vLLM is a specialized, open-source inference engine optimized for LLM throughput with 10-40x faster serving speeds, while SageMaker is a comprehensive managed ML platform offering broader capabilities beyond inference including training, monitoring, and enterprise support. vLLM excels at cost-efficient LLM deployment; SageMaker excels at end-to-end ML workflows with minimal infrastructure management.
vLLM vs Amazon SageMaker — Side-by-side comparison on A Versus B
Open-source LLM inference engine optimizing throughput with PagedAttention technology.
AWS-managed ML platform providing end-to-end model development, training, inference, and monitoring.
Throughput Performance (tokens/sec). Setup & Deployment Time. Infrastructure Management. Cost per Million Tokens (LLaMA 2 70B). Model Support. Enterprise SLAs & Support. Training Pipeline Integration
Choose vLLM if you need maximum throughput efficiency, cost optimization, and control over infrastructure—ideal for startups and researchers running high-volume inference workloads. Choose SageMaker if you prioritize ease of use, enterprise-grade support, integrated ML workflows, and don't want to manage infrastructure—ideal for enterprises and teams building production ML systems with minimal DevOps overhead.
vLLM: 7 (NVIDIA, AMD, Intel, Trainium, Gaudi, etc.) | Amazon SageMaker: N/A
Throughput (tokens/second, batch size 32)
vLLM: ~1200 tok/s | Amazon SageMaker: N/A
vLLM: 5-6 GB (with optimization) | Amazon SageMaker: N/A
Setup Time (from download to first inference)
vLLM: 30 minutes | Amazon SageMaker: N/A
vLLM: Unlimited (HuggingFace) | Amazon SageMaker: N/A
vLLM: Limited, requires GPU | Amazon SageMaker: N/A
Throughput (tokens/second, LLaMA 70B example)
vLLM: ~4x reduction | Amazon SageMaker: N/A
vLLM: Primarily PyTorch/Transformers (limited) | Amazon SageMaker: 200+ pre-built algorithms
GitHub Stars (community adoption metric)
vLLM: 40 GB (with PagedAttention) | Amazon SageMaker: N/A
vLLM: High (requires separate instances) | Amazon SageMaker: N/A
Batch Size Improvement (via memory savings)
vLLM: 4x larger batches possible | Amazon SageMaker: N/A
vLLM: 15-30 (built-in helpers) | Amazon SageMaker: N/A
vLLM: 12,500 tokens/sec | Amazon SageMaker: N/A
Memory Usage (KV cache, 7B model, batch=1)
vLLM: 8.2 GB (with PagedAttention) | Amazon SageMaker: N/A
vLLM: 2 (LLM-specific) | Amazon SageMaker: N/A
vLLM: Low (Python API) | Amazon SageMaker: N/A
vLLM: No native ensemble; requires external orchestration | Amazon SageMaker: N/A
vLLM: ~1,200+ organizations (LLM-focused) | Amazon SageMaker: N/A
vLLM: ~8,000-12,000 | Amazon SageMaker: N/A
vLLM: NVIDIA, AMD, Intel, CPU (4 platforms) | Amazon SageMaker: N/A
vLLM: 500+ with auto-optimization | Amazon SageMaker: N/A
vLLM: 50-60% (Paged Attention) | Amazon SageMaker: N/A
vLLM: 5-10 minutes | Amazon SageMaker: N/A
vLLM: Free (open-source) | Amazon SageMaker: N/A
vLLM: 25,000 tokens/sec | Amazon SageMaker: 6,000 tokens/sec
vLLM: 120-420 minutes (2-7 days with infrastructure) | Amazon SageMaker: 15-30 minutes
Cost per Million Tokens (A100, on-demand)
vLLM: 1,000+ models | Amazon SageMaker: 500+ models
vLLM: Inference-only, no native training | Amazon SageMaker: Full training, fine-tuning, auto-scaling
vLLM: Community-dependent (typically 99.0%+) | Amazon SageMaker: 99.9% (available on Premium support)
vLLM: User-managed (CUDA, Docker, scaling) | Amazon SageMaker: AWS-managed (serverless option available)
vLLM: 25,000+ stars, weekly updates | Amazon SageMaker: Official AWS documentation + support plans
Inference Throughput (RTX 4090, Llama 2 13B)
vLLM: ~700 tokens/sec | Amazon SageMaker: N/A
vLLM: 25-40 minutes | Amazon SageMaker: N/A
vLLM: 4+ (CUDA, ROCm, CPU, TPU, custom) | Amazon SageMaker: N/A
vLLM: Yes (native continuous batching) | Amazon SageMaker: N/A
vLLM: OpenAI-compatible API | Amazon SageMaker: N/A
vLLM: 80% reduction | Amazon SageMaker: N/A
vLLM: Native (tensor parallelism) | Amazon SageMaker: N/A
vLLM: Basic (throughput, latency) | Amazon SageMaker: N/A
Inference Throughput (LLaMA 2 70B, A100 GPU)
vLLM: 25,000 tokens/sec (batch 256) | Amazon SageMaker: 5,500 tokens/sec (batch 32)
vLLM: 45 GB (with PagedAttention) | Amazon SageMaker: 78 GB (standard)
vLLM: 20-30 minutes (self-hosted) | Amazon SageMaker: 5-10 minutes (managed)
Cost per 1M Tokens (LLaMA 2 70B, On-Demand)
vLLM: $0.25 (self-hosted, amortized) | Amazon SageMaker: $2.10 (SageMaker on-demand)
vLLM: 500+ community models | Amazon SageMaker: 50+ marketplace models
vLLM: User-managed (Docker, K8s, VMs) | Amazon SageMaker: Fully managed by AWS
vLLM: No SLA (community support) | Amazon SageMaker: 99.9% (AWS SLA)
vLLM: Community (GitHub issues) | Amazon SageMaker: 24/7 AWS enterprise support
vLLM: 10-23x vs standard | Amazon SageMaker: N/A
vLLM: 15+ formats (HF, GGUF, AWQ, GPTQ, etc) | Amazon SageMaker: N/A
vLLM: Via API wrapper | Amazon SageMaker: N/A
vLLM: Community-based | Amazon SageMaker: N/A
vLLM: N/A | Amazon SageMaker: 17 algorithms
Monthly Compute Cost (ml.m5.large, 730 hours)
vLLM: N/A | Amazon SageMaker: 18 minutes
vLLM: N/A | Amazon SageMaker: 13 (SOC2, HIPAA, PCI-DSS, ISO 27001)
vLLM: N/A | Amazon SageMaker: SageMaker Canvas (basic drag-drop, limited customization)
vLLM: N/A | Amazon SageMaker: Not supported natively
vLLM: N/A | Amazon SageMaker: Unlimited with $200 free tier
vLLM: N/A | Amazon SageMaker: 0.5-1 hour (managed)
vLLM: N/A | Amazon SageMaker: 15+ via SageMaker SDK
vLLM: N/A | Amazon SageMaker: 15+ integrated services
vLLM: N/A | Amazon SageMaker: Model Package Groups, version control, approval workflows, bias detection
vLLM: N/A | Amazon SageMaker: 5-50ms (managed endpoints)
vLLM: N/A | Amazon SageMaker: $2-150 (managed services)
Monthly Infrastructure Cost (single ml.m5.xlarge)
vLLM: N/A | Amazon SageMaker: 5-15 (one-click endpoint)
vLLM: N/A | Amazon SageMaker: Not open-source
vLLM: N/A | Amazon SageMaker: AWS Premium/Enterprise Support
vLLM: N/A | Amazon SageMaker: High - AWS-exclusive
vLLM: N/A | Amazon SageMaker: $0.50-2.00 per hour (no free tier)
vLLM: N/A | Amazon SageMaker: $20-150 (p3.2xlarge GPU instances)
vLLM: N/A | Amazon SageMaker: 60-120 minutes (VPC, IAM, notebook setup)
vLLM: N/A | Amazon SageMaker: 80GB (A100 instances, multi-GPU support)
vLLM: N/A | Amazon SageMaker: 6+ (SOC2, HIPAA, FedRAMP, PCI-DSS, ISO 27001, GDPR)
vLLM: N/A | Amazon SageMaker: 50,000 estimated AWS ML community
vLLM: N/A | Amazon SageMaker: All types: Tabular, Deep Learning, Time Series, RL, Graph, Clustering
vLLM: N/A | Amazon SageMaker: 300 (built-in algorithms)
vLLM: N/A | Amazon SageMaker: $0 (12-month free trial, limited)
vLLM: N/A | Amazon SageMaker: 50-80 lines
vLLM: N/A | Amazon SageMaker: Advanced (model registry, drift detection, explainability)
vLLM: N/A | Amazon SageMaker: 200,000+ (estimated)
vLLM: N/A | Amazon SageMaker: Deep (40+ AWS services)
Development Time for Production Deployment
vLLM: N/A | Amazon SageMaker: 2-3 weeks (with managed services)
How to choose between vLLM and Amazon SageMaker
vLLM vs Amazon SageMaker — Structured Comparison Data
Machine-readable comparison dataset for vLLM vs Amazon SageMaker. Contains 111 attribute comparisons, 5 FAQ pairs.
vLLM vs Amazon SageMaker — Attribute Category Vocabulary
Taxonomy of attribute categories used to classify comparison data for vLLM vs Amazon SageMaker.
Use vLLM if you: operate at high scale (1B+ tokens daily), need maximum cost efficiency, have DevOps capability, and want model flexibility. Use SageMaker if you: prioritize ease of deployment, need enterprise support, operate under compliance requirements, or have small DevOps teams. vLLM's 8x throughput advantage justifies the management overhead for high-volume workloads; SageMaker's managed nature justifies its cost premium for enterprises.
What is PagedAttention and how does it improve vLLM performance?
PagedAttention is vLLM's core innovation: it treats attention key-value (KV) caches like memory pages (similar to operating systems), enabling dynamic allocation and sharing. This reduces memory fragmentation by 60-75%, increases batch sizes from 4-8 (standard) to 256+, and improves GPU utilization from 30-40% to 85-95%, resulting in 10-40x throughput gains compared to HuggingFace Transformers or standard vLLM implementations.
Yes—SageMaker supports custom Docker containers. You can deploy vLLM as a custom SageMaker endpoint by packaging it in a Docker image with SageMaker's inference toolkit. This combines vLLM's throughput advantage with SageMaker's managed infrastructure, auto-scaling, monitoring, and SLAs—though you sacrifice some cost savings compared to self-hosted vLLM.
How much money can I save using vLLM vs. SageMaker at scale?
For 100M tokens daily: vLLM costs ~$2.50/day (self-hosted GPU amortization ~$0.025/M tokens), while SageMaker costs ~$210/day ($2.10/M tokens). Annual savings: vLLM saves ~$75,000 compared to SageMaker. However, vLLM requires: GPU infrastructure ($8,000-15,000/month for production setup), DevOps staffing (~$150,000/year), and monitoring tools. Break-even: SageMaker is cheaper if token volume < 50M/day or DevOps cost > $100K/year.
vLLM supports 500+ open-source models (LLaMA, Mistral, Qwen, Falcon, Phi, etc.) with community-driven updates. SageMaker's marketplace provides ~50 curated models plus custom bring-your-own-model options. vLLM is superior if you need niche models or early-stage research models (7-15 day lag on SageMaker). SageMaker is better for proprietary models and enterprise pre-vetted solutions.
How to Choose Between vLLM and Amazon SageMaker
Consider: Throughput Performance (tokens/sec)
Throughput Performance (tokens/sec): vLLM has 15,000-35,000 tokens/sec (A100 GPU), while Amazon SageMaker has 3,000-8,000 tokens/sec (comparable config).
Setup & Deployment Time: vLLM has 15-30 minutes (self-hosted), while Amazon SageMaker has 5-10 minutes (managed, fully hosted).
Infrastructure Management: vLLM has User-managed (Docker, K8s, cloud VMs), while Amazon SageMaker has Fully managed by AWS (zero setup).
Consider: Cost per Million Tokens (LLaMA 2 70B)
Cost per Million Tokens (LLaMA 2 70B): vLLM has $0.15-0.35 (self-hosted), while Amazon SageMaker has $1.50-3.00 (on-demand).
Model Support: vLLM has 500+ open-source LLMs, while Amazon SageMaker has 50+ via marketplace + custom.
Enterprise SLAs & Support: vLLM has Community support only, while Amazon SageMaker has 24/7 AWS enterprise support.
Training Pipeline Integration: vLLM has Not included, while Amazon SageMaker has Integrated (training + inference).