software
vLLM vs SageMaker 2026: Cost & Performance Comparison
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
Open-source LLM inference engine optimizing throughput with PagedAttention technology.
ML engineers, startups, and researchers prioritizing cost efficiency and throughput for high-volume LLM serving.
Score63%
VS
AS
Amazon SageMaker
AWS-managed ML platform providing end-to-end model development, training, inference, and monitoring.
Enterprise teams, Fortune 500 companies, and organizations needing managed ML infrastructure with compliance, monitoring, and minimal DevOps.
Score63%
Quick Answer
AI SummaryvLLM 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.
Our Verdict
AI-assistedChoose 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.
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vLLM
8.5/10
Amazon SageMaker
6.5/10
A
Choose vLLM if
Best pickML engineers, startups, and researchers prioritizing cost efficiency and throughput for high-volume LLM serving.
A
Choose Amazon SageMaker if
Enterprise teams, Fortune 500 companies, and organizations needing managed ML infrastructure with compliance, monitoring, and minimal DevOps.
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Key Differences at a Glance
- Throughput Performance (tokens/sec):✓ vLLM wins(15,000-35,000 tokens/sec (A100 GPU) vs 3,000-8,000 tokens/sec (comparable config))
- Setup & Deployment Time:✓ Amazon SageMaker wins(5-10 minutes (managed, fully hosted) vs 15-30 minutes (self-hosted))
- Infrastructure Management:✓ Amazon SageMaker wins(Fully managed by AWS (zero setup) vs User-managed (Docker, K8s, cloud VMs))
Key Facts & Figures
83 numeric metrics compared
| Metric | vLLM | Amazon SageMaker | Ratio |
|---|---|---|---|
| Peak Throughput (13B model, V100)(tokens/second) | 2800 | — | — |
| Memory Usage (13B model, batch=32)(GB) | 10.5 | — | — |
| Time to First Token (p99 latency)(milliseconds) | 45 | — | — |
| Setup Time (from install to inference)(minutes) | 5 | — | — |
| GPU Platform Support Count(platforms) | 7 (NVIDIA, AMD, Intel, Trainium, Gaudi, etc.) | — | — |
| Maximum Concurrent Requests(requests) | 256 | — | — |
| 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(stars) | 23,000+ | — | — |
| 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) | 2 (LLM-specific) | — | — |
| P99 Latency (7B model, batch=32)(milliseconds) | 380 ms | — | — |
| Production Users (Estimated)(organizations) | ~1,200+ organizations (LLM-focused) | — | — |
| GitHub Stars (as of 2026)(stars) | ~24,000 | — | — |
| 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) | 25,000+ | — | — |
| Setup Time (basic deployment)(minutes) | 5-10 minutes | — | — |
| Inference Throughput (single A100 GPU)(tokens/second) | 25,000 tokens/sec | 6,000 tokens/sec | |
| Setup Time (basic inference)(minutes) | 120-420 minutes (2-7 days with infrastructure) | 15-30 minutes | |
| Cost per Million Tokens (A100, on-demand)(USD) | $0.12 | $0.85 | |
| Supported Models (major open-source)(count) | 1,000+ models | 500+ models | |
| 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 | — |
| Inference Throughput (RTX 4090, Llama 2 13B)(tokens/second) | ~700 tokens/sec | — | — |
| Memory Usage (Llama 2 7B quantized)(GB) | ~6.5 GB | — | — |
| Installation Time (from zero)(minutes) | 25-40 minutes | — | — |
| Minimum VRAM for Llama 2 7B(GB) | 6 GB | — | — |
| Number of Supported GPU Backends(count) | 4+ (CUDA, ROCm, CPU, TPU, custom) | — | — |
| LLM Throughput Improvement(x faster than baseline) | 24x | — | — |
| Memory Usage (KV Cache)(% reduction vs standard) | 80% reduction | — | — |
| Enterprise Deployment Features(feature count) | 3 (basic) | — | — |
| Inference Throughput (LLaMA 2 70B, A100 GPU)(tokens/sec) | 25,000 tokens/sec (batch 256) | 5,500 tokens/sec (batch 32) | |
| Memory Usage (LLaMA 2 70B)(GB) | 45 GB (with PagedAttention) | 78 GB (standard) | |
| Deployment Time(minutes) | 20-30 minutes (self-hosted) | 5-10 minutes (managed) | |
| Cost per 1M Tokens (LLaMA 2 70B, On-Demand)(USD) | $0.25 (self-hosted, amortized) | $2.10 (SageMaker on-demand) | |
| Model Support (Open-Source LLMs)(models) | 500+ community models | 50+ marketplace models | |
| SLA Availability Guarantee(%) | No SLA (community support) | 99.9% (AWS SLA) | — |
| GPU Memory Reduction vs Baseline(%) | ~60% | — | — |
| Throughput Improvement (Batching)(x improvement) | 10-23x vs standard | — | — |
| Supported Model Formats(formats) | 15+ formats (HF, GGUF, AWQ, GPTQ, etc) | — | — |
| Time to Deploy (Minutes)(minutes) | 5-10 minutes | — | — |
| Latest Version Release Cycle(weeks) | 2-3 weeks | — | — |
| 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(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(available) | 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) | |
| Model Hub Size(models) | 300 (built-in algorithms) | 300 (built-in algorithms) | |
| Free Tier Cost(USD/month) | $0 (12-month free trial, limited) | $0 (12-month free trial, limited) | |
| Average Model Fine-Tuning Time(lines of code) | 50-80 lines | 50-80 lines | |
| Compute Cost Reduction (Spot Instances)(percent savings) | Up to 90% | Up to 90% | |
| AWS Integration Depth(integrated services) | Deep (40+ AWS services) | Deep (40+ AWS services) | |
| Development Time for Production Deployment(weeks (typical NLP project)) | 2-3 weeks (with managed services) | 2-3 weeks (with managed services) |
Sourced from publicly available data ·
Key Differences
7 attributes compared head-to-head
Amazon SageMaker leads
AS
4Amazon SageMakervLLM
Attribute
Amazon SageMaker
- 15,000-35,000 tokens/sec (A100 GPU)(winner)Throughput Performance (tokens/sec)3,000-8,000 tokens/sec (comparable config)
- 15-30 minutes (self-hosted)Setup & Deployment Time5-10 minutes (managed, fully hosted)(winner)
- User-managed (Docker, K8s, cloud VMs)Infrastructure ManagementFully managed by AWS (zero setup)(winner)
- $0.15-0.35 (self-hosted)(winner)Cost per Million Tokens (LLaMA 2 70B)$1.50-3.00 (on-demand)
- 500+ open-source LLMs(winner)Model Support50+ via marketplace + custom
- Community support onlyEnterprise SLAs & Support24/7 AWS enterprise support(winner)
- Not includedTraining Pipeline IntegrationIntegrated (training + inference)(winner)
- Throughput Performance (tokens/sec)
vLLM
15,000-35,000 tokens/sec (A100 GPU)(winner)
Amazon SageMaker
3,000-8,000 tokens/sec (comparable config)
- Setup & Deployment Time
vLLM
15-30 minutes (self-hosted)
Amazon SageMaker
5-10 minutes (managed, fully hosted)(winner)
- Infrastructure Management
vLLM
User-managed (Docker, K8s, cloud VMs)
Amazon SageMaker
Fully managed by AWS (zero setup)(winner)
- Cost per Million Tokens (LLaMA 2 70B)
vLLM
$0.15-0.35 (self-hosted)(winner)
Amazon SageMaker
$1.50-3.00 (on-demand)
- Model Support
vLLM
500+ open-source LLMs(winner)
Amazon SageMaker
50+ via marketplace + custom
- Enterprise SLAs & Support
vLLM
Community support only
Amazon SageMaker
24/7 AWS enterprise support(winner)
- Training Pipeline Integration
vLLM
Not included
Amazon SageMaker
Integrated (training + inference)(winner)
Full Comparison
| Attribute | vLLM | Amazon SageMaker |
|---|---|---|
| Peak Throughput (13B model, V100)(tokens/second) | 2800 | — |
| Time to First Token (p99 latency)(milliseconds) | 45 | — |
| 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+ | — |
Show 14 more attributesToken Throughput (A100-40GB, 7B model)(tokens/sec) 12,500 tokens/sec — P99 Latency (7B model, batch=32)(milliseconds) 380 ms — Throughput (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 Throughput (RTX 4090, Llama 2 13B)(tokens/second) ~700 tokens/sec — LLM Throughput Improvement(x faster than baseline) 24x — Memory Usage (KV Cache)(% reduction vs standard) 80% reduction — Inference Throughput (LLaMA 2 70B, A100 GPU)(tokens/sec) 25,000 tokens/sec (batch 256) 5,500 tokens/sec (batch 32) Memory Usage (LLaMA 2 70B)(GB) 45 GB (with PagedAttention) 78 GB (standard) GPU Memory Reduction vs Baseline(%) ~60% — Throughput Improvement (Batching)(x improvement) 10-23x vs standard — Inference Latency (Typical)(milliseconds) 5-50ms (managed endpoints) — Maximum Parallel Training Jobs(count) 500 — | ||
| Memory Usage (13B model, batch=32)(GB) | 10.5 | — |
| Memory Usage (Llama 2 7B quantized)(GB) | ~6.5 GB | — |
| Setup Time (from install to inference)(minutes) | 5 | — |
| GPU Platform Support Count(platforms) | 7 (NVIDIA, AMD, Intel, Trainium, Gaudi, etc.) | — |
| Supported ML Frameworks(count) | Primarily PyTorch/Transformers (limited) | 200+ pre-built algorithms |
| Supported Model Frameworks(count) | 2 (LLM-specific) | — |
| Supported GPU Platforms(number of platforms) | NVIDIA, AMD, Intel, CPU (4 platforms) | — |
| Number of Supported GPU Backends(count) | 4+ (CUDA, ROCm, CPU, TPU, custom) | — |
Show 1 more attributeSupported Model Formats(formats) 15+ formats (HF, GGUF, AWQ, GPTQ, etc) — | ||
| Maximum Concurrent Requests(requests) | 256 | — |
| Batch Size Improvement (via memory savings)(x multiplier) | 4x larger batches possible | — |
| Multi-GPU Support(scaling efficiency) | Native (tensor parallelism) | — |
| 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) | — |
| Minimum VRAM for Llama 2 7B(GB) | 6 GB | — |
| Setup Time (from download to first inference)(minutes) | 30 minutes | — |
| Configuration Complexity(complexity rating) | Low (Python API) | — |
| 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(stars) | 23,000+ | — |
| GitHub Stars (as of 2026)(stars) | ~24,000 | — |
| CPU Fallback Support(capability) | Limited, requires GPU | — |
| KV Cache Memory Usage Reduction(x factor) | ~4x reduction | — |
| GitHub Stars (community adoption metric)(stars) | 21,000+ | — |
| GitHub Stars (2026)(stars) | 25,000+ | — |
| Community Size (GitHub Stars)(stars) | Not open-source | — |
| Community Size(users) | 50,000 estimated AWS ML community | — |
| Multi-Model Serving Setup Complexity(complexity level) | High (requires separate instances) | — |
| Setup Time (basic deployment)(minutes) | 5-10 minutes | — |
| Setup Time (basic inference)(minutes) | 120-420 minutes (2-7 days with infrastructure) | 15-30 minutes(winner) |
| Installation Time (from zero)(minutes) | 25-40 minutes | — |
| Time to Deploy (Minutes)(minutes) | 5-10 minutes | — |
Show 2 more attributesSetup Time to First Model Deployment(minutes) 60-120 minutes (VPC, IAM, notebook setup) — Average Model Fine-Tuning Time(lines of code) 50-80 lines — | ||
| Distributed Parallelism Setup Time(minutes to configure) | 15-30 (built-in helpers) | — |
| Deployment Time(minutes) | 20-30 minutes (self-hosted) | 5-10 minutes (managed)(winner) |
| 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 |
| Batch Processing Support(null) | Yes (native continuous batching) | — |
| Token Streaming Native Support(boolean) | Via API wrapper | — |
| End-to-End Managed Services(count) | 15+ integrated services | — |
Show 1 more attributeModel Registry Capabilities(features) Model Package Groups, version control, approval workflows, bias detection — | ||
| Production Users (Estimated)(organizations) | ~1,200+ organizations (LLM-focused) | — |
| Cost(USD) | Free (open-source) | — |
| Cost per Million Tokens (A100, on-demand)(USD) | $0.12(winner) | $0.85 |
| Cost per 1M Tokens (LLaMA 2 70B, On-Demand)(USD) | $0.25 (self-hosted, amortized)(winner) | $2.10 (SageMaker on-demand) |
| Monthly Infrastructure Cost (single ml.m5.xlarge)(USD) | $90-$360 | — |
| Supported Models (major open-source)(count) | 1,000+ models(winner) | 500+ models |
| Enterprise SLA Uptime(percent) | Community-dependent (typically 99.0%+) | 99.9% (available on Premium support)(winner) |
| SLA Availability Guarantee(%) | No SLA (community support) | 99.9% (AWS SLA) |
| Infrastructure Management | User-managed (CUDA, Docker, scaling) | AWS-managed (serverless option available) |
| Production Monitoring(metrics exported) | Basic (throughput, latency) | — |
| Infrastructure Management Required(null) | User-managed (Docker, K8s, VMs) | Fully managed by AWS |
| 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 Availability | Community (GitHub issues) | 24/7 AWS enterprise support |
| Official Enterprise Support(boolean) | Community-based | — |
| API Standardization(null) | OpenAI-compatible API | — |
| Microsoft Enterprise Tool Integration | Not supported natively | — |
| AWS Integration Depth(integrated services) | Deep (40+ AWS services) | — |
| Enterprise Deployment Features(feature count) | 3 (basic) | — |
| Model Support (Open-Source LLMs)(models) | 500+ community models(winner) | 50+ marketplace models |
| Multi-Cloud Support(cloud providers) | AWS only | — |
| Cloud Provider Lock-in Risk(risk level) | High - AWS-exclusive | — |
| Latest Version Release Cycle(weeks) | 2-3 weeks | — |
| 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) | — |
| Free Tier Cost(USD/month) | $0 (12-month free trial, limited) | — |
Show 1 more attributeCompute Cost Reduction (Spot Instances)(percent savings) Up to 90% — | ||
| Average Time to Production(weeks) | 18 minutes | — |
| Compliance Certifications(certifications) | 13 (SOC2, HIPAA, PCI-DSS, ISO 27001) | — |
| Market Share (2024)(percent) | 31% | — |
| Free Trial Duration(days) | Unlimited with $200 free tier | — |
| ML Frameworks Supported(count) | 15+ via SageMaker SDK | — |
| Initial Setup Time(minutes) | 2-4 hours | — |
| Enterprise Support Options(available) | AWS Premium/Enterprise Support | — |
| 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 | — |
| Model Hub Size(models) | 300 (built-in algorithms) | — |
| Enterprise Monitoring/Governance(features) | Advanced (model registry, drift detection, explainability) | — |
| Monthly Active Users(billion) | 200,000+ (estimated) | — |
| Development Time for Production Deployment(weeks (typical NLP project)) | 2-3 weeks (with managed services) | — |
vLLMAAmazon SageMaker
Peak Throughput (13B model, V100)(tokens/second)
2800
—
Time to First Token (p99 latency)(milliseconds)
45
—
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+
—
Show 14 more attributes
Token Throughput (A100-40GB, 7B model)(tokens/sec)
12,500 tokens/sec
—
P99 Latency (7B model, batch=32)(milliseconds)
380 ms
—
Throughput (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 Throughput (RTX 4090, Llama 2 13B)(tokens/second)
~700 tokens/sec
—
LLM Throughput Improvement(x faster than baseline)
24x
—
Memory Usage (KV Cache)(% reduction vs standard)
80% reduction
—
Inference Throughput (LLaMA 2 70B, A100 GPU)(tokens/sec)
25,000 tokens/sec (batch 256)
5,500 tokens/sec (batch 32)
Memory Usage (LLaMA 2 70B)(GB)
45 GB (with PagedAttention)
78 GB (standard)
GPU Memory Reduction vs Baseline(%)
~60%
—
Throughput Improvement (Batching)(x improvement)
10-23x vs standard
—
Inference Latency (Typical)(milliseconds)
5-50ms (managed endpoints)
—
Maximum Parallel Training Jobs(count)
500
—
Memory Usage (13B model, batch=32)(GB)
10.5
—
Memory Usage (Llama 2 7B quantized)(GB)
~6.5 GB
—
Setup Time (from install to inference)(minutes)
5
—
GPU Platform Support Count(platforms)
7 (NVIDIA, AMD, Intel, Trainium, Gaudi, etc.)
—
Supported ML Frameworks(count)
Primarily PyTorch/Transformers (limited)
200+ pre-built algorithms
Supported Model Frameworks(count)
2 (LLM-specific)
—
Supported GPU Platforms(number of platforms)
NVIDIA, AMD, Intel, CPU (4 platforms)
—
Number of Supported GPU Backends(count)
4+ (CUDA, ROCm, CPU, TPU, custom)
—
Show 1 more attribute
Supported Model Formats(formats)
15+ formats (HF, GGUF, AWQ, GPTQ, etc)
—
Maximum Concurrent Requests(requests)
256
—
Batch Size Improvement (via memory savings)(x multiplier)
4x larger batches possible
—
Multi-GPU Support(scaling efficiency)
Native (tensor parallelism)
—
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)
—
Minimum VRAM for Llama 2 7B(GB)
6 GB
—
Setup Time (from download to first inference)(minutes)
30 minutes
—
Configuration Complexity(complexity rating)
Low (Python API)
—
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(stars)
23,000+
—
GitHub Stars (as of 2026)(stars)
~24,000
—
CPU Fallback Support(capability)
Limited, requires GPU
—
KV Cache Memory Usage Reduction(x factor)
~4x reduction
—
GitHub Stars (community adoption metric)(stars)
21,000+
—
GitHub Stars (2026)(stars)
25,000+
—
Community Size (GitHub Stars)(stars)
Not open-source
—
Community Size(users)
50,000 estimated AWS ML community
—
Multi-Model Serving Setup Complexity(complexity level)
High (requires separate instances)
—
Setup Time (basic deployment)(minutes)
5-10 minutes
—
Setup Time (basic inference)(minutes)
120-420 minutes (2-7 days with infrastructure)
15-30 minutes
Installation Time (from zero)(minutes)
25-40 minutes
—
Time to Deploy (Minutes)(minutes)
5-10 minutes
—
Show 2 more attributes
Setup Time to First Model Deployment(minutes)
60-120 minutes (VPC, IAM, notebook setup)
—
Average Model Fine-Tuning Time(lines of code)
50-80 lines
—
Distributed Parallelism Setup Time(minutes to configure)
15-30 (built-in helpers)
—
Deployment Time(minutes)
20-30 minutes (self-hosted)
5-10 minutes (managed)
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
Batch Processing Support(null)
Yes (native continuous batching)
—
Token Streaming Native Support(boolean)
Via API wrapper
—
End-to-End Managed Services(count)
15+ integrated services
—
Show 1 more attribute
Model Registry Capabilities(features)
Model Package Groups, version control, approval workflows, bias detection
—
Production Users (Estimated)(organizations)
~1,200+ organizations (LLM-focused)
—
Cost(USD)
Free (open-source)
—
Cost per Million Tokens (A100, on-demand)(USD)
$0.12
$0.85
Cost per 1M Tokens (LLaMA 2 70B, On-Demand)(USD)
$0.25 (self-hosted, amortized)
$2.10 (SageMaker on-demand)
Monthly Infrastructure Cost (single ml.m5.xlarge)(USD)
$90-$360
—
Supported Models (major open-source)(count)
1,000+ models
500+ models
Enterprise SLA Uptime(percent)
Community-dependent (typically 99.0%+)
99.9% (available on Premium support)
SLA Availability Guarantee(%)
No SLA (community support)
99.9% (AWS SLA)
Infrastructure Management
User-managed (CUDA, Docker, scaling)
AWS-managed (serverless option available)
Production Monitoring(metrics exported)
Basic (throughput, latency)
—
Infrastructure Management Required(null)
User-managed (Docker, K8s, VMs)
Fully managed by AWS
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 Availability
Community (GitHub issues)
24/7 AWS enterprise support
Official Enterprise Support(boolean)
Community-based
—
API Standardization(null)
OpenAI-compatible API
—
Microsoft Enterprise Tool Integration
Not supported natively
—
AWS Integration Depth(integrated services)
Deep (40+ AWS services)
—
Enterprise Deployment Features(feature count)
3 (basic)
—
Model Support (Open-Source LLMs)(models)
500+ community models
50+ marketplace models
Multi-Cloud Support(cloud providers)
AWS only
—
Cloud Provider Lock-in Risk(risk level)
High - AWS-exclusive
—
Latest Version Release Cycle(weeks)
2-3 weeks
—
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)
—
Free Tier Cost(USD/month)
$0 (12-month free trial, limited)
—
Show 1 more attribute
Compute Cost Reduction (Spot Instances)(percent savings)
Up to 90%
—
Average Time to Production(weeks)
18 minutes
—
Compliance Certifications(certifications)
13 (SOC2, HIPAA, PCI-DSS, ISO 27001)
—
Market Share (2024)(percent)
31%
—
Free Trial Duration(days)
Unlimited with $200 free tier
—
ML Frameworks Supported(count)
15+ via SageMaker SDK
—
Initial Setup Time(minutes)
2-4 hours
—
Enterprise Support Options(available)
AWS Premium/Enterprise Support
—
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
—
Model Hub Size(models)
300 (built-in algorithms)
—
Enterprise Monitoring/Governance(features)
Advanced (model registry, drift detection, explainability)
—
Monthly Active Users(billion)
200,000+ (estimated)
—
Development Time for Production Deployment(weeks (typical NLP project))
2-3 weeks (with managed services)
—
Pros & Cons
10 pros·6 cons across both
AS
vLLM
+5-3
Pros
- 10-40x higher throughput than standard inference engines (15,000+ tokens/sec on A100)
- PagedAttention algorithm reduces memory usage by 60-75%, enabling larger batch sizes
- Supports 500+ open-source models (LLaMA, Mistral, Qwen, Falcon, etc.)
- Free and self-hosted—no recurring licensing costs after infrastructure
- Highly customizable with extensive documentation and active GitHub community (30,000+ stars)
Cons
- Requires infrastructure management (deployment on GPU servers, Kubernetes, cloud VMs)
- No built-in monitoring, logging, or auto-scaling—must integrate with external tools
- Community support only—no SLA guarantees or enterprise assistance
AS
Amazon SageMaker
+5-3
Pros
- Fully managed infrastructure—deploy LLM endpoints in 5-10 minutes with zero DevOps
- Integrated ML workflows from data preprocessing → training → inference → monitoring
- Enterprise-grade SLAs (99.9% availability), 24/7 AWS support, and compliance certifications
- Built-in auto-scaling, A/B testing, model monitoring, and drift detection
- Native AWS integration (S3, Lambda, EventBridge, IAM) for enterprise deployments
Cons
- 3-8x higher inference costs than vLLM ($1.50-3.00 per million tokens vs. $0.15-0.35)
- Limited to 50+ models in marketplace—less flexibility for niche or custom open-source models
- Higher learning curve for AWS-specific APIs compared to open-source frameworks
Frequently Asked Questions
5 questions
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.
Resources & Learn More
Curated sources to dive deeper
Where to Buy
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