vLLM vs SGLang: Which Inference Engine Should You Use in 2026?

As large language models move from experimentation to production, the inference layer has become one of the most critical parts of the AI stack. Performance, memory efficiency, batching behavior, and scheduling strategy now determine cost, latency, and scalability more than model architecture alone.

Two inference engines are increasingly compared in production environments: vLLM and SGLang.While both are designed to optimize LLM inference, they solve different problems and make different tradeoffs. Choosing the right one depends on workload type, orchestration needs, and infrastructure constraints.

This guide breaks down how they differ and when each makes sense in 2026.

What Is vLLM?

vLLM is an open-source high-throughput inference engine designed to maximize GPU utilization for large language models. It became widely adopted due to its implementation of PagedAttention, which significantly improves memory efficiency during autoregressive decoding.The core goal of vLLM is simple:Serve as many requests as possible per GPU while keeping latency predictable.It achieves this through:

• Efficient KV cache management
• Continuous batching
• Memory-optimized attention handling
• Strong integration with Hugging Face models

vLLM performs especially well in:

• Chat applications
• API-based inference workloads
• High-concurrency environments
• Cost-sensitive deployments

It is optimized primarily around throughput and GPU efficiency.

What Is SGLang?

SGLang approaches inference from a different angle. Instead of focusing purely on throughput, it is designed to support structured, multi-step, and programmatic generation workflows.SGLang allows developers to define structured generation logic, including:

• Tool use
• Control flow
• Conditional reasoning
• Multi-call workflows

This makes it particularly well suited for:

• Agent-based systems
• Tool-calling architectures
• Multi-step reasoning chains
• Applications requiring deterministic output formatting

Where vLLM focuses on serving requests efficiently, SGLang focuses on controlling how generation unfolds.

Performance and Throughput

In raw throughput scenarios, vLLM typically has the advantage. Its memory optimizations and batching strategy allow more concurrent requests per GPU compared to traditional inference setups.For workloads where:

• Thousands of short requests need to be served
• Chat latency must remain stable
• GPU cost per token matters

vLLM is often the stronger choice.SGLang can still perform well, but its added orchestration and structured execution layer introduces additional overhead. In exchange, you gain more control over output flow and reasoning structure.If your primary KPI is tokens per second per GPU, vLLM is usually the more direct solution.

Structured Generation and Agent Workflows

Where SGLang becomes compelling is in complex production systems.Modern AI applications are no longer simple prompt-response APIs. Many involve:

• Multi-agent loops
• Tool invocation
• Retrieval-augmented generation
• Iterative reasoning
• Conditional branching

In these environments, generation is not linear.SGLang allows you to define structured generation pipelines natively, reducing the need for heavy orchestration layers on top of the inference engine.For agent-based systems, SGLang often simplifies architecture. Instead of coordinating logic externally, part of the reasoning control lives within the generation framework.

Memory Efficiency and GPU Utilization

Memory efficiency determines how many concurrent requests you can serve.vLLM’s PagedAttention mechanism is specifically built to reduce fragmentation in the KV cache. This allows more sessions to share GPU memory efficiently, which directly reduces infrastructure cost.If your workload is:

• High concurrency
• Stateless
• Primarily chat-based
• Latency-sensitive

vLLM is typically more cost-efficient per GPU.SGLang may require more careful tuning depending on workload complexity, especially in large-scale deployments.

When to Choose vLLM

vLLM is generally the better choice if:

• You need maximum throughput per GPU
• Your workload is primarily chat or API-based
• You care deeply about cost per token
• You are optimizing for large-scale inference efficiency
• Your orchestration logic lives outside the inference engine

It is ideal for production systems where efficiency and scaling matter most.

When to Choose SGLang

SGLang makes more sense if:

• You are building multi-agent systems
• You require structured, multi-step generation
• You want native support for tool execution workflows
• Your reasoning paths are dynamic and conditional
• You prioritize workflow control over raw throughput

It is better suited for complex AI systems that behave more like programs than simple chat endpoints.

Infrastructure Considerations in 2026

The choice between vLLM and SGLang is not just a framework decision. It affects:

• GPU allocation strategy
• Autoscaling behavior
• Memory planning
• Latency predictability
• Cost modeling

In many real-world systems, teams even mix approaches. For example:

• vLLM for high-throughput serving layers
• SGLang for agent orchestration pipelines

As inference complexity grows, orchestration often becomes the bigger bottleneck than model performance itself.

Production Infrastructure Matters More Than Engine Choice

Regardless of whether you choose vLLM or SGLang, the inference engine is only one layer of the production stack.In real-world deployments, teams quickly encounter challenges around:

• Multi-GPU coordination
• Cross-region scaling
• Memory fragmentation under burst traffic
• Latency spikes from autoscaling delays
• Underutilized GPUs due to poor scheduling

This is where infrastructure architecture becomes more important than the inference engine itself.Even the most optimized engine will struggle if GPU allocation, networking, and orchestration are not designed for high-variance inference workloads.

For a deeper look at how orchestration impacts inference performance at scale, see our breakdown of why hardware alone doesn’t determine real-world latency:

Why Orchestration, Not Hardware, Determines Inference Performance at Scale

You can also explore how GPU allocation strategy affects cost efficiency in production environments:

Why GPU Utilization Matters More Than Raw GPU Count

Final Thoughts

There is no universal winner between vLLM and SGLang.vLLM is optimized for efficiency and throughput at scale.

SGLang is optimized for structured reasoning and agent-based workflows.In 2026, the real differentiator will not just be model size, but how effectively your inference layer handles orchestration, memory, and multi-step generation.

Choosing the right inference engine means understanding your workload first — then designing infrastructure around it.