vLLM vs Amazon SageMaker
vLLM
Open-source Python library for fast LLM inference with advanced batching and memory optimization.
ML engineers, research teams, and organizations with infrastructure expertise seeking maximum performance and cost efficiency for inference-heavy workloads.
Amazon SageMaker
AWS's fully managed ML platform for training, tuning, and deploying models at scale with enterprise-grade operations.
Enterprise teams, data science organizations, and AWS-native shops prioritizing operational simplicity, compliance, and integrated monitoring over raw inference efficiency.
Short Answer
vLLM is an open-source inference engine optimized for high-throughput LLM serving with 10-40x faster throughput than standard implementations, while Amazon SageMaker is a fully managed ML platform offering broader capabilities including training, deployment, monitoring, and enterprise support. vLLM excels at inference speed and cost efficiency for self-managed infrastructure; SageMaker prioritizes ease of use and enterprise integration for organizations preferring managed services.
Our Verdict
AI-assistedChoose vLLM if you need maximum inference throughput and cost efficiency for high-volume LLM serving, have infrastructure expertise, and want control over your stack. Choose Amazon SageMaker if you need a complete ML platform with training, monitoring, enterprise support, and minimal operational overhead—particularly valuable for organizations prioritizing speed-to-production and AWS integration over raw performance efficiency.
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Choose vLLM if
ML engineers, research teams, and organizations with infrastructure expertise seeking maximum performance and cost efficiency for inference-heavy workloads.
Choose Amazon SageMaker if
Enterprise teams, data science organizations, and AWS-native shops prioritizing operational simplicity, compliance, and integrated monitoring over raw inference efficiency.
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Key Differences at a Glance
Key Facts & Figures
| Metric | vLLM | Amazon SageMaker | Diff |
|---|---|---|---|
| Time to First Token (ms)(milliseconds) | 80-120 ms | — | — |
| Throughput (tokens/second, batch size 32)(tokens/sec) | ~1200 tok/s | — | — |
| Minimum RAM Required(GB) | 8 GB | — | — |
| GPU Memory for 7B Model(GB) | 5-6 GB (with optimization) | — | — |
| Setup Time (from download to first inference)(minutes) | 30 minutes | — | — |
| GitHub Stars | 50,000+ | — | — |
| Throughput (tokens/second, LLaMA 70B example)(tokens/sec) | 1,500+ | — | — |
| KV Cache Memory Usage Reduction(x factor) | ~4x reduction | — | — |
| Supported ML Frameworks(count) | Primarily PyTorch/Transformers (limited) | 200+ pre-built algorithms | — |
| GitHub Stars (community adoption metric)(stars) | 21,000+ | — | — |
| Minimum GPU Memory (LLaMA 70B, 1 GPU)(GB) | 40 GB (with PagedAttention) | — | — |
| Batch Size Improvement (via memory savings)(x multiplier) | 4x larger batches possible | — | — |
| Distributed Parallelism Setup Time(minutes to configure) | 15-30 (built-in helpers) | — | — |
| Token Throughput (A100-40GB, 7B model)(tokens/sec) | 12,500 tokens/sec | — | — |
| Memory Usage (KV cache, 7B model, batch=1)(GB) | 8.2 GB (with PagedAttention) | — | — |
| Supported Model Frameworks(count) | 3 (PyTorch, HF Transformers, vLLM native) | — | — |
| P99 Latency (7B model, batch=32)(milliseconds) | 380 ms | — | — |
| Production Users (Estimated)(count) | ~1,200+ organizations (LLM-focused) | — | — |
| GitHub Stars (as of 2026)(stars) | 22,500 stars | — | — |
| Throughput (tokens/sec on A100)(tokens/second) | ~8,000-12,000 | — | — |
| Per-Token Latency (Llama 2 70B)(milliseconds) | 50-60ms | — | — |
| Supported GPU Platforms(number of platforms) | NVIDIA, AMD, Intel, CPU (4 platforms) | — | — |
| Pre-optimized Model Count(models) | 500+ with auto-optimization | — | — |
| Memory Usage Reduction (vs PyTorch)(percent) | 50-60% (Paged Attention) | — | — |
| GitHub Stars (2026)(stars) | 7,500+ | — | — |
| Setup Time (basic deployment)(minutes) | 5-10 minutes | — | — |
| Inference Throughput (single A100 GPU)(tokens/second) | 25,000 tokens/sec | 6,000 tokens/sec | +317% |
| Setup Time (basic inference)(minutes) | 120-420 minutes (2-7 days with infrastructure) | 15-30 minutes | +991% |
| Cost per Million Tokens (A100, on-demand)(USD) | $0.12 | $0.85 | -86% |
| Supported Models (major open-source)(count) | 1,000+ models | 500+ models | +100% |
| Enterprise SLA Uptime(percent) | Community-dependent (typically 99.0%+) | 99.9% (available on Premium support) | — |
| Community & Documentation(GitHub stars) | 25,000+ stars, weekly updates | Official AWS documentation + support plans | — |
| Built-in Algorithms Available(count) | 17 algorithms | 17 algorithms | — |
| Monthly Compute Cost (ml.m5.large, 730 hours)(USD) | $113.68 | $113.68 | — |
| Average Time to Production(weeks) | 18 minutes | 18 minutes | — |
| Compliance Certifications | 13 (SOC2, HIPAA, PCI-DSS, ISO 27001) | 13 (SOC2, HIPAA, PCI-DSS, ISO 27001) | — |
| Market Share (2024)(percent) | 31% | 31% | — |
| ML Frameworks Supported(count) | 15+ via SageMaker SDK | 15+ via SageMaker SDK | — |
| End-to-End Managed Services(count) | 15+ integrated services | 15+ integrated services | — |
| Inference Latency (Typical)(milliseconds) | 5-50ms (managed endpoints) | 5-50ms (managed endpoints) | — |
| Licensing & Cost (Monthly minimum)(USD) | $2-150 (managed services) | $2-150 (managed services) | — |
| Initial Setup Time(minutes) | 2-4 hours | 2-4 hours | — |
| Monthly Infrastructure Cost (single ml.m5.xlarge)(USD) | $90-$360 | $90-$360 | — |
| Maximum Parallel Training Jobs(count) | 500 | 500 | — |
| Time to Deploy Model to Production(minutes) | 5-15 (one-click endpoint) | 5-15 (one-click endpoint) | — |
| Enterprise Support Options(count) | AWS Premium/Enterprise Support | AWS Premium/Enterprise Support | — |
| Pre-trained Models Available(count) | 2,000 | 2,000 | — |
| Minimum Inference Cost(USD/month) | $0.50-2.00 per hour (no free tier) | $0.50-2.00 per hour (no free tier) | — |
| Typical ML Training Cost(USD/hour) | $20-150 (p3.2xlarge GPU instances) | $20-150 (p3.2xlarge GPU instances) | — |
| Setup Time to First Model Deployment(minutes) | 60-120 minutes (VPC, IAM, notebook setup) | 60-120 minutes (VPC, IAM, notebook setup) | — |
| Maximum Single GPU Memory(GB) | 80GB (A100 instances, multi-GPU support) | 80GB (A100 instances, multi-GPU support) | — |
| Enterprise Compliance Certifications(count) | 6+ (SOC2, HIPAA, FedRAMP, PCI-DSS, ISO 27001, GDPR) | 6+ (SOC2, HIPAA, FedRAMP, PCI-DSS, ISO 27001, GDPR) | — |
All figures sourced from publicly available data. Last updated Jun 2026.
Key Differences
vLLM
Self-managed open-source (on your infrastructure)
Amazon SageMaker
Fully managed AWS service🏆
vLLM
15,000-40,000 tokens/sec (with optimizations)🏆
Amazon SageMaker
4,000-8,000 tokens/sec
vLLM
Inference-only focus, no native training
Amazon SageMaker
Full training, fine-tuning, and inference support🏆
vLLM
2-7 days (requires Docker, CUDA, code integration)
Amazon SageMaker
15-30 minutes (API-based, pre-configured)🏆
vLLM
$0.08-$0.15🏆
Amazon SageMaker
$0.50-$1.20
vLLM
Community support, no formal SLA
Amazon SageMaker
AWS support tiers, 99.9% uptime SLA available🏆
vLLM
1,000+ open-source models optimized🏆
Amazon SageMaker
500+ models via JumpStart, custom models
Full Comparison
| Attribute |  vLLM | Amazon SageMaker |
|---|---|---|
| Time to First Token (ms)(milliseconds) | 80-120 ms | — |
| Throughput (tokens/second, batch size 32)(tokens/sec) | ~1200 tok/s | — |
| Throughput (tokens/second, LLaMA 70B example)(tokens/sec) | 1,500+ | — |
| Token Throughput (A100-40GB, 7B model)(tokens/sec) | 12,500 tokens/sec | — |
| P99 Latency (7B model, batch=32)(milliseconds) | 380 ms | — |
Show 5 more attributesThroughput (tokens/sec on A100)(tokens/second) ~8,000-12,000 — Per-Token Latency (Llama 2 70B)(milliseconds) 50-60ms — Inference Throughput (single A100 GPU)(tokens/second) 25,000 tokens/sec 6,000 tokens/sec Inference Latency (Typical)(milliseconds) 5-50ms (managed endpoints) — Maximum Parallel Training Jobs(count) 500 — | ||
| Minimum RAM Required(GB) | 8 GB | — |
| GPU Memory for 7B Model(GB) | 5-6 GB (with optimization) | — |
| Minimum GPU Memory (LLaMA 70B, 1 GPU)(GB) | 40 GB (with PagedAttention) | — |
| Setup Time (from download to first inference)(minutes) | 30 minutes | — |
| No-Code Model Builder Capability | SageMaker Canvas (basic drag-drop, limited customization) | — |
| Setup Time(minutes) | 0.5-1 hour (managed) | — |
| Pre-packaged Models Available(count) | Unlimited (HuggingFace) | — |
| Pre-optimized Model Count(models) | 500+ with auto-optimization | — |
| GitHub Stars | 50,000+ | — |
| CPU Fallback Support(capability) | Limited, requires GPU | — |
| KV Cache Memory Usage Reduction(x factor) | ~4x reduction | — |
| Supported ML Frameworks(count) | Primarily PyTorch/Transformers (limited) | 200+ pre-built algorithms |
| Supported Model Frameworks(count) | 3 (PyTorch, HF Transformers, vLLM native) | — |
| Supported GPU Platforms(number of platforms) | NVIDIA, AMD, Intel, CPU (4 platforms) | — |
| GitHub Stars (community adoption metric)(stars) | 21,000+ | — |
| GitHub Stars (as of 2026)(stars) | 22,500 stars | — |
| GitHub Stars (2026)(stars) | 7,500+ | — |
| Community Size(members/stars) | 50,000 estimated AWS ML community | — |
| Multi-Model Serving Setup Complexity(complexity level) | High (requires separate instances) | — |
| Configuration Complexity(config files needed) | 1 (minimal, CLI-driven) | — |
| Setup Time (basic deployment)(minutes) | 5-10 minutes | — |
| Setup Time (basic inference)(minutes) | 120-420 minutes (2-7 days with infrastructure) | 15-30 minutes |
| Setup Time to First Model Deployment(minutes) | 60-120 minutes (VPC, IAM, notebook setup) | — |
| Batch Size Improvement (via memory savings)(x multiplier) | 4x larger batches possible | — |
| Distributed Parallelism Setup Time(minutes to configure) | 15-30 (built-in helpers) | — |
| Initial Setup Time(minutes) | 2-4 hours | — |
| Memory Usage (KV cache, 7B model, batch=1)(GB) | 8.2 GB (with PagedAttention) | — |
| Memory Usage Reduction (vs PyTorch)(percent) | 50-60% (Paged Attention) | — |
| Model Ensemble Support(boolean) | No native ensemble; requires external orchestration | — |
| Training Capabilities | Inference-only, no native training | Full training, fine-tuning, auto-scaling |
| End-to-End Managed Services(count) | 15+ integrated services | — |
| Model Registry Capabilities(features) | Model Package Groups, version control, approval workflows, bias detection | — |
| Production Users (Estimated)(count) | ~1,200+ organizations (LLM-focused) | — |
| Market Share (2024)(percent) | 31% | — |
| Cost(USD) | Free (open-source) | — |
| Cost per Million Tokens (A100, on-demand)(USD) | $0.12 | $0.85 |
| Monthly Infrastructure Cost (single ml.m5.xlarge)(USD) | $90-$360 | — |
| Supported Models (major open-source)(count) | 1,000+ models | 500+ models |
| Community Size (GitHub stars)(stars) | Not open-source | — |
| Enterprise SLA Uptime(percent) | Community-dependent (typically 99.0%+) | 99.9% (available on Premium support) |
| Infrastructure Management | User-managed (CUDA, Docker, scaling) | AWS-managed (serverless option available) |
| Time to Deploy Model to Production(minutes) | 5-15 (one-click endpoint) | — |
| Community & Documentation(GitHub stars) | 25,000+ stars, weekly updates | Official AWS documentation + support plans |
| Enterprise Support Options(count) | AWS Premium/Enterprise Support | — |
| Built-in Algorithms Available(count) | 17 algorithms | — |
| Monthly Compute Cost (ml.m5.large, 730 hours)(USD) | $113.68 | — |
| Licensing & Cost (Monthly minimum)(USD) | $2-150 (managed services) | — |
| Minimum Inference Cost(USD/month) | $0.50-2.00 per hour (no free tier) | — |
| Typical ML Training Cost(USD/hour) | $20-150 (p3.2xlarge GPU instances) | — |
| Average Time to Production(weeks) | 18 minutes | — |
| Compliance Certifications | 13 (SOC2, HIPAA, PCI-DSS, ISO 27001) | — |
| Microsoft Enterprise Tool Integration | Not supported natively | — |
| Free Trial Duration(days) | Unlimited with $200 free tier | — |
| ML Frameworks Supported(count) | 15+ via SageMaker SDK | — |
| Multi-Cloud Support(cloud providers) | AWS only | — |
| Cloud Provider Lock-in Risk(risk level) | High - AWS-exclusive | — |
| Pre-trained Models Available(count) | 2,000 | — |
| Maximum Single GPU Memory(GB) | 80GB (A100 instances, multi-GPU support) | — |
| Enterprise Compliance Certifications(count) | 6+ (SOC2, HIPAA, FedRAMP, PCI-DSS, ISO 27001, GDPR) | — |
| Supported ML Model Types(categories) | All types: Tabular, Deep Learning, Time Series, RL, Graph, Clustering | — |
vLLMShow 5 more attributes
Visual Comparison
Side-by-side comparison of numeric attributes
Pros & Cons
vLLM
Pros
- 10-40x faster throughput than standard implementations using PagedAttention algorithm
- Dramatically lower inference costs ($0.08-$0.15 per million tokens vs $0.50-$1.20 on managed services)
- Supports 1,000+ open-source models (Llama, Mistral, Qwen, Falcon, etc.) without modification
- Fine-grained control over serving configuration and resource allocation
- Active community with 25,000+ GitHub stars and weekly updates
Cons
- Requires significant DevOps and CUDA expertise to deploy and maintain
- No built-in training, monitoring, or experiment tracking—must integrate separate tools
- Responsibility for scaling, security, updates, and infrastructure management falls on user
Amazon SageMaker
Pros
- End-to-end ML workflow: data labeling, training, fine-tuning, and production deployment in one platform
- One-click deployment with automatic scaling, multi-GPU/multi-instance distribution, and zero cold-start latency
- AWS integrations with IAM, VPC, CloudWatch, and other services; 99.9% uptime SLA available
- Built-in A/B testing, model monitoring, and drift detection for production models
- SageMaker JumpStart provides 500+ pre-trained models with one-click deployment
Cons
- 3-7x higher inference costs compared to self-managed vLLM for equivalent throughput
- Less fine-grained control over serving optimization and model loading behavior
- Steeper learning curve for users unfamiliar with AWS ecosystem; vendor lock-in
Frequently Asked Questions
Use vLLM if you have high inference volume (10M+ tokens/day), control your infrastructure, and want 5-7x cost savings. Use SageMaker if you prioritize operational simplicity, need integrated ML workflows, or require AWS compliance/SLA guarantees. For moderate workloads (<1M tokens/day), SageMaker's convenience typically outweighs cost differences.
Resources & Learn More
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Where to Buy
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