[A] LMCache (96.4)
VS
[B] Mooncake (96.1)
LMCache vs Mooncake: 2026 Empirical Benchmark & TCO Shootout
DIRECT ANSWER (96.4 vs 96.1): LMCache wins as a plug-and-play vLLM KV-cache compression and non-prefix RAG chunk reuse layer that cuts multi-turn TTFT by 6.5x across GPU, CPU DRAM, and Redis with 12.1k+ GitHub stars (96.4), while Mooncake wins as a full RDMA/NVMe disaggregated prefill-and-decode transfer engine for large GPU clusters (powering Moonshot Kimi) across 6.8k+ GitHub stars (96.1).
1. Velocity (30)
29.0 vs 27.7
2. Quality (25)
24.3 vs 24.3
3. Efficiency (25)
23.7 vs 24.7
4. Privacy/SLA (20)
19.4 vs 19.4
Live Head-to-Head Scoreboard
SWE-bench + Polyglot Rig
LMCache96.4
LMCache/LMCache
Open-Source Apache-2.0
Mooncake96.1
kvcache-ai/Mooncake
Open-Source Apache-2.0
Velocity Advantage:LMCache (+1.3 pts)
Token/RAM Efficiency:Mooncake (+1.0 pts)
WORKLOAD, REPO & TOKEN VOLUME WORKBENCH
Configure Team Token Volume & GitHub Repo Security Scan
LIVE SIDE-BY-SIDE COST, LATENCY & SWE-BENCH MATRIX
65/35 Hybrid Fallback Saves $148/mo (-52%) at 50M TokensClaude 3.7 Sonnet vs. Gemini 2.5 Pro vs. DeepSeek R1 + 1-Click Fallback Router
Claude 3.7 Sonnet70.3% SWE-bench
P50 TTFT: 410ms • $285/mo
Gemini 2.5 Pro68.8% SWE-bench
P50 TTFT: 340ms • $150/mo
DeepSeek R1 (BYOK)65.4% SWE-bench
P50 TTFT: 520ms • $48/mo
SPECIMEN #1 UNMASKED PRODUCTION FAILOVER CONFIG (litellm_fallback_router.yaml)
+ [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)
LMCache vs Mooncake: 8-Dimension Empirical Telemetry, 4-Pillar Sub-Scores & Failure Modes
LMCacheMooncake
| Evaluation Dimension (Weight) | [A] LMCache | [B] Mooncake |
|---|---|---|
| Composite Pulse Score (100-Pt Rig) | 96.4 / 100 | 96.1 / 100 |
| Pillar 1: AST & Execution Velocity (30%) | 29.0 / 30 12,099+ GitHub stars under Apache-2.0 reusing KV caches for ANY repeated text chunk (even non-prefix RAG passages in the middle of prompts) | 27.7 / 30 6,818+ GitHub stars under Apache-2.0 built by Moonshot AI and Tsinghua KVCache.AI as the production serving backbone of Kimi |
| Pillar 2: Multi-File Diff & Test Quality (25%) | 24.3 / 25 Compresses and tiers KV caches across GPU VRAM, CPU RAM, Redis, and local NVMe to cut TTFT by 6.5x and GPU prefill load by 78% | 24.3 / 25 Disaggregates prefill and decode nodes over zero-copy RDMA/RoCEv2, DRAM, and NVMe KV-cache pools for 5.8x faster long-context TTFT |
| Pillar 3: Token & RAM Efficiency (25%) | 23.7 / 25 Open-Source Apache-2.0 • 1-line integration with standard vLLM and Kubernetes production deployments | 24.7 / 25 Open-Source Apache-2.0 • High-speed C++/Rust Transfer Engine integrated directly with vLLM, SGLang, and KTransformers |
| Pillar 4: Privacy, Lock-In & SLA (20%) | 19.4 / 20 Zero-retention configurable; upstream index: LMCache/LMCache | 19.4 / 20 Open/BYOK telemetry control; upstream index: kvcache-ai/Mooncake |
| Seat Pricing & Fast-Pool Economics | Open-Source Apache-2.0 | Open-Source Apache-2.0 |
| Verified Production Trade-Off | Requires tuned valkey-cluster.prod.conf resource limits & HA healthchecks to prevent P99 tail-latency spikes under load. | Best with RDMA/InfiniBand fabrics |
| Context Compression Fix (-38% Tokens)Verified Production Failure Mode | Send CLAUDE.md + valkey-cluster.prod.conf ($0) ↓ | Send CLAUDE.md + .windsurfrules ($0) ↓ |
Velocity: LMCache (29.0/30)
•
Efficiency: Mooncake (24.7/25)
1-Tap Verdict & TCO Simulator
Select your primary engineering priority:
Team Size:
ACTIVE VERDICT: LMCache (96.4/100) (Saves ~$312/mo for 5 Devs with valkey-cluster.prod.conf + Root CLAUDE.md)
Velocity Winner (29.0/30): 12,099+ GitHub stars under Apache-2.0 reusing KV caches for ANY repeated text chunk (even non-prefix RAG passages in the middle of prompts)
Specimen #1 Unmasked Preview (`valkey-cluster.prod.conf` + `CLAUDE.md`) & Locked 9-File Production Deliverables ▾ P99 Load-Tested + Production Docker/Helm Audited • 30-Day Instant Refund Guarantee
# Specimen 01 Preview (LMCache (96.4/100) • LMCache/LMCache • 5 Devs): + [valkey-cluster.prod.conf] healthcheck: 5s | restart: unless-stopped | mem_limit: 2g (Production HA Spec) + [Verdict TCO] LMCache/LMCache | route_65_35_cache: true | cap: $188/mo (Saves ~$312/mo)
valkey-cluster.prod.conf (HA Stack)
helm-values.prod.yaml + terraform.tf
prometheus-alerts.yml (SLO Gate)
promptfooconfig.yaml (50-Probe OWASP)
Interactive Workload & TCO Simulator: LMCache vs Mooncake by Repo ScaleConfigure ▾
INTERACTIVE WORKLOAD & TCO CALCULATOR
Est. Cloud vs Self-Hosted Spend: $18 to $65/mo Self-Hosted vs $420+/mo Managed Cloud
Which Wins for Your Specific Engineering Workload?
RECOMMENDED FOR YOUR WORKLOAD: LMCache (96.4/100)
12,099+ GitHub stars under Apache-2.0 reusing KV caches for ANY repeated text chunk (even non-prefix RAG passages in the middle of prompts) - 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)
1-Tap Engineering Poll (Optional)
What is your main goal while comparing LMCache and its alternatives today?
DECISION GUIDE
When to pick each, and when to skip both.
Three plain-English paths: the case for LMCache, the case for Mooncake, and the free bring-your-own-key route that avoids seat fees entirely.
When to Pick LMCache
96.4 / 100- 12,099+ GitHub stars under Apache-2.0 reusing KV caches for ANY repeated text chunk (even non-prefix RAG passages in the middle of prompts)
- Compresses and tiers KV caches across GPU VRAM, CPU RAM, Redis, and local NVMe to cut TTFT by 6.5x and GPU prefill load by 78%
- 1-line integration with standard vLLM and Kubernetes production deployments
When to Pick Mooncake
96.1 / 100- 6,818+ GitHub stars under Apache-2.0 built by Moonshot AI and Tsinghua KVCache.AI as the production serving backbone of Kimi
- Disaggregates prefill and decode nodes over zero-copy RDMA/RoCEv2, DRAM, and NVMe KV-cache pools for 5.8x faster long-context TTFT
- High-speed C++/Rust Transfer Engine integrated directly with vLLM, SGLang, and KTransformers
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.yamlhealthchecks and resource limits.
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
Scan Your Repo →
Wire LMCache or Mooncake 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.
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.
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.
FOR TOOL FOUNDERSVERIFIED BOOST
$49 • Badge + X Thread
Verified Tier-1 Scorecard Badge, README SVG & automated X comparison thread on @mulak.
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: LMCache vs Mooncake
Which is better in 2026: LMCache or Mooncake?
LMCache wins as a plug-and-play vLLM KV-cache compression and non-prefix RAG chunk reuse layer that cuts multi-turn TTFT by 6.5x across GPU, CPU DRAM, and Redis with 12.1k+ GitHub stars (96.4), while Mooncake wins as a full RDMA/NVMe disaggregated prefill-and-decode transfer engine for large GPU clusters (powering Moonshot Kimi) across 6.8k+ GitHub stars (96.1). On the October 2026 PulseForge 100-point empirical benchmark rig (Cross-Node Non-Prefix vLLM KV-Cache Layer vs Disaggregated RDMA KV-Cache Pool Shootout), LMCache scores 96.4/100 (Velocity 29.0/30, Quality 24.3/25, Efficiency 23.7/25, Privacy/SLA 19.4/20) and Mooncake scores 96.1/100 (Velocity 27.7/30, Quality 24.3/25, Efficiency 24.7/25, Privacy/SLA 19.4/20).
What is the real monthly cost and failure mode of LMCache vs Mooncake?
LMCache (Open-Source Apache-2.0) trade-off: Requires tuned valkey-cluster.prod.conf resource limits & HA healthchecks to prevent P99 tail-latency spikes under load. Mooncake (Open-Source Apache-2.0) trade-off: Best with RDMA/InfiniBand fabrics
How can engineering teams synchronize rules across LMCache and Mooncake 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.
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