vLLM vs SGLang

vLLM vs SGLang: Which Should You Pick?

vLLM wins the default deployment decision. SGLang wins the first evaluation slot for workloads dominated by shared context. These are recommendations based on the documented deployment paths and cache controls, not a claim that one engine is universally faster.

For an engineer serving many short chats, a shared system prompt is a reason to evaluate SGLang, not a reason to discard vLLM. Both reuse prompt computation. For a founder shipping a straightforward API with limited infrastructure time, start with vLLM and require a demonstrated service or cost improvement before changing it.

RAG, or retrieval-augmented generation, adds retrieved documents to a model's prompt. It is the repeated beginning of that prompt, not the RAG label, that makes prefix caching useful.

Verification date: 14 October 2026. The latest stable releases resolved this run were vLLM 0.31.0, released 5 October, and SGLang 0.5.21, released 2 October. Feature descriptions below follow the live project docs; validate the combination you need against your pinned release. No inference benchmark was run for this comparison.

The licences are verified against vLLM's licence and SGLang's repository. Model-weight licences, infrastructure and engineering are separate costs. Neither project's open-source engine has a hosted per-token tariff to compare.

What Each Engine Does, and Where It Hits a Wall

vLLM: The Default General Serving Layer

vLLM is an inference and serving engine that loads model weights and schedules requests against your hardware. Its documented building blocks include continuous batching, which admits new requests as others finish, and PagedAttention, which manages the model's key-value cache in blocks rather than reserving one large contiguous allocation per request. The KV cache stores intermediate attention state so the model can continue generating without recomputing its whole history. vLLM documentation

The reason to start here is a clear deployment unit: a model, an official server image, an API and explicit platform installation instructions. For a product team building a customer-support endpoint, this is a manageable baseline from which to measure capacity.

Its wall is the validated configuration. A supported GPU is only the beginning; model architecture, quantization, attention backend and requested features must work together. A concrete cache exception matters for evaluation services: vLLM says a request asking for prompt log probabilities bypasses the prefix cache and recomputes the prompt. A scoring endpoint can therefore behave differently from a chat endpoint using the same document. vLLM V1 guide

The engine costs $0. Choose it for general serving and a mixed fleet; skip a migration to it when your working SGLang installation already meets the service target and no required capability is missing.

SGLang: A Strong Starting Point for Repeated Agent Context

SGLang is an open-source inference framework for language and multimodal models, with agentic workloads among its stated priorities. RadixAttention organizes reusable prompt state around shared prefixes. Its HiCache extension adds host memory and storage behind the GPU cache. The practical attraction is a documented set of tools for retaining and reusing context across repeated requests. SGLang repository, HiCache design

Imagine an agent that repeatedly reads the same support policy and branches into different customer questions. That is a more compelling SGLang evaluation than unrelated prompts that happen to arrive at high concurrency.

Its wall is cache locality and backend parity. HiCache's GPU and host-memory tiers belong to an inference instance. Storage becomes shared only when the selected backend is configured for that purpose. Enabling a larger host cache does not automatically create a fleet-wide cache. Extra tiers also mean memory budgets, transfers and configuration to operate. HiCache's sharing scope

The engine costs $0. Choose it when repeated context is material enough to tune; skip a switch based solely on the word “agents” if your prompts rarely share an exact prefix.

Prefix Caching and RadixAttention: SGLang Gets the First Trial

SGLang wins the evaluation priority for branching, repeated-context workloads; vLLM already has the essential caching capability. This is not PagedAttention versus the ability to cache. PagedAttention concerns memory organization, while prefix caching concerns reuse. vLLM's Automatic Prefix Caching explicitly covers repeated long-document questions and multi-round conversations. vLLM prefix caching

Prefill is the work of reading the prompt before generation starts. Decode is the work of generating new tokens. A cache hit can avoid repeated prefill; it does not remove the decode work of writing a fresh answer. A long answer with little shared input can therefore see little benefit from either engine's prefix reuse.

For a short-chat product, put stable policy and tool descriptions before changing request content wherever the model's expected prompt format allows it. Preserve stable serialization and document ordering. A different timestamp near the beginning can destroy reuse of otherwise identical material after it. Think of a shared path: once the route diverges, identical scenery farther along does not make the earlier route identical.

Long-context RAG needs a second check: is the context reusable, or merely long? Repeated questions over an unchanged manual are a strong caching case. A search product assembling different passages in a different order for every request may retain only its short system prefix. Measure those separately before investing in hierarchical storage.

SGLang's documented RadixAttention default is on, controlled by --disable-radix-cache; HiCache is an additional configuration choice. In either engine, inspect the effective configuration rather than infer cache behavior from the project name. SGLang server arguments

Agent Features: JSON Is a Tie; Adapter and Draft Choices Need Validation

Neither engine wins simply because you need JSON. Both support schema-constrained generation. Use the model to choose values, then constrain the structure and validate the result before executing an action.

Structured JSON Output

vLLM supports JSON Schema through response_format and its structured_outputs options. SGLang supports JSON Schema too, with XGrammar as its default grammar backend. These are decoding constraints, not just instructions asking the model to “return JSON.” vLLM structured outputs, SGLang structured outputs

For a support agent, a schema can require an action and a ticket identifier. It cannot establish that the ticket belongs to the requesting user or that the chosen action is justified. Keep those checks in application code. Test your actual schemas, reasoning mode and streaming behavior together; an engine supporting each item separately is not proof that your combination is correct.

Winner: tie for the ordinary JSON requirement. Stay with the engine you already operate unless representative schema requests expose a concrete difference.

Speculative Decoding

Speculative decoding is a tuning option in both projects, not a reason to assume a speedup. A draft mechanism proposes tokens and the target model verifies them. Useful accepted proposals can reduce sequential generation work; drafting, verification and memory still have a cost.

vLLM documents EAGLE, multi-token prediction, separate draft models, n-gram and suffix methods. SGLang documents EAGLE, multi-token prediction, draft-model and NGRAM paths too. The matching draft, target model, hardware and traffic level matter more than a checkbox. vLLM methods, SGLang methods

Winner: the engine with a validated draft path for your model. Enable speculation after establishing a baseline, then compare response latency and total capacity. Keep it off if the extra work does not pay back under your production load.

Multi-LoRA for Tenant-Specific Behavior

Both can serve multiple LoRA adapters, small fine-tuning weight additions that customize a shared base model. vLLM exposes adapter registration through --lora-modules, with concurrency and rank controls. SGLang exposes --lora-paths and --max-loras-per-batch; its documented batch default is 8 adapters. That is not a total customer limit. vLLM LoRA, SGLang LoRA

For a tenant-specific writing product, test the adapter distribution you expect, including cold adapters and frequent changes. SGLang's overlap-loading option can hide transfers, but its docs also warn that it can reduce multi-adapter prefill batching and increase first-token latency. More switches do not automatically mean more capacity.

Winner: keep the engine whose adapter path you have validated. Multi-LoRA alone does not establish a reason to switch. SGLang earns a trial when adapter-loading behavior or a cache-heavy workload is your bottleneck. For TPU, use the backend's own feature matrix: the SGLang-JAX guide still marks multi-LoRA as in development. SGLang TPU matrix

Hardware Support: vLLM Wins the Broad-Fleet Starting Point

Both projects state NVIDIA, AMD, TPU and CPU support. The support boundary is the backend and model combination. Do not turn a project-wide hardware list into a promise that every GPU recipe, adapter or decoding method transfers unchanged.

• NVIDIA: vLLM documents CUDA GPU installation; SGLang's repository lists datacenter and RTX families. The current release images and host driver must agree. vLLM GPU installation, SGLang hardware list
• AMD: both document ROCm deployment. SGLang lists Instinct MI300X, MI325X, MI350X and MI355X, with a separate installation guide. Use the matching ROCm image and model recipe. SGLang AMD guide
• Google TPU: vLLM links to its separate tpu-inference installation. SGLang's TPU guide uses the separate SGLang-JAX backend and lists v6e and v7. These are distinct deployment paths requiring their own feature checks. vLLM TPU installation, SGLang TPU guide
• CPU: vLLM documents Intel/AMD x86 and ARM AArch64 among its CPU paths. SGLang's CPU server guide specifically targets Intel AMX on fourth-generation or newer Xeon Scalable processors. “CPU supported” does not mean identical coverage. vLLM CPU guide, SGLang CPU guide

A concrete operations difference may decide your next deployment: vLLM 0.31.0 publishes a CUDA 12.9 image alongside its CUDA 13.0 default. SGLang's current install guide requires CUDA 13 and says its CUDA 12 image line ended at 0.5.19. If your fleet cannot move to a compatible driver environment yet, investigate that before comparing any throughput result. vLLM release artifacts, SGLang installation

OpenAI-Compatible Servers: The Client Is Easier to Move Than the Workload

Both engines can sit behind a client that uses OpenAI-style chat completions. Both document /v1/chat/completions and /v1/completions; this reduces the amount of application plumbing you must replace. It does not mean every endpoint, extension, tool parser or model behavior is interchangeable. vLLM server, SGLang API

A chat template turns role-labelled messages into the token sequence the model expects. Preserve it when comparing engines. Otherwise you may be comparing two prompts without realizing it. Keep the model revision, tokenizer, sampling settings and output limit aligned too.

For an existing vLLM client, inspect old constrained-output fields before migration or upgrade. The docs say guided_json and related fields were removed in 0.12.0; current code should use the supported structured_outputs or response_format shape. A stale request body can masquerade as an engine incompatibility. vLLM migration note

Winner: tie for the basic client interface; vLLM for the default operations recommendation. Your acceptance test should cover complete agent turns, including streamed tool arguments, errors and retries, rather than stopping after one successful text response.

Run Each on a GPU Cloud

Start with a pinned container and a small smoke-test model, then substitute your production model and memory budget. The following examples adapt the projects' documented Docker and launch commands for a Linux NVIDIA GPU host with Docker, the NVIDIA container runtime and a driver compatible with the image. They were checked against documentation, not executed on a rented GPU. vLLM Docker, SGLang quickstart

The Qwen/Qwen3-0.6B model below is a wiring check, not a recommendation for your agent's answer quality. Run one engine at a time on the same port. The context cap is an explicit example setting, not the model's advertised maximum.

vLLM Container

docker run --rm --gpus all --ipc=host \
  -p 127.0.0.1:8000:8000 \
  -v "$HOME/.cache/huggingface:/root/.cache/huggingface" \
  vllm/vllm-openai:v0.31.0 \
  --model Qwen/Qwen3-0.6B \
  --host 0.0.0.0 --port 8000 \
  --max-model-len 8192 \
  --enable-prefix-caching

SGLang Container

docker run --rm --gpus all --ipc=host \
  -p 127.0.0.1:8000:8000 \
  -v "$HOME/.cache/huggingface:/root/.cache/huggingface" \
  lmsysorg/sglang:v0.5.21 \
  python3 -m sglang.launch_server \
  --model-path Qwen/Qwen3-0.6B \
  --host 0.0.0.0 --port 8000 \
  --context-length 8192

Run this request on the GPU host after initialization:

curl http://127.0.0.1:8000/v1/chat/completions \
  -H 'Content-Type: application/json' \
  -d '{
    "model": "Qwen/Qwen3-0.6B",
    "messages": [{"role": "user", "content": "Say hello."}],
    "max_tokens": 64
  }'

The examples bind the host port to loopback. Put your authenticated application gateway in front of the server before serving remote users. This matters particularly for vLLM: its docs state that --api-key does not authenticate every route on the HTTP server. vLLM authentication scope

Runpod's Documented vLLM Worker

Runpod provides a documented ready-to-deploy vLLM worker through its Hub. This is the cloud shortcut supporting the default-operations recommendation. Its worker packaging is a separate deployment artifact; verify its bundled engine version instead of assuming it matches the latest upstream release. Runpod's deployment guide

For SGLang, the reproducible cloud path described here is its official container on a compatible GPU host. The Runpod shortcut above is specifically the vLLM worker documented by Runpod.

Price Difference: The Same GPU Has the Same Bill

Neither engine is cheaper at equal rented capacity. On 14 October 2026, Runpod's live pricing page listed an A100 PCIe with 80 GB at $1.79/hour for Pods. That is a dedicated GPU price, not the Serverless worker's billing rate. It is the common infrastructure input for the calculation below. Runpod pricing

Using a 730-hour budgeting month, one continuously provisioned GPU costs $1,306.70/month with either vLLM or SGLang. Two cost $2,613.40/month. These figures exclude storage, network charges, redundancy and engineering.

Normalize a hypothetical support workload: 50,000 requests per month, each with 2,000 input tokens and 200 output tokens. That is 100 million input tokens and 10 million delivered output tokens. If it fits on one GPU at your required response time, either engine's GPU bill is $0.13067 per 1,000 delivered output tokens. The denominator is output tokens; the bill also pays for reading the input.

This is cost arithmetic, not measured capacity. If the workload does not fit, the one-GPU result does not apply.

The crossover is the point where one configuration needs an additional replica to meet the same service target. If SGLang needs one GPU and vLLM needs two, SGLang saves $1,306.70/month. If the measured capacity relationship reverses, vLLM saves the same amount. If both need one, the infrastructure saving is zero.

For a migration budget of 8 hours at an assumed $100/hour, the cost is $800. Retiring one such GPU repays that in about 0.61 months, before any difference in ongoing operations. Without a removed replica or a valued service improvement, “faster” may produce no financial return at all.

Price winner: the configuration that meets the target with lower total operating cost. There is no honest engine-specific traffic crossover to print before measuring the safe capacity of your workload.

Throughput: Choose by Completed Work at Your Latency Target

No universal throughput winner is established here. A project-published result belongs to its model, hardware, precision, request lengths and release. A kernel improvement is not an end-to-end serving improvement, and prefill throughput is not output-token throughput. Neither should become a blanket “SGLang is faster” or “vLLM is faster” claim.

Use the projects' own tools as starting points: vLLM's benchmark CLI and SGLang's serving benchmark guide. The comparison needs both warm-cache and cold-cache runs, plus the workload shape your product creates.

Measure four conditions separately: short chats sharing a stable system prompt; repeated questions over the same long document; long RAG prompts with changing retrieved passages; and your actual mix of schemas, tool calls and adapters. Preserve arrival timing as well as concurrency. A burst of simultaneous requests and a steady stream can expose different bottlenecks.

Record completed requests, delivered output tokens, errors and first-token latency. Also inspect p95 and p99 latency, the 95th and 99th percentiles, because a good average can hide a painful tail. Count outputs that fail the application's schema or correctness checks as failures, not useful capacity.

The winner is the lowest-cost deployment that stays inside that acceptance envelope. A speed claim from a different model or an older release is a reason to run this comparison, not its answer.

Switching Engines: Preserve the Contract and Keep a Route Back

Move the serving layer only when the measured benefit exceeds migration and operating cost. Your application data and retrieval store can usually remain where they are when only the generation backend changes. The work sits in model packaging, request extensions, output parsing and deployment behavior.

Do not switch a healthy vLLM deployment merely to obtain prefix caching or JSON constraints; it already has both. Do not switch a healthy SGLang deployment merely because vLLM is the default recommendation here. Existing operational knowledge is part of the cost comparison.

If the unresolved decision is whether you need a shared production server at all, read Ollama vs vLLM. That comparison addresses the earlier choice between a convenient local model workflow and a serving layer for overlapping requests.

Your next move: capture representative peak traffic, set the maximum acceptable response times and error rate, and compare the pinned engines on the same hardware before expanding the fleet.

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