vLLM vs SGLang: Choosing a Production LLM Inference Engine in Late 2026

	vLLM	SGLang
Origin	UC Berkeley Sky Computing Lab, PagedAttention paper, 2023	LMSYS Org, RadixAttention paper, 2024
Core memory technique	PagedAttention: block based KV cache allocation	RadixAttention: radix tree based KV cache reuse across requests
Best fit	Single turn, high throughput batch serving, broad model and hardware support	Multi turn chat, shared system prompts, structured generation, agent workloads
License	Apache 2.0	Apache 2.0
Ecosystem maturity	Widest model and hardware coverage of any open source serving engine	Faster growing, strong adoption at AI labs running chat and agent products

vLLM and SGLang get recommended for the same job constantly, which is the actual problem. They are not competing implementations of the same idea. They solve the KV cache memory problem, the thing that determines how many concurrent requests your GPU can actually serve, in two architecturally different ways, and which one wins depends entirely on whether your traffic looks like independent one shot completions or long, branching, prefix sharing conversations.

The architectural difference that actually matters

vLLM's foundational contribution, documented in the original PagedAttention paper out of UC Berkeley's Sky Computing Lab, treats the KV cache the way an operating system treats memory: it allocates it in fixed size blocks rather than one contiguous chunk per sequence. That eliminates the memory fragmentation that plagued earlier serving systems and lets vLLM pack far more concurrent sequences into the same GPU memory. It is a general purpose win that applies regardless of what your prompts look like, which is a major reason vLLM, maintained on GitHub at the vllm project organization, has the broadest hardware and model support of any open source serving engine today, spanning Nvidia, AMD, and increasingly Apple Silicon through community ports.

SGLang, maintained by the sgl project organization that grew out of UC Berkeley and Stanford's LMSYS Org, starts from a different observation: a huge share of real traffic, multi turn chat, agent loops that reuse a system prompt and tool definitions across many calls, few shot prompts with a shared prefix, is not actually independent. Those requests share long stretches of identical tokens at the start of the prompt. RadixAttention organizes the KV cache as a radix tree so that shared prefixes get computed once and reused across every request that shares them, instead of recomputing and re storing the same tokens' key and value states over and over. For a workload that is genuinely full of shared prefixes, that is not a marginal optimization. It changes how much of your GPU's compute goes to redundant work versus new tokens.

Where each one actually wins in practice

If your workload is single shot completions with mostly unique prompts, batch document processing, one off classification calls, independent API requests with no shared system prompt, vLLM's general purpose batching is the safer default. There is no meaningful prefix sharing for RadixAttention to exploit, so you get vLLM's broader hardware support and larger ecosystem without giving anything up on throughput.

If your workload is an agent loop, a multi turn chatbot, or anything where the same system prompt, tool schema, or few shot examples get sent on every single call, SGLang's prefix caching starts doing real work the moment the second request with a shared prefix arrives. Our own breakdown of self hosted open weight serving costs versus API pricing treats this exact distinction as the single biggest lever on your effective cost per token: an engine that recomputes a 2,000 token system prompt on every call is paying for that recomputation on every single request, and at agent scale, where a single task can trigger dozens of calls that all repeat the same tool definitions, that waste adds up fast.

SGLang's frontend language, which lets you express structured generation, parallel branches, and control flow directly rather than hand rolling prompt templates, is also worth weighing on its own merits if you are building anything agentic. It is not just a serving engine; it is a programming model for LLM applications that happens to pair naturally with RadixAttention's caching.

What this means for your infrastructure decision

Do not pick a serving engine by reading a benchmark chart from a blog that ran its own synthetic load test with undisclosed batch sizes and request patterns; those numbers rarely transfer to your traffic. Instead, profile your own request logs for prefix overlap: what fraction of your tokens are identical across consecutive requests. A low overlap workload gets little from RadixAttention and should default to vLLM's broader support and maturity. A high overlap workload, which describes most production agent systems and chat products, is leaving real throughput on the table by not using prefix caching, whether through SGLang or through vLLM's own prefix caching features, which have narrowed but not closed the gap on this specific pattern.

Hardware and model coverage still favor vLLM if you need breadth: more quantization formats, more model architectures on day one of release, and a larger base of production deployments to borrow operational playbooks from. SGLang's growing adoption inside AI labs building chat and agent products is itself a signal: the teams closest to agentic workloads at scale are choosing the engine built around the caching pattern their traffic actually produces.

Operational considerations beyond raw throughput

Throughput and memory efficiency are not the only variables that should factor into this decision. vLLM's longer production history means more operational playbooks exist for failure modes: how to handle a GPU falling out of a multi node deployment, how to tune continuous batching parameters under bursty traffic, how to debug an out of memory error under a specific model and hardware combination. That accumulated operational knowledge, spread across a larger base of production deployments, has real value when something breaks at 2am and you need a known fix rather than a novel debugging session.

SGLang's smaller but faster growing deployment base means less of that accumulated operational folklore exists publicly, though its adoption inside AI labs running serious production chat and agent traffic suggests the operational maturity gap is closing quickly. If your team is small and cannot absorb a steep operational learning curve, that maturity gap is worth weighing alongside the architectural fit. If your team already runs GPU infrastructure at scale and can debug novel failure modes without an established playbook, the architectural advantage for a prefix heavy workload should outweigh the maturity gap.

Monitoring and observability tooling is another practical factor: check whether your existing metrics and tracing stack has first class integrations for whichever engine you choose, since bolting on custom instrumentation after the fact is its own ongoing maintenance cost.

When this applies to your stack

If you are self hosting open weight models for anything with repeated system prompts, tool calling, or multi turn context, run the prefix overlap check before you commit to an engine. The architecture that matches your actual traffic pattern will beat the one with the bigger GitHub star count every time it matters, which is in your GPU bill.

FAQ

Can I switch between vLLM and SGLang without changing my model weights? Yes. Both load standard Hugging Face format weights for the models they support, so switching serving engines is an infrastructure change, not a model retraining or reconversion project.

Does vLLM have any prefix caching at all? Yes, vLLM has added its own prefix caching support, which narrows the gap for shared prefix workloads, but RadixAttention's radix tree approach remains SGLang's core architectural advantage for highly branching, multi turn traffic.

Which engine has better quantization support? vLLM currently supports a broader range of quantization formats across more hardware targets, which matters if you are running mixed hardware or need day one support for a newly released model.

Is SGLang only useful for chat applications? No. Any workload with repeated prefixes, including RAG pipelines that reuse the same retrieval instructions, benefits from RadixAttention, not just conversational chat.