChain process managers, four key failure modes, OpenTelemetry tracing, and Grafana dashboards for AI agent reliability.

Datadog Migration: From $15K/mo to $3K/mo — The Step-by-Step Playbook

Three client migrations from Datadog to Grafana + Prometheus + Tempo. Average savings: 80%. This playbook covers the exact sequence (don't skip order), the billing traps that inflate your new stack, and the three dashboards you need first.

Apr 11, 2026 18 min read

LiteLLM Production Monitoring 2026: Gateway + Cost Tracking

Monitor LiteLLM in production: unified API gateway patterns, cost tracking by model and team, relay-proxy metrics, and the complete observability stack for multi-provider LLM infrastructure.

LLM Model Drift Detection 2026: Monitoring AI Behavior Degradation

A practical guide to detecting and monitoring LLM model drift in production. Covers statistical drift detection, embedding-based methods, automated evaluation pipelines, and the tools you need to catch AI behavior degradation before it impacts users.

LLM Incident Postmortem 2026: What Production AI Failures Taught Us

Real incident retrospectives from legal RAG, medical AI, and customer support AI failures. Learn the four-question AI postmortem framework, the failure modes unique to non-deterministic systems, and the runbook patterns that prevent repeat incidents.

LLMOps Platform Comparison 2026: Complete Guide to Leading Tools

The definitive 2026 guide to LLMOps platforms. Braintrust, Arize AI Phoenix, Weights & Biases, LangSmith, Promptfoo, and Guardrails AI compared on evaluation, observability, security, pricing, and integration.

Apr 11, 2026 16 min read

SRE Best Practices for AI/LLM Systems in 2026: A Practical Playbook

A practical SRE playbook for operating AI and LLM systems in production. Covers AI-specific SLOs, SLIs, error budgets, incident response runbooks, on-call procedures, and chaos engineering for AI workloads.

vLLM vs TGI vs TensorRT-LLM on H100s: The Benchmarks

vLLM (50 req/s) vs TGI (80) vs TRT-LLM (200) on identical H100s: which engine hits 200 req/s, which tops out at INT8, and which demands CUDA expertise before you ship. The benchmark data that guided our production stack decision.

Terraform vs pulumi for AI Infrastructure: A Practical Decision Guide

Comparing Terraform and Pulumi for AI/ML infrastructure — dynamic GPU clusters, Kubernetes, multi-cloud routing, and the programmatic vs declarative trade-off for modern ML platforms.

LLM Security Hardening 2026: A Practical Defense-in-Depth Guide

Prompt injection, jailbreaking, and model extraction threaten production AI systems. Here are the six defense layers every AI engineer needs in their production stack.

Helicone vs Portkey vs LangSmith: LLM Observability 2026

Three leading LLM observability platforms head to head — tracing depth, evaluation, guardrails, gateway routing, and pricing. Which one belongs in your production stack?

The Rise of eBPF 2026: A New Era for System Observability

eBPF is rewriting the rules of Linux observability. Learn how extended Berkeley Packet Filter programs enable kernel-level monitoring without instrumentation, and why it matters for AI infrastructure.

Tooling

Datadog Alternatives 2026: 5 Cost-Effective Picks

Datadog's pricing at scale is pushing engineering teams to explore alternatives. Here are the 5 monitoring platforms that deliver better value for LLM inference, Kubernetes, and cloud cost observability.

Apr 11, 2026 11 min read

Kubernetes GPU Scheduling for ML Workloads: A Practical Guide

Schedule GPUs in Kubernetes for ML training and inference. Covers time-slicing, node pooling, gang scheduling, device plugins, and the k8s-gpu-operator setup for production ML workloads.

OpenClaw Reliability: Production AI Agent Patterns

A senior SRE perspective on OpenClaw failure modes in AI agent production environments, with hardening patterns and monitoring strategies for DevOps and AI engineers.

MCP Monitoring: Observability for Model Context Protocol Servers

A practical guide to monitoring MCP (Model Context Protocol) servers in production. Covering metrics, dashboards, alerting rules, and open-source tooling for 2026.

Apr 11, 2026 11 min read

The State of AI Infrastructure in 2026: From Hype to Production

A practical analysis of the AI infrastructure landscape in 2026 — GPU providers, inference frameworks, SLM adoption, and the FinOps reality check that follows scale.

Apr 10, 2026 12 min read

GPU Monitoring for AI Inference: A Practical Guide for 2026

Monitor GPU utilization, VRAM, temperature, and power draw for AI inference. Covers DCGM, Prometheus, Kubernetes GPU scheduling, MIG partitioning, and cost optimization.

Apr 10, 2026 15 min read

RAG Observability 2026: Measuring What Matters in Production Retrieval

A practical guide to monitoring RAG pipelines in production — retrieval precision, context utilization, answer faithfulness, embedding drift, and the metrics that actually predict user satisfaction.

Apr 10, 2026 12 min read

Kubernetes Cost Optimization: Cutting Cloud Bills in Half

Practical strategies to cut Kubernetes spend by 40-60%: right-sizing nodes, Spot instance mixing, cluster autoscaling, namespace quotas, storage tiering, and Kubecost for visibility.

Apr 9, 2026 12 min read

LLM Observability: A Complete Implementation Guide for Production AI

A practical guide to implementing LLM observability in production. Covers the 8 critical signals, OpenTelemetry instrumentation architecture, and the monitoring stack your AI applications need at scale.

Apr 9, 2026 14 min read

Kubernetes

Kubernetes Monitoring Stack: Prometheus + Grafana + eBPF

Prometheus, Grafana, kube-state-metrics, and eBPF — a production-ready Kubernetes observability stack for 2026. Includes Grafana dashboard JSON and PromQL queries.

Apr 9, 2026 18 min read

Vector Database Comparison 2026: Pinecone vs. Milvus vs. Weaviate

A rigorous comparison of the three dominant vector databases for production RAG applications — covering performance, scalability, developer experience, cost, and operational trade-offs.

Apr 8, 2026 13 min read

Observability

The State of Observability in 2026: Trends and Tech

From semantic observability to AI-driven autonomous incident response — a comprehensive look at how monitoring has evolved in the age of agentic AI.

Apr 8, 2026 14 min read

Cloud FinOps in 2026: From Chaos to Controlled Spend

A practical guide to cloud waste reduction without sacrificing performance — covering tagging strategies, reserved capacity, and cost-aware architecture.

Apr 8, 2026 10 min read

LLM FinOps 2026 — Cutting Your AI Bill Without Cutting Performance

A practical guide to reducing LLM inference costs by 60-80% using tiered model routing, semantic caching, prompt optimization, and self-hosting — without measurable accuracy loss.

Apr 8, 2026 11 min read

Monitoring the Unseen: Observability for AI/ML Pipelines

LLMs, vector databases, and RAG pipelines introduce new failure modes. Here is how to instrument your AI stack for production reliability.

Apr 8, 2026 9 min read