Multi-Model API Cost Governance with the Inference Router
Running multiple models requires an effective inference router to send designated tasks to the most efficient endpoint. This tutorial builds a working router with three task policies across a SaaS support backend: a low-cost classifier path, a quality-sensitive customer Q&A path, and a reasoning path. You'll learn how to set up a router over the standard OpenAI chat completions endpoint, per-request cost signals readable from the response header, and a session pinning pattern for the Q&A path that keeps KV-cache warm across multi-turn conversations.
Efficient LLM Compression with SparseGPT and Wanda on GPU Cloud
Training trillion-parameter models is expensive, but inference is the ongoing operational cost. Buying larger GPUs is an unsustainable way to handle this growth. LLM compression is therefore becoming critical for inference efficiency. Read how to compress large language models using SparseGPT and Wanda. Compare pruning methods, reduce inference costs, and accelerate deployment on GPU cloud infrastructure.
Server-Side Tools for AI Agents: Architecture, Latency, and When to Switch
Server-Side Tools for DigitalOcean Inference Engine let you add tool execution directly into inference requests. This tutorial demonstrates how server-side tools work in AI agents, how they affect architecture and latency, and when it makes sense to move from client-side tool execution to a server-side approach.
Metrics that Matter with Serverless Inference
Serverless LLM inference has no single performance metric that can reflect performance for all applications. Throughput, latency, reliability, and cost each measure something different, and the right one depends on your workload. This article covers the metrics that actually matter for production serverless inference, what each one measures, and which workloads should care about it.
Java Generics Explained: Benefits, Examples, and Best Practices
Java generics let you write classes, interfaces, and methods that operate on a type specified at compile time rather than at runtime. Our guide covers generic classes, methods, and interfaces, bounded type parameters, wildcards, and type erasure, along with the practices that keep generic code safe and readable. The examples use Java 8+ syntax and compile as shown.
