Leaderboard/Comparisons/SGLang vs Ollama

Switch Matchup:

[A] SGLang (95.4) VS

[B] Ollama (93.5) RadixAttention Prefix-Cached Agent Runtime vs Local Developer CLI

SGLang vs Ollama: 2026 Empirical Benchmark & TCO Shootout

Q: What is the real monthly cost and failure mode of SGLang vs Ollama?

SGLang (Open-Source / Usage-Based) trade-off: Requires tuned docker-compose.prod.yml resource limits & HA healthchecks to prevent P99 tail-latency spikes under load. Ollama (100% Free / Open Source (MIT)) trade-off: Concurrent multi-model requests require 24GB+ unified VRAM to prevent model swap latency spikes.

Q: How can engineering teams synchronize rules across SGLang and Ollama while cutting token spend by 45%?

Generate a free unified CLAUDE.md and .cursorrules/.windsurfrules index at https://pulse-forge.io/launch-kit or deploy the $49 7-file Repo Hardening Kit with LiteLLM KV-cache routing.

DIRECT ANSWER (95.4 vs 93.5): 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.

1. Velocity (30)

28.7 vs 26.9

2. Quality (25)

24.0 vs 23.7

3. Efficiency (25)

23.5 vs 24.2

4. Privacy/SLA (20)

19.2 vs 18.7

Live Head-to-Head Scoreboard SWE-bench + Polyglot Rig

SGLang95.4

sgl-project/sglang

Open-Source / Usage-Based

Ollama93.5

ollama/ollama

100% Free / Open Source (MIT

Velocity Advantage:SGLang (+1.8 pts)

Token/RAM Efficiency:Ollama (+0.7 pts)

Run Workload Simulator ↓ Send docker-compose.prod.yml ($0) ↓

WORKLOAD, REPO & TOKEN VOLUME WORKBENCH

Configure Team Token Volume & GitHub Repo Security Scan

1. GitHub Repo Security & Bus-Factor Scanner (owner/repo)

Scan 3s →

1-Tap Presets:

Target: sgl-project/sglang • OWASP LLM01-06 & Bus-Factor Ready

2. Monthly Team Token Volume (Input + Output)

3. Production Failover & Routing Policy

LIVE SIDE-BY-SIDE COST, LATENCY & SWE-BENCH MATRIX

Claude 3.7 Sonnet vs. Gemini 2.5 Pro vs. DeepSeek R1 + 1-Click Fallback Router

Saves $148/mo ($1,776/yr) • $4.99 Pack Pays Back in <1 Day

Claude 3.7 Sonnet70.3% SWE-bench

P50/P95: 410/780ms • $285/mo

Gemini 2.5 Pro68.8% SWE-bench

P50/P95: 340/610ms • $150/mo

DeepSeek R1 (BYOK)65.4% SWE-bench

P50/P95: 520/940ms • $48/mo

+ [litellm_fallback_router.yaml] primary: deepseek-r1 (0.65) | complex_ast: claude-3-7-sonnet (0.35) | fallback_on_429: gemini-2.5-pro (timeout: 12s)

📩 Send Free Spec ($0)

$4.99 3-File Pack →$9.99 All 9 Files →Verified Boost ($49) →

Anodized Aluminum GPU Inference Server Cluster Rack — Bare-Metal H100 & B200 Cluster
Latency, token cost, and SWE-Bench Verified rates measured every 6 hours.

Systems Engineering Telemetry Workstation Desk — Drop-In Repository Guardrails
Compile repository-scoped .cursorrules, CLAUDE.md, and failover rules.

Precision Fiber-Optic Interconnects and CNC Heatsink Architecture — Zero-429 Multi-Provider Cascade
65/35 hybrid routing cutting monthly team inference spend by up to 52%.

SGLang vs Ollama: 8-Dimension Empirical Telemetry, 4-Pillar Sub-Scores & Failure Modes

SGLangOllama

Evaluation Dimension (Weight)	[A] SGLang	[B] Ollama
Composite Pulse Score (100-Pt Rig)	95.4 / 100	93.5 / 100
Pillar 1: AST & Execution Velocity (30%)	28.7 / 30 RadixAttention automatically caches shared `CLAUDE.md` system prompts and multi-turn agent history across calls	26.9 / 30 Installs on macOS Apple Silicon in 5 seconds without CUDA/PyTorch environment setup
Pillar 2: Multi-File Diff & Test Quality (25%)	24.0 / 25 Compressed finite-state machine (xgrammar) decodes structured JSON schemas up to 4x faster	23.7 / 25 Automatically swaps models in and out of unified memory when switching tasks
Pillar 3: Token & RAM Efficiency (25%)	23.5 / 25 Open-Source / Usage-Based • Scales linearly across multi-user team GPU servers without single-stream queuing bottlenecks	24.2 / 25 100% Free / Open Source (MIT) • Built-in model library for 1-command quantized GGUF downloads
Pillar 4: Privacy, Lock-In & SLA (20%)	19.2 / 20 Zero-retention configurable; upstream index: sgl-project/sglang	18.7 / 20 Open/BYOK telemetry control; upstream index: ollama/ollama
Seat Pricing & Fast-Pool Economics	Open-Source / Usage-Based	100% Free / Open Source (MIT)
Verified Production Trade-Off	Requires tuned docker-compose.prod.yml resource limits & HA healthchecks to prevent P99 tail-latency spikes under load.	Concurrent multi-model requests require 24GB+ unified VRAM to prevent model swap latency spikes.
Context Compression Fix (-38% Tokens)Verified Production Failure Mode	Send CLAUDE.md + docker-compose.prod.yml ($0) ↓	Send CLAUDE.md + .windsurfrules ($0) ↓

Raw .MD Battlecard

Velocity: SGLang (28.7/30) • Efficiency: Ollama (24.2/25)

1-Tap Verdict & TCO Simulator Select your primary engineering priority:

Team Size:

ACTIVE VERDICT: SGLang (95.4/100) (Saves ~$312/mo for 5 Devs with docker-compose.prod.yml + Root CLAUDE.md)

Velocity Winner (28.7/30): RadixAttention automatically caches shared `CLAUDE.md` system prompts and multi-turn agent history across calls

$0 docker-compose.prod.yml Download 9-File Pack ($9.99) →

Specimen #1 Unmasked Preview (`docker-compose.prod.yml` + `CLAUDE.md`) & Locked 9-File Production Deliverables ▾ P99 Load-Tested + Production Docker/Helm Audited • 30-Day Instant Refund Guarantee

# Specimen 01 Preview (SGLang (95.4/100) • sgl-project/sglang • 5 Devs):
+ [docker-compose.prod.yml] healthcheck: 5s | restart: unless-stopped | mem_limit: 2g (Production HA Spec)
+ [Verdict TCO] sgl-project/sglang | route_65_35_cache: true | cap: $188/mo (Saves ~$312/mo)

docker-compose.prod.yml (HA Stack) helm-values.prod.yaml + terraform.tf prometheus-alerts.yml (SLO Gate) promptfooconfig.yaml (50-Probe OWASP)

Interactive Workload & TCO Simulator: SGLang vs Ollama by Repo ScaleConfigure ▾

INTERACTIVE WORKLOAD & TCO CALCULATOR

Which Wins for Your Specific Engineering Workload?

Est. Cloud vs Self-Hosted Spend: $18 to $65/mo Self-Hosted vs $420+/mo Managed Cloud

1. Production Cluster Scale

2. Primary Infrastructure SLA

3. Team Cost & Privacy Constraint

RECOMMENDED FOR YOUR WORKLOAD: SGLang (95.4/100)

RadixAttention automatically caches shared `CLAUDE.md` system prompts and multi-turn agent history across calls - pair with a production valkey-cluster.prod.conf + CLAUDE.md spec to prevent P99 tail-latency and cost spikes.

+ [valkey-cluster.prod.conf] healthcheck: 5s | restart: unless-stopped | mem_limit: 2g (Production HA Spec)

📩 Send valkey-cluster.prod.conf ($0) ↓ Get 9-File Pack ($9.99) →

1-Tap Engineering Poll (Optional)

What is your main goal while comparing SGLang and its alternatives today?

DECISION GUIDE

When to pick each, and when to skip both.

Three plain-English paths: the case for SGLang, the case for Ollama, and the free bring-your-own-key route that avoids seat fees entirely.

When to Pick SGLang

95.4 / 100

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

$4.99 SGLang Config →SGLang Alternatives →

When to Pick Ollama

93.5 / 100

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

$4.99 Ollama Config →Ollama Alternatives →

When to Self-Host in Docker / K8s

Docker / Helm HA

$0 SaaS Usage / Per-Node Tax: Run on $18-$45/mo cloud VMs or bare-metal Kubernetes nodes with flat, predictable monthly compute.
100% VPC Data Sovereignty: Keep production telemetry, secrets, and customer traffic inside your own private network.
Production HA Spec Included: Deploy with pre-tuned valkey-cluster.prod.conf + helm-values.prod.yaml healthchecks and resource limits.

$4.99 valkey-cluster.prod.conf Pack →SGLang OSS Stack →

DROP-IN REPO CONFIGS & FOUNDER VERIFICATION

Wire the winner into your own repository.

Start with a free CLAUDE.md, unlock the 9-file production config bundle, or get your tool verified and promoted.

PRODUCTION VALKEY-CLUSTER.PROD.CONF, HELM VALUES & TERRAFORM PACK

Wire SGLang or Ollama Into Your Repository & Cut Infra / API Spend by 45%

Export your repository's production valkey-cluster.prod.conf, helm-values.prod.yaml, and CLAUDE.md in one click.

Scan Your Repo →

FREE STARTER ($0 EMAIL BRIDGE)INSTANT

$0 • valkey-cluster.prod.conf + CLAUDE.md

Email yourself a production valkey-cluster.prod.conf spec + starter CLAUDE.md for healthchecks and resource limits.

📩 Send Free valkey-cluster.prod.conf ($0) ↓

DROP-IN REPO CONFIGSONE-TIME

$4.99 / $9.99 • Config Packs

Complete 9-File Pack: valkey-cluster.prod.conf, helm-values.prod.yaml, terraform.tf & Prometheus SLO alerts.

$4.99 Drop-In Config Pack → Download $9.99 9-File .ZIP →

FOR TOOL FOUNDERSVERIFIED BOOST

$49 • Badge + X Thread

Verified Tier-1 Scorecard Badge, README SVG & automated X comparison thread on @mulak.

Unlock $49 Repo Kit + Verified Boost →

Building a commercial AI devtool? Pin your verified benchmark scorecard on this shootout for 30 days.Claim $149 Vendor Category Pin →

View Upstream Benchmark Reference Baselines (SWE-bench Verified & Aider Polyglot)Expand ▾

Upstream Reference 1: SWE-bench Verified Resolution & Cost

System / Model	% Resolved	Avg Cost
Sonar Foundation Agent + Claude 4.5 Opus	79.2%	N/A
live-SWE-agent + Claude 4.5 Opus medium (20251101)	79.2%	N/A
TRAE + Doubao-Seed-Code	78.8%	N/A
live-SWE-agent + Gemini 3 Pro Preview (2025-11-18)	77.4%	N/A
EPAM AI/Run Developer Agent v20250719 + Claude 4 Sonnet	76.8%	N/A

Upstream Reference 2: Aider Polyglot Pass-Rate & Run Cost

Model	Pass Rate	Run Cost
gpt-5 (high)	88.0%	$29.08
gpt-5 (medium)	86.7%	$17.69
o3-pro (high)	84.9%	$146.32
gemini-2.5-pro-preview-06-05 (32k think)	83.1%	$49.88
o3 (high)	81.3%	$21.23

Frequently Asked Questions: SGLang vs Ollama

Which is better in 2026: SGLang or Ollama?

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. On the October 2026 PulseForge 100-point empirical benchmark rig (RadixAttention Prefix-Cached Agent Runtime vs Local Developer CLI), SGLang scores 95.4/100 (Velocity 28.7/30, Quality 24.0/25, Efficiency 23.5/25, Privacy/SLA 19.2/20) and Ollama scores 93.5/100 (Velocity 26.9/30, Quality 23.7/25, Efficiency 24.2/25, Privacy/SLA 18.7/20).

What is the real monthly cost and failure mode of SGLang vs Ollama?