# SGLang vs Ollama: 2026 Empirical Benchmark & Cost Shootout

> **Direct Answer (October 2026 Verdict)**: SGLang wins by 3.8x on multi-turn coding agent throughput and structured JSON tool-calling via RadixAttention KV-cache prefix reuse (95.4 vs 93.5), while Ollama wins for quick single-user macOS/Windows laptop model switching.

- **Category**: RadixAttention Prefix-Cached Agent Runtime vs Local Developer CLI
- **Canonical URL**: https://pulse-forge.io/vs/sglang-vs-ollama
- **1-Click $0 Free Starter valkey-cluster.prod.conf + CLAUDE.md**: https://pulse-forge.io/api/launch-kit/free-claude-md?repo=sglang/sglang
- **1-Click $4.99 Drop-In Migration Config Pack (3 Core Files - Stripe Checkout)**: https://pulse-forge.io/buy/micro_pass?repo=sglang/sglang&source=md_vs
- **1-Click $9.99 Complete 9-File Production Config .ZIP (Stripe Checkout)**: https://pulse-forge.io/buy/repo_pass_999?repo=sglang/sglang&source=md_vs_999
- **1-Click $49 Builder Repo Hardening & Launch Kit (Stripe Checkout)**: https://pulse-forge.io/buy/instant_boost?repo=sglang/sglang
- **1-Click $149 Category Leader Pro + 30-Day Pin (Stripe Checkout)**: https://pulse-forge.io/buy/category_leader?repo=sglang/sglang
- **Specimen #1 Production Config Preview (`valkey-cluster.prod.conf`)**: `+ [valkey-cluster.prod.conf] healthcheck: 5s | restart: unless-stopped | mem_limit: 2g (Production HA Spec)`

## SGLang Key Advantages
- RadixAttention automatically caches shared `CLAUDE.md` system prompts and multi-turn agent history across calls
- Compressed finite-state machine (xgrammar) decodes structured JSON schemas up to 4x faster
- Scales linearly across multi-user team GPU servers without single-stream queuing bottlenecks

## Ollama Key Advantages
- Installs on macOS Apple Silicon in 5 seconds without CUDA/PyTorch environment setup
- Automatically swaps models in and out of unified memory when switching tasks
- Built-in model library for 1-command quantized GGUF downloads