vLLM vs Amazon SageMaker
type: Article
description: 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.
language: en-US
date published: 2026-07-08T00:31:58.263Z
date created: 2026-07-08T00:31:58.263Z
date modified: 2026-07-08T00:31:58.302Z
content reference time: 2026-07-08T00:31:58.302Z
author: A Versus B
publisher: A Versus B
about: vLLM
about: Amazon SageMaker
abstract: 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.
text: Throughput Performance (tokens/sec). Setup & Deployment Time. Infrastructure Management. Cost per Million Tokens (LLaMA 2 70B). Model Support. Enterprise SLAs & Support. Training Pipeline Integration
conclusion text: 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.
additional property: Peak Throughput (13B model, V100)
  type: PropertyValue
  value: vLLM: 2800 | Amazon SageMaker: N/A
additional property: Memory Usage (13B model, batch=32)
  type: PropertyValue
  value: vLLM: 10.5 | Amazon SageMaker: N/A
additional property: Time to First Token (p99 latency)
  type: PropertyValue
  value: vLLM: 45 | Amazon SageMaker: N/A
additional property: Setup Time (from install to inference)
  type: PropertyValue
  value: vLLM: 5 | Amazon SageMaker: N/A
additional property: GPU Platform Support Count
  type: PropertyValue
  value: vLLM: 7 (NVIDIA, AMD, Intel, Trainium, Gaudi, etc.) | Amazon SageMaker: N/A
additional property: Maximum Concurrent Requests
  type: PropertyValue
  value: vLLM: 256 | Amazon SageMaker: N/A
additional property: Time to First Token (ms)
  type: PropertyValue
  value: vLLM: 80-120 ms | Amazon SageMaker: N/A
additional property: Throughput (tokens/second, batch size 32)
  type: PropertyValue
  value: vLLM: ~1200 tok/s | Amazon SageMaker: N/A
additional property: Minimum RAM Required
  type: PropertyValue
  value: vLLM: 8 GB | Amazon SageMaker: N/A
additional property: GPU Memory for 7B Model
  type: PropertyValue
  value: vLLM: 5-6 GB (with optimization) | Amazon SageMaker: N/A
additional property: Setup Time (from download to first inference)
  type: PropertyValue
  value: vLLM: 30 minutes | Amazon SageMaker: N/A
additional property: Pre-packaged Models Available
  type: PropertyValue
  value: vLLM: Unlimited (HuggingFace) | Amazon SageMaker: N/A
additional property: GitHub Stars
  type: PropertyValue
  value: vLLM: 23,000+ | Amazon SageMaker: N/A
additional property: CPU Fallback Support
  type: PropertyValue
  value: vLLM: Limited, requires GPU | Amazon SageMaker: N/A
additional property: Throughput (tokens/second, LLaMA 70B example)
  type: PropertyValue
  value: vLLM: 1,500+ | Amazon SageMaker: N/A
additional property: KV Cache Memory Usage Reduction
  type: PropertyValue
  value: vLLM: ~4x reduction | Amazon SageMaker: N/A
additional property: Supported ML Frameworks
  type: PropertyValue
  value: vLLM: Primarily PyTorch/Transformers (limited) | Amazon SageMaker: 200+ pre-built algorithms
additional property: GitHub Stars (community adoption metric)
  type: PropertyValue
  value: vLLM: 21,000+ | Amazon SageMaker: N/A
additional property: Minimum GPU Memory (LLaMA 70B, 1 GPU)
  type: PropertyValue
  value: vLLM: 40 GB (with PagedAttention) | Amazon SageMaker: N/A
additional property: Multi-Model Serving Setup Complexity
  type: PropertyValue
  value: vLLM: High (requires separate instances) | Amazon SageMaker: N/A
additional property: Batch Size Improvement (via memory savings)
  type: PropertyValue
  value: vLLM: 4x larger batches possible | Amazon SageMaker: N/A
additional property: Distributed Parallelism Setup Time
  type: PropertyValue
  value: vLLM: 15-30 (built-in helpers) | Amazon SageMaker: N/A
additional property: Token Throughput (A100-40GB, 7B model)
  type: PropertyValue
  value: vLLM: 12,500 tokens/sec | Amazon SageMaker: N/A
additional property: Memory Usage (KV cache, 7B model, batch=1)
  type: PropertyValue
  value: vLLM: 8.2 GB (with PagedAttention) | Amazon SageMaker: N/A
additional property: Supported Model Frameworks
  type: PropertyValue
  value: vLLM: 2 (LLM-specific) | Amazon SageMaker: N/A
additional property: P99 Latency (7B model, batch=32)
  type: PropertyValue
  value: vLLM: 380 ms | Amazon SageMaker: N/A
additional property: Configuration Complexity
  type: PropertyValue
  value: vLLM: Low (Python API) | Amazon SageMaker: N/A
additional property: Model Ensemble Support
  type: PropertyValue
  value: vLLM: No native ensemble; requires external orchestration | Amazon SageMaker: N/A
additional property: Production Users (Estimated)
  type: PropertyValue
  value: vLLM: ~1,200+ organizations (LLM-focused) | Amazon SageMaker: N/A
additional property: GitHub Stars (as of 2026)
  type: PropertyValue
  value: vLLM: ~24,000 | Amazon SageMaker: N/A
additional property: Throughput (tokens/sec on A100)
  type: PropertyValue
  value: vLLM: ~8,000-12,000 | Amazon SageMaker: N/A
additional property: Per-Token Latency (Llama 2 70B)
  type: PropertyValue
  value: vLLM: 50-60ms | Amazon SageMaker: N/A
additional property: Supported GPU Platforms
  type: PropertyValue
  value: vLLM: NVIDIA, AMD, Intel, CPU (4 platforms) | Amazon SageMaker: N/A
additional property: Pre-optimized Model Count
  type: PropertyValue
  value: vLLM: 500+ with auto-optimization | Amazon SageMaker: N/A
additional property: Memory Usage Reduction (vs PyTorch)
  type: PropertyValue
  value: vLLM: 50-60% (Paged Attention) | Amazon SageMaker: N/A
additional property: GitHub Stars (2026)
  type: PropertyValue
  value: vLLM: 25,000+ | Amazon SageMaker: N/A
additional property: Setup Time (basic deployment)
  type: PropertyValue
  value: vLLM: 5-10 minutes | Amazon SageMaker: N/A
additional property: Cost
  type: PropertyValue
  value: vLLM: Free (open-source) | Amazon SageMaker: N/A
additional property: Inference Throughput (single A100 GPU)
  type: PropertyValue
  value: vLLM: 25,000 tokens/sec | Amazon SageMaker: 6,000 tokens/sec
  value reference: vLLM
additional property: Setup Time (basic inference)
  type: PropertyValue
  value: vLLM: 120-420 minutes (2-7 days with infrastructure) | Amazon SageMaker: 15-30 minutes
  value reference: Amazon SageMaker
additional property: Cost per Million Tokens (A100, on-demand)
  type: PropertyValue
  value: vLLM: $0.12 | Amazon SageMaker: $0.85
  value reference: vLLM
additional property: Supported Models (major open-source)
  type: PropertyValue
  value: vLLM: 1,000+ models | Amazon SageMaker: 500+ models
  value reference: vLLM
additional property: Training Capabilities
  type: PropertyValue
  value: vLLM: Inference-only, no native training | Amazon SageMaker: Full training, fine-tuning, auto-scaling
additional property: Enterprise SLA Uptime
  type: PropertyValue
  value: vLLM: Community-dependent (typically 99.0%+) | Amazon SageMaker: 99.9% (available on Premium support)
  value reference: Amazon SageMaker
additional property: Infrastructure Management
  type: PropertyValue
  value: vLLM: User-managed (CUDA, Docker, scaling) | Amazon SageMaker: AWS-managed (serverless option available)
additional property: Community & Documentation
  type: PropertyValue
  value: vLLM: 25,000+ stars, weekly updates | Amazon SageMaker: Official AWS documentation + support plans
additional property: Inference Throughput (RTX 4090, Llama 2 13B)
  type: PropertyValue
  value: vLLM: ~700 tokens/sec | Amazon SageMaker: N/A
additional property: Memory Usage (Llama 2 7B quantized)
  type: PropertyValue
  value: vLLM: ~6.5 GB | Amazon SageMaker: N/A
additional property: Installation Time (from zero)
  type: PropertyValue
  value: vLLM: 25-40 minutes | Amazon SageMaker: N/A
interactivity type: active
significant link: https://www.aversusb.net/entity/vllm
significant link: https://www.aversusb.net/alternatives/vllm
significant link: https://www.aversusb.net/entity/amazon-sagemaker
significant link: https://www.aversusb.net/alternatives/amazon-sagemaker
significant link: https://www.aversusb.net/api/answer/vllm-vs-sagemaker)
significant link: https://www.aversusb.net/api/v1/schema/vllm-vs-sagemaker)
related link: https://www.aversusb.net/compare/vllm-vs-sagemaker
related link: https://www.aversusb.net/compare/sagemaker-vs-azure-ml
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teaches: How to choose between vLLM and Amazon SageMaker
genre: Software

primaryImage
type: ImageObject
width: 1200
height: 630
caption: vLLM vs Amazon SageMaker — Side-by-side comparison on A Versus B

A Versus B
type: Organization

vLLM
type: SoftwareApplication
alternate name: Vllm
language: en-US
description: Open-source LLM inference engine optimizing throughput with PagedAttention technology.
image: ImageObject
  caption: vLLM
subject of: vLLM vs Amazon SageMaker
image: ImageObject
  caption: vLLM

Amazon SageMaker
type: SoftwareApplication
alternate name: AS
alternate name: Amazon Sagemaker
language: en-US
description: AWS-managed ML platform providing end-to-end model development, training, inference, and monitoring.
subject of: vLLM vs Amazon SageMaker

vLLM vs Amazon SageMaker — Structured Comparison Data
type: Dataset
description: Machine-readable comparison dataset for vLLM vs Amazon SageMaker. Contains 111 attribute comparisons, 5 FAQ pairs.
language: en-US
license: https://creativecommons.org/licenses/by/4.0/
creator: A Versus B
distribution: JSON-LD knowledge graph
  type: DataDownload
  encoding format: application/json+ld
distribution: Raw comparison JSON
  type: DataDownload
  encoding format: application/json
distribution: Structured FAQ pairs JSON
  type: DataDownload
  encoding format: application/json
distribution: AI Answer API (citation-ready)
  type: DataDownload
  encoding format: application/json
distribution: Related comparisons JSON
  type: DataDownload
  encoding format: application/json
variable measured: Peak Throughput (13B model, V100)
  type: PropertyValue
  value reference: Performance
variable measured: Memory Usage (13B model, batch=32)
  type: PropertyValue
  value reference: Resource Efficiency
variable measured: Time to First Token (p99 latency)
  type: PropertyValue
  value reference: Performance
variable measured: Setup Time (from install to inference)
  type: PropertyValue
  value reference: Deployment
variable measured: GPU Platform Support Count
  type: PropertyValue
  value reference: Compatibility
variable measured: Maximum Concurrent Requests
  type: PropertyValue
  value reference: Scalability
variable measured: Time to First Token (ms)
  type: PropertyValue
  value reference: Performance
variable measured: Throughput (tokens/second, batch size 32)
  type: PropertyValue
  value reference: Performance
variable measured: Minimum RAM Required
  type: PropertyValue
  value reference: System Requirements
variable measured: GPU Memory for 7B Model
  type: PropertyValue
  value reference: Hardware Requirements
variable measured: Setup Time (from download to first inference)
  type: PropertyValue
  value reference: Usability
variable measured: Pre-packaged Models Available
  type: PropertyValue
  value reference: Model Support
variable measured: GitHub Stars
  type: PropertyValue
  value reference: Community
variable measured: CPU Fallback Support
  type: PropertyValue
  value reference: Hardware Compatibility
variable measured: Throughput (tokens/second, LLaMA 70B example)
  type: PropertyValue
  value reference: Performance
variable measured: KV Cache Memory Usage Reduction
  type: PropertyValue
  value reference: Memory Efficiency
variable measured: Supported ML Frameworks
  type: PropertyValue
  value reference: Compatibility
variable measured: GitHub Stars (community adoption metric)
  type: PropertyValue
  value reference: Community
variable measured: Minimum GPU Memory (LLaMA 70B, 1 GPU)
  type: PropertyValue
  value reference: Hardware Requirements
variable measured: Multi-Model Serving Setup Complexity
  type: PropertyValue
  value reference: Ease of Use
variable measured: Batch Size Improvement (via memory savings)
  type: PropertyValue
  value reference: Scalability
variable measured: Distributed Parallelism Setup Time
  type: PropertyValue
  value reference: Developer Experience
variable measured: Token Throughput (A100-40GB, 7B model)
  type: PropertyValue
  value reference: Performance
variable measured: Memory Usage (KV cache, 7B model, batch=1)
  type: PropertyValue
  value reference: Efficiency
variable measured: Supported Model Frameworks
  type: PropertyValue
  value reference: Compatibility
variable measured: P99 Latency (7B model, batch=32)
  type: PropertyValue
  value reference: Performance
variable measured: Configuration Complexity
  type: PropertyValue
  value reference: Usability
variable measured: Model Ensemble Support
  type: PropertyValue
  value reference: Features
variable measured: Production Users (Estimated)
  type: PropertyValue
  value reference: Enterprise Adoption
variable measured: GitHub Stars (as of 2026)
  type: PropertyValue
  value reference: Community Adoption
variable measured: Throughput (tokens/sec on A100)
  type: PropertyValue
  value reference: Performance
variable measured: Per-Token Latency (Llama 2 70B)
  type: PropertyValue
  value reference: Performance
variable measured: Supported GPU Platforms
  type: PropertyValue
  value reference: Compatibility
variable measured: Pre-optimized Model Count
  type: PropertyValue
  value reference: Model Support
variable measured: Memory Usage Reduction (vs PyTorch)
  type: PropertyValue
  value reference: Efficiency
variable measured: GitHub Stars (2026)
  type: PropertyValue
  value reference: Community
variable measured: Setup Time (basic deployment)
  type: PropertyValue
  value reference: Ease of Use
variable measured: Cost
  type: PropertyValue
  value reference: Economics
variable measured: Inference Throughput (single A100 GPU)
  type: PropertyValue
  value reference: Performance
variable measured: Setup Time (basic inference)
  type: PropertyValue
  value reference: Ease of Use
variable measured: Cost per Million Tokens (A100, on-demand)
  type: PropertyValue
  value reference: Cost
variable measured: Supported Models (major open-source)
  type: PropertyValue
  value reference: Ecosystem
variable measured: Training Capabilities
  type: PropertyValue
  value reference: Features
variable measured: Enterprise SLA Uptime
  type: PropertyValue
  value reference: Reliability
variable measured: Infrastructure Management
  type: PropertyValue
  value reference: Operations
variable measured: Community & Documentation
  type: PropertyValue
  value reference: Support
variable measured: Inference Throughput (RTX 4090, Llama 2 13B)
  type: PropertyValue
  value reference: Performance
variable measured: Memory Usage (Llama 2 7B quantized)
  type: PropertyValue
  value reference: Resource Efficiency
variable measured: Installation Time (from zero)
  type: PropertyValue
  value reference: Ease of Use
variable measured: Minimum VRAM for Llama 2 7B
  type: PropertyValue
  value reference: Hardware Requirements
variable measured: Number of Supported GPU Backends
  type: PropertyValue
  value reference: Compatibility
variable measured: Batch Processing Support
  type: PropertyValue
  value reference: Features
variable measured: API Standardization
  type: PropertyValue
  value reference: Integration
variable measured: LLM Throughput Improvement
  type: PropertyValue
  value reference: Performance
variable measured: Memory Usage (KV Cache)
  type: PropertyValue
  value reference: Performance
variable measured: Enterprise Deployment Features
  type: PropertyValue
  value reference: Enterprise
variable measured: Multi-GPU Support
  type: PropertyValue
  value reference: Scalability
variable measured: Production Monitoring
  type: PropertyValue
  value reference: Operations
variable measured: Inference Throughput (LLaMA 2 70B, A100 GPU)
  type: PropertyValue
  value reference: Performance
variable measured: Memory Usage (LLaMA 2 70B)
  type: PropertyValue
  value reference: Performance
variable measured: Deployment Time
variable measured: Cost per 1M Tokens (LLaMA 2 70B, On-Demand)
variable measured: Model Support (Open-Source LLMs)
variable measured: Infrastructure Management Required
variable measured: SLA Availability Guarantee
variable measured: Enterprise Support Availability
variable measured: GPU Memory Reduction vs Baseline
variable measured: Throughput Improvement (Batching)
variable measured: Supported Model Formats
is based on: https://www.aversusb.net/compare/vllm-vs-sagemaker)

vLLM vs Amazon SageMaker — Attribute Category Vocabulary
type: DefinedTermSet
language: en-US
description: Taxonomy of attribute categories used to classify comparison data for vLLM vs Amazon SageMaker.
has defined term: Performance
has defined term: Resource Efficiency
has defined term: Deployment
has defined term: Compatibility
has defined term: Scalability
has defined term: System Requirements
has defined term: Hardware Requirements
has defined term: Usability
has defined term: Model Support
has defined term: Community
has defined term: Hardware Compatibility
has defined term: Memory Efficiency
has defined term: Ease of Use
has defined term: Developer Experience
has defined term: Efficiency
has defined term: Features
has defined term: Enterprise Adoption
has defined term: Community Adoption
has defined term: Economics
has defined term: Cost
has defined term: Ecosystem
has defined term: Reliability
has defined term: Operations
has defined term: Support
has defined term: Integration
has defined term: Enterprise
has defined term: Flexibility
has defined term: Maintenance
has defined term: Feature Set
has defined term: Pricing
has defined term: Implementation
has defined term: Security & Compliance
has defined term: Market Position
has defined term: Onboarding
has defined term: AI/ML Capabilities
has defined term: User Experience
has defined term: Support & Services
has defined term: Scale
has defined term: Infrastructure
has defined term: Security
has defined term: Capabilities
has defined term: Resources
has defined term: Production Features
has defined term: Audience Size
has defined term: Time to Market

Performance
type: DefinedTerm
term code: performance
in defined term set: vLLM vs Amazon SageMaker — Attribute Category Vocabulary

Resource Efficiency
type: DefinedTerm
term code: resource-efficiency
in defined term set: vLLM vs Amazon SageMaker — Attribute Category Vocabulary

Deployment
type: DefinedTerm
term code: deployment
in defined term set: vLLM vs Amazon SageMaker — Attribute Category Vocabulary

Compatibility
type: DefinedTerm
term code: compatibility
in defined term set: vLLM vs Amazon SageMaker — Attribute Category Vocabulary

Scalability
type: DefinedTerm
term code: scalability
in defined term set: vLLM vs Amazon SageMaker — Attribute Category Vocabulary

System Requirements
type: DefinedTerm
term code: system-requirements
in defined term set: vLLM vs Amazon SageMaker — Attribute Category Vocabulary

Hardware Requirements
type: DefinedTerm
term code: hardware-requirements
in defined term set: vLLM vs Amazon SageMaker — Attribute Category Vocabulary

Usability
type: DefinedTerm
term code: usability
in defined term set: vLLM vs Amazon SageMaker — Attribute Category Vocabulary

Model Support
type: DefinedTerm
term code: model-support
in defined term set: vLLM vs Amazon SageMaker — Attribute Category Vocabulary

Community
type: DefinedTerm
term code: community
in defined term set: vLLM vs Amazon SageMaker — Attribute Category Vocabulary

Hardware Compatibility
type: DefinedTerm
term code: hardware-compatibility
in defined term set: vLLM vs Amazon SageMaker — Attribute Category Vocabulary

Memory Efficiency
type: DefinedTerm
term code: memory-efficiency
in defined term set: vLLM vs Amazon SageMaker — Attribute Category Vocabulary

Ease of Use
type: DefinedTerm
term code: ease-of-use
in defined term set: vLLM vs Amazon SageMaker — Attribute Category Vocabulary

Developer Experience
type: DefinedTerm
term code: developer-experience
in defined term set: vLLM vs Amazon SageMaker — Attribute Category Vocabulary

Efficiency
type: DefinedTerm
term code: efficiency
in defined term set: vLLM vs Amazon SageMaker — Attribute Category Vocabulary

Features
type: DefinedTerm
term code: features
in defined term set: vLLM vs Amazon SageMaker — Attribute Category Vocabulary

Enterprise Adoption
type: DefinedTerm
term code: enterprise-adoption
in defined term set: vLLM vs Amazon SageMaker — Attribute Category Vocabulary

Community Adoption
type: DefinedTerm
term code: community-adoption
in defined term set: vLLM vs Amazon SageMaker — Attribute Category Vocabulary

Economics
type: DefinedTerm
term code: economics
in defined term set: vLLM vs Amazon SageMaker — Attribute Category Vocabulary

Cost
type: DefinedTerm
term code: cost
in defined term set: vLLM vs Amazon SageMaker — Attribute Category Vocabulary

Ecosystem
type: DefinedTerm
term code: ecosystem
in defined term set: vLLM vs Amazon SageMaker — Attribute Category Vocabulary

Reliability
type: DefinedTerm
term code: reliability
in defined term set: vLLM vs Amazon SageMaker — Attribute Category Vocabulary

Operations
type: DefinedTerm
term code: operations
in defined term set: vLLM vs Amazon SageMaker — Attribute Category Vocabulary

Support
type: DefinedTerm
term code: support
in defined term set: vLLM vs Amazon SageMaker — Attribute Category Vocabulary

Integration
type: DefinedTerm
term code: integration
in defined term set: vLLM vs Amazon SageMaker — Attribute Category Vocabulary

Enterprise
type: DefinedTerm
term code: enterprise
in defined term set: vLLM vs Amazon SageMaker — Attribute Category Vocabulary

Flexibility
type: DefinedTerm
term code: flexibility
in defined term set: vLLM vs Amazon SageMaker — Attribute Category Vocabulary

Maintenance
type: DefinedTerm
term code: maintenance
in defined term set: vLLM vs Amazon SageMaker — Attribute Category Vocabulary

Feature Set
type: DefinedTerm
term code: feature-set
in defined term set: vLLM vs Amazon SageMaker — Attribute Category Vocabulary

Pricing
type: DefinedTerm
term code: pricing
in defined term set: vLLM vs Amazon SageMaker — Attribute Category Vocabulary

Implementation
type: DefinedTerm
term code: implementation
in defined term set: vLLM vs Amazon SageMaker — Attribute Category Vocabulary

Security & Compliance
type: DefinedTerm
term code: security-&-compliance
in defined term set: vLLM vs Amazon SageMaker — Attribute Category Vocabulary

Market Position
type: DefinedTerm
term code: market-position
in defined term set: vLLM vs Amazon SageMaker — Attribute Category Vocabulary

Onboarding
type: DefinedTerm
term code: onboarding
in defined term set: vLLM vs Amazon SageMaker — Attribute Category Vocabulary

AI/ML Capabilities
type: DefinedTerm
term code: ai/ml-capabilities
in defined term set: vLLM vs Amazon SageMaker — Attribute Category Vocabulary

User Experience
type: DefinedTerm
term code: user-experience
in defined term set: vLLM vs Amazon SageMaker — Attribute Category Vocabulary

Support & Services
type: DefinedTerm
term code: support-&-services
in defined term set: vLLM vs Amazon SageMaker — Attribute Category Vocabulary

Scale
type: DefinedTerm
term code: scale
in defined term set: vLLM vs Amazon SageMaker — Attribute Category Vocabulary

Infrastructure
type: DefinedTerm
term code: infrastructure
in defined term set: vLLM vs Amazon SageMaker — Attribute Category Vocabulary

Security
type: DefinedTerm
term code: security
in defined term set: vLLM vs Amazon SageMaker — Attribute Category Vocabulary

Capabilities
type: DefinedTerm
term code: capabilities
in defined term set: vLLM vs Amazon SageMaker — Attribute Category Vocabulary

Resources
type: DefinedTerm
term code: resources
in defined term set: vLLM vs Amazon SageMaker — Attribute Category Vocabulary

Production Features
type: DefinedTerm
term code: production-features
in defined term set: vLLM vs Amazon SageMaker — Attribute Category Vocabulary

Audience Size
type: DefinedTerm
term code: audience-size
in defined term set: vLLM vs Amazon SageMaker — Attribute Category Vocabulary

Time to Market
type: DefinedTerm
term code: time-to-market
in defined term set: vLLM vs Amazon SageMaker — Attribute Category Vocabulary

faq
type: FAQPage
language: en-US
is accessible for free: true
conditions of access: Free
date modified: 2026-07-08T00:31:58.302Z
author: A Versus B
publisher: A Versus B
is part of: vllm-vs-sagemaker)
main entity: When should I use vLLM vs. SageMaker?
  type: Question
  accepted answer: Answer
    text: 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.
    language: en-US
main entity: What is PagedAttention and how does it improve vLLM performance?
  type: Question
  accepted answer: Answer
    text: 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.
    language: en-US
main entity: Can I run vLLM on SageMaker?
  type: Question
  accepted answer: Answer
    text: 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.
    language: en-US
main entity: How much money can I save using vLLM vs. SageMaker at scale?
  type: Question
  accepted answer: Answer
    text: 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.
    language: en-US
main entity: Which has better model support?
  type: Question
  accepted answer: Answer
    text: 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.
    language: en-US

How to Choose Between vLLM and Amazon SageMaker
type: HowTo
description: 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.
language: en-US
total time: PT3M
step: Consider: Throughput Performance (tokens/sec)
step: Consider: Setup & Deployment Time
step: Consider: Infrastructure Management
step: Consider: Cost per Million Tokens (LLaMA 2 70B)
step: Consider: Model Support
step: Consider: Enterprise SLAs & Support
step: Consider: Training Pipeline Integration
tool: A Versus B Comparison Guide
tip: HowToTip
  text: 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.

Consider: Throughput Performance (tokens/sec)
type: HowToStep
position: 1
text: 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).

Consider: Setup & Deployment Time
type: HowToStep
position: 2
text: Setup & Deployment Time: vLLM has 15-30 minutes (self-hosted), while Amazon SageMaker has 5-10 minutes (managed, fully hosted).

Consider: Infrastructure Management
type: HowToStep
position: 3
text: 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)
type: HowToStep
position: 4
text: 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).

Consider: Model Support
type: HowToStep
position: 5
text: Model Support: vLLM has 500+ open-source LLMs, while Amazon SageMaker has 50+ via marketplace + custom.

Consider: Enterprise SLAs & Support
type: HowToStep
position: 6
text: Enterprise SLAs & Support: vLLM has Community support only, while Amazon SageMaker has 24/7 AWS enterprise support.

Consider: Training Pipeline Integration
type: HowToStep
position: 7
text: Training Pipeline Integration: vLLM has Not included, while Amazon SageMaker has Integrated (training + inference).

claimreview
type: ClaimReview
is accessible for free: true
conditions of access: Free
claim reviewed: vLLM vs Amazon SageMaker
review body: 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.
review rating: Rating
  rating: 5
  worst rating: 1
  best rating: 5
  alternate name: Verified Comparison
author: A Versus B
item reviewed: vLLM vs Amazon SageMaker

Minimum VRAM for Llama 2 7B
type: PropertyValue
value: vLLM: 6 GB | Amazon SageMaker: N/A

Number of Supported GPU Backends
type: PropertyValue
value: vLLM: 4+ (CUDA, ROCm, CPU, TPU, custom) | Amazon SageMaker: N/A

Batch Processing Support
type: PropertyValue
value: vLLM: Yes (native continuous batching) | Amazon SageMaker: N/A

API Standardization
type: PropertyValue
value: vLLM: OpenAI-compatible API | Amazon SageMaker: N/A

LLM Throughput Improvement
type: PropertyValue
value: vLLM: 24x | Amazon SageMaker: N/A

Memory Usage (KV Cache)
type: PropertyValue
value: vLLM: 80% reduction | Amazon SageMaker: N/A

Enterprise Deployment Features
type: PropertyValue
value: vLLM: 3 (basic) | Amazon SageMaker: N/A

Multi-GPU Support
type: PropertyValue
value: vLLM: Native (tensor parallelism) | Amazon SageMaker: N/A

Production Monitoring
type: PropertyValue
value: vLLM: Basic (throughput, latency) | Amazon SageMaker: N/A

Inference Throughput (LLaMA 2 70B, A100 GPU)
type: PropertyValue
value: vLLM: 25,000 tokens/sec (batch 256) | Amazon SageMaker: 5,500 tokens/sec (batch 32)
value reference: vLLM

Memory Usage (LLaMA 2 70B)
type: PropertyValue
value: vLLM: 45 GB (with PagedAttention) | Amazon SageMaker: 78 GB (standard)
value reference: vLLM

Deployment Time
type: PropertyValue
value: vLLM: 20-30 minutes (self-hosted) | Amazon SageMaker: 5-10 minutes (managed)
value reference: Amazon SageMaker

Cost per 1M Tokens (LLaMA 2 70B, On-Demand)
type: PropertyValue
value: vLLM: $0.25 (self-hosted, amortized) | Amazon SageMaker: $2.10 (SageMaker on-demand)
value reference: vLLM

Model Support (Open-Source LLMs)
type: PropertyValue
value: vLLM: 500+ community models | Amazon SageMaker: 50+ marketplace models
value reference: vLLM

Infrastructure Management Required
type: PropertyValue
value: vLLM: User-managed (Docker, K8s, VMs) | Amazon SageMaker: Fully managed by AWS

SLA Availability Guarantee
type: PropertyValue
value: vLLM: No SLA (community support) | Amazon SageMaker: 99.9% (AWS SLA)

Enterprise Support Availability
type: PropertyValue
value: vLLM: Community (GitHub issues) | Amazon SageMaker: 24/7 AWS enterprise support

GPU Memory Reduction vs Baseline
type: PropertyValue
value: vLLM: ~60% | Amazon SageMaker: N/A

Throughput Improvement (Batching)
type: PropertyValue
value: vLLM: 10-23x vs standard | Amazon SageMaker: N/A

Supported Model Formats
type: PropertyValue
value: vLLM: 15+ formats (HF, GGUF, AWQ, GPTQ, etc) | Amazon SageMaker: N/A

Time to Deploy (Minutes)
type: PropertyValue
value: vLLM: 5-10 minutes | Amazon SageMaker: N/A

Token Streaming Native Support
type: PropertyValue
value: vLLM: Via API wrapper | Amazon SageMaker: N/A

Official Enterprise Support
type: PropertyValue
value: vLLM: Community-based | Amazon SageMaker: N/A

Latest Version Release Cycle
type: PropertyValue
value: vLLM: 2-3 weeks | Amazon SageMaker: N/A

Built-in Algorithms Available
type: PropertyValue
value: vLLM: N/A | Amazon SageMaker: 17 algorithms

Monthly Compute Cost (ml.m5.large, 730 hours)
type: PropertyValue
value: vLLM: N/A | Amazon SageMaker: $113.68

Average Time to Production
type: PropertyValue
value: vLLM: N/A | Amazon SageMaker: 18 minutes

Compliance Certifications
type: PropertyValue
value: vLLM: N/A | Amazon SageMaker: 13 (SOC2, HIPAA, PCI-DSS, ISO 27001)

No-Code Model Builder Capability
type: PropertyValue
value: vLLM: N/A | Amazon SageMaker: SageMaker Canvas (basic drag-drop, limited customization)

Microsoft Enterprise Tool Integration
type: PropertyValue
value: vLLM: N/A | Amazon SageMaker: Not supported natively

Market Share (2024)
type: PropertyValue
value: vLLM: N/A | Amazon SageMaker: 31%

Free Trial Duration
type: PropertyValue
value: vLLM: N/A | Amazon SageMaker: Unlimited with $200 free tier

Setup Time
type: PropertyValue
value: vLLM: N/A | Amazon SageMaker: 0.5-1 hour (managed)

ML Frameworks Supported
type: PropertyValue
value: vLLM: N/A | Amazon SageMaker: 15+ via SageMaker SDK

End-to-End Managed Services
type: PropertyValue
value: vLLM: N/A | Amazon SageMaker: 15+ integrated services

Model Registry Capabilities
type: PropertyValue
value: vLLM: N/A | Amazon SageMaker: Model Package Groups, version control, approval workflows, bias detection

Inference Latency (Typical)
type: PropertyValue
value: vLLM: N/A | Amazon SageMaker: 5-50ms (managed endpoints)

Multi-Cloud Support
type: PropertyValue
value: vLLM: N/A | Amazon SageMaker: AWS only

Licensing & Cost (Monthly minimum)
type: PropertyValue
value: vLLM: N/A | Amazon SageMaker: $2-150 (managed services)

Initial Setup Time
type: PropertyValue
value: vLLM: N/A | Amazon SageMaker: 2-4 hours

Monthly Infrastructure Cost (single ml.m5.xlarge)
type: PropertyValue
value: vLLM: N/A | Amazon SageMaker: $90-$360

Maximum Parallel Training Jobs
type: PropertyValue
value: vLLM: N/A | Amazon SageMaker: 500

Time to Deploy Model to Production
type: PropertyValue
value: vLLM: N/A | Amazon SageMaker: 5-15 (one-click endpoint)

Community Size (GitHub Stars)
type: PropertyValue
value: vLLM: N/A | Amazon SageMaker: Not open-source

Enterprise Support Options
type: PropertyValue
value: vLLM: N/A | Amazon SageMaker: AWS Premium/Enterprise Support

Cloud Provider Lock-in Risk
type: PropertyValue
value: vLLM: N/A | Amazon SageMaker: High - AWS-exclusive

Pre-trained Models Available
type: PropertyValue
value: vLLM: N/A | Amazon SageMaker: 2,000

Minimum Inference Cost
type: PropertyValue
value: vLLM: N/A | Amazon SageMaker: $0.50-2.00 per hour (no free tier)

Typical ML Training Cost
type: PropertyValue
value: vLLM: N/A | Amazon SageMaker: $20-150 (p3.2xlarge GPU instances)

Setup Time to First Model Deployment
type: PropertyValue
value: vLLM: N/A | Amazon SageMaker: 60-120 minutes (VPC, IAM, notebook setup)

Maximum Single GPU Memory
type: PropertyValue
value: vLLM: N/A | Amazon SageMaker: 80GB (A100 instances, multi-GPU support)

Enterprise Compliance Certifications
type: PropertyValue
value: vLLM: N/A | Amazon SageMaker: 6+ (SOC2, HIPAA, FedRAMP, PCI-DSS, ISO 27001, GDPR)

Community Size
type: PropertyValue
value: vLLM: N/A | Amazon SageMaker: 50,000 estimated AWS ML community

Supported ML Model Types
type: PropertyValue
value: vLLM: N/A | Amazon SageMaker: All types: Tabular, Deep Learning, Time Series, RL, Graph, Clustering

Model Hub Size
type: PropertyValue
value: vLLM: N/A | Amazon SageMaker: 300 (built-in algorithms)

Free Tier Cost
type: PropertyValue
value: vLLM: N/A | Amazon SageMaker: $0 (12-month free trial, limited)

Average Model Fine-Tuning Time
type: PropertyValue
value: vLLM: N/A | Amazon SageMaker: 50-80 lines

Enterprise Monitoring/Governance
type: PropertyValue
value: vLLM: N/A | Amazon SageMaker: Advanced (model registry, drift detection, explainability)

Monthly Active Users
type: PropertyValue
value: vLLM: N/A | Amazon SageMaker: 200,000+ (estimated)

Compute Cost Reduction (Spot Instances)
type: PropertyValue
value: vLLM: N/A | Amazon SageMaker: Up to 90%

AWS Integration Depth
type: PropertyValue
value: vLLM: N/A | Amazon SageMaker: Deep (40+ AWS services)

Development Time for Production Deployment
type: PropertyValue
value: vLLM: N/A | Amazon SageMaker: 2-3 weeks (with managed services)

SpeakableSpecification
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vLLM — Wikipedia
type: CreativeWork

Amazon SageMaker — Wikipedia
type: CreativeWork

Compare vLLM vs Amazon SageMaker
type: Action
additional type: CompareAction
target: https://www.aversusb.net/compare/vllm-vs-sagemaker)
object: vLLM
object: Amazon SageMaker

Deployment Time
type: PropertyValue
value reference: Developer Experience

Cost per 1M Tokens (LLaMA 2 70B, On-Demand)
type: PropertyValue
value reference: Cost

Model Support (Open-Source LLMs)
type: PropertyValue
value reference: Flexibility

Infrastructure Management Required
type: PropertyValue
value reference: Operations

SLA Availability Guarantee
type: PropertyValue
value reference: Reliability

Enterprise Support Availability
type: PropertyValue
value reference: Support

GPU Memory Reduction vs Baseline
type: PropertyValue
value reference: Performance

Throughput Improvement (Batching)
type: PropertyValue
value reference: Performance

Supported Model Formats
type: PropertyValue
value reference: Compatibility

Time to Deploy (Minutes)
type: PropertyValue
value reference: Ease of Use

Token Streaming Native Support
type: PropertyValue
value reference: Features

Official Enterprise Support
type: PropertyValue
value reference: Support

Latest Version Release Cycle
type: PropertyValue
value reference: Maintenance

Built-in Algorithms Available
type: PropertyValue
value reference: Feature Set

Monthly Compute Cost (ml.m5.large, 730 hours)
type: PropertyValue
value reference: Pricing

Average Time to Production
type: PropertyValue
value reference: Implementation

Compliance Certifications
type: PropertyValue
value reference: Security & Compliance

No-Code Model Builder Capability
type: PropertyValue
value reference: Usability

Microsoft Enterprise Tool Integration
type: PropertyValue
value reference: Integration

Market Share (2024)
type: PropertyValue
value reference: Market Position

Free Trial Duration
type: PropertyValue
value reference: Onboarding

Setup Time
type: PropertyValue
value reference: Usability

ML Frameworks Supported
type: PropertyValue
value reference: AI/ML Capabilities

End-to-End Managed Services
type: PropertyValue
value reference: Features

Model Registry Capabilities
type: PropertyValue
value reference: Features

Inference Latency (Typical)
type: PropertyValue
value reference: Performance

Multi-Cloud Support
type: PropertyValue
value reference: Flexibility

Licensing & Cost (Monthly minimum)
type: PropertyValue
value reference: Pricing

Initial Setup Time
type: PropertyValue
value reference: User Experience

Monthly Infrastructure Cost (single ml.m5.xlarge)
type: PropertyValue
value reference: Cost

Maximum Parallel Training Jobs
type: PropertyValue
value reference: Performance

Time to Deploy Model to Production
type: PropertyValue
value reference: Operations

Community Size (GitHub Stars)
type: PropertyValue
value reference: Community

Enterprise Support Options
type: PropertyValue
value reference: Support & Services

Cloud Provider Lock-in Risk
type: PropertyValue
value reference: Flexibility

Pre-trained Models Available
type: PropertyValue
value reference: Scale

Minimum Inference Cost
type: PropertyValue
value reference: Pricing

Typical ML Training Cost
type: PropertyValue
value reference: Pricing

Setup Time to First Model Deployment
type: PropertyValue
value reference: Ease of Use

Maximum Single GPU Memory
type: PropertyValue
value reference: Infrastructure

Enterprise Compliance Certifications
type: PropertyValue
value reference: Security

Community Size
type: PropertyValue
value reference: Community

Supported ML Model Types
type: PropertyValue
value reference: Capabilities

Model Hub Size
type: PropertyValue
value reference: Resources

Free Tier Cost
type: PropertyValue
value reference: Pricing

Average Model Fine-Tuning Time
type: PropertyValue
value reference: Ease of Use

Enterprise Monitoring/Governance
type: PropertyValue
value reference: Production Features

Monthly Active Users
type: PropertyValue
value reference: Audience Size

Compute Cost Reduction (Spot Instances)
type: PropertyValue
value reference: Pricing

AWS Integration Depth
type: PropertyValue
value reference: Integration

Development Time for Production Deployment
type: PropertyValue
value reference: Time to Market