vLLM vs SGLang

Side-by-side — pricing, strengths, tradeoffs, and fit.

vLLM

High-throughput and memory-efficient inference engine for large language models.

SGLang

Fast serving framework for large language models and vision language models.

At a glance

Open SourceFree Tier
● Established·From $0
Open SourceFree Tier
◐ Growing·From $0

Overview

One-liner

High-throughput and memory-efficient inference engine for large language models.

One-liner

Fast serving framework for large language models and vision language models.

Maturity

Established

Maturity

Growing

Open source

✓ Yes

Open source

✓ Yes

Pricing

Free tier

✓ Available

Open source — free to use

Free tier

✓ Available

Open source — free to use

Starts at

$0

Starts at

$0

Model

Flat rate

Model

Flat rate

Enterprise

✕ None

Enterprise

✕ None

Best for

✓ Best for

Teams deploying large language models who need high throughput and low memory usage
Projects with limited computational resources but requiring efficient model serving
Developers optimizing the performance of their applications that rely on LLMs

✓ Best for

Developers looking to deploy large language and vision-language models efficiently.
Teams requiring low-latency inference for real-time applications.

Not ideal for

✕ Not for

Applications requiring real-time streaming capabilities (batch-only architecture)
Scenarios where a managed cloud service is preferred over self-hosting

✕ Not for

Projects that require a managed cloud service without self-hosting capabilities.
Applications needing frequent model updates where re-deployment is not feasible.

Strengths

↑ Strengths

High throughput for serving large language models
Memory-efficient model inference
Optimized for resource-constrained environments

↑ Strengths

High-performance serving of large language models and vision-language models.
Optimized for low-latency inference.
Supports both CPU and GPU deployment.

Weaknesses

↓ Weaknesses

Limited documentation for advanced use cases
Small community and limited third-party integrations

↓ Weaknesses

Steep learning curve for non-C++ developers
Limited third-party integrations
Complex setup process
Documentation is sparse and not beginner-friendly
Full vLLM profile →Full SGLang profile →