Which to choose between vLLM and SGLang
Choose vLLM when your accelerator inventory is mixed or still changing and your platform team already runs model servers on Kubernetes. vLLM runs on NVIDIA CUDA, AMD ROCm and Intel XPU GPUs, on x86, ARM and Apple silicon CPUs, and on IBM Z, and its server exposes OpenAI-compatible endpoints such as /v1/chat/completions and /v1/embeddings, so applications written for the OpenAI API can usually point at it with little change. Its Kubernetes options range from native manifests and Helm to KServe and KubeRay. Choose SGLang when prefix reuse is central to your workload. Its documentation emphasizes RadixAttention and prefix caching for low-latency, high-throughput inference, and its README lists TPU v6e and v7, AMD Instinct MI300X to MI355X, Huawei Ascend NPUs and Apple silicon through Metal or MLX, which may suit a fleet that is not NVIDIA-only.
The decision turns on four things. First, your accelerator inventory: list every accelerator model you plan to run in production, then confirm support for that exact model and driver stack in each project's installation and hardware documentation, since a family name on a list does not guarantee a tuned path. Second, workload shape: long shared system prompts, multi-turn chat and retrieval pipelines reuse prefixes heavily, which is where SGLang's caching emphasis matters, while short unrelated requests gain less. Replay your own prompt traces against both engines and measure. Third, the operating model: decide who owns the GPU nodes, the upgrades and the reverse proxy in front of the server, because the vLLM security docs recommend one that allowlists only user-facing endpoints. Fourth, your team's comfort with each project's documentation and release pace.
License will not separate them. Both projects are under Apache 2.0 and neither has a paid edition, so the cost of either comes from accelerator capacity, the surrounding infrastructure, implementation and the people who run it. Release pace is the operational difference to plan for. vLLM ships new minor releases about every two weeks, most recently v0.31.0 on October 5, 2026 (see the vLLM releases page), and SGLang has released roughly every two weeks, with v0.5.21 published on October 2, 2026. Either cadence means a pinned version, a staging environment and a regression run for each upgrade. Before you commit, confirm the release notes and deprecation practice for the endpoints and flags your applications use.
Which engine fits which scenario
This table maps common team situations to the engine with the better fit and the supplied fact behind each call. Rows marked as benchmark-dependent need your own measurements on your model and hardware before you commit, because the documented features alone do not settle them.
| Scenario | Better fit | Reason and what to confirm |
|---|---|---|
| NVIDIA-only fleet on Kubernetes | vLLM (lean toward) | vLLM documents native manifests, Helm, the production-stack, KServe, KubeRay and llm-d among Kubernetes options. Confirm your chosen chart and operator against your cluster version. |
| Mixed AMD and NVIDIA estate | vLLM (lean toward) | vLLM runs on NVIDIA CUDA, AMD ROCm and Intel XPU, with other accelerators via third-party plugins. SGLang also lists AMD Instinct MI300X to MI355X, so confirm support for your exact GPU and model. |
| TPU or Ascend access | SGLang (lean toward) | SGLang's README lists Google TPU v6e and v7 and Huawei Ascend NPUs. The vLLM installation docs list NVIDIA, AMD, Intel XPU and CPU targets, so verify TPU or Ascend support in its current docs before planning. |
| High prefix reuse in agent or chat workloads | Benchmark required | SGLang documents RadixAttention and prefix caching, and vLLM uses its own KV cache management. Measure time to first token and throughput on your real prompts, because the gap depends on prefix overlap. |
| Single GPU node for an internal assistant | Either, benchmark required | Both serve OpenAI-compatible endpoints, so the choice rests on model support and operating simplicity. Test your model on your GPU, since memory headroom and launch flags decide the result more than the engine name. |
| Team wanting the widest documented deployment options | vLLM (lean toward) | vLLM's docs cover several Kubernetes stacks, multi-node parallelism via Ray or its own multiprocessing backend, and metrics at /metrics. Confirm which of these your platform team will actually operate. |
| Team that needs a Hugging Face and OpenAI API drop-in | Either | SGLang docs state compatibility with Hugging Face and OpenAI APIs, and vLLM exposes /v1/chat/completions and /v1/responses. Check the specific request fields your applications send against each server. |
| Team that must control release pace closely | Benchmark and pin required | vLLM ships minor releases about every two weeks, and SGLang's releases also come roughly every two weeks. Pin versions and run regression tests on each upgrade regardless of which you choose. |
How each engine is built and what that means for operations
Both engines serve open-weight models over OpenAI-compatible APIs, but they reach that goal through different caching, parallelism and packaging choices. The cards below cover the parts a platform team touches every day: the API surface, multi-node networking, install path, ecosystem layers and monitoring.
vLLM server and API surface
Running vllm serve starts an OpenAI-compatible server with /v1/completions, /v1/chat/completions, /v1/responses and /v1/embeddings, so existing OpenAI client code can point at it with a base URL change. The operational consequence is that your gateway, allowlists and client tests must cover each endpoint you expose, not just chat. Confirm which endpoints your application actually calls before you choose a proxy rule set.
vLLM multi-node networking
Multi-node vLLM runs on Ray or on vLLM's own multiprocessing backend, with tensor parallelism inside a node and pipeline parallelism across nodes. The security docs state that node-to-node traffic is insecure by default, with no authorization or encryption, so the nodes must sit on an isolated network. Plan that network segment, and confirm the interconnect speed your chosen parallel layout needs before buying hardware.
SGLang caching and parallelism
SGLang's docs center on RadixAttention and prefix caching for reuse across requests, combined with multi-GPU parallelism for throughput. Workloads with long shared system prompts or repeated agent prefixes are where this design matters most. Benchmark your own prompt mix on both engines, because cache hit rates depend on how much your requests share a prefix.
SGLang packaging and install path
SGLang is distributed as the lmsysorg/sglang Docker image and can be installed with uv pip install sglang. Teams that standardize on container images can adopt it with the same pipeline they use for other services. Pin the image tag and record the release you test, since SGLang ships roughly every two weeks and the install path changes with the hardware cookbook you follow.
Ecosystem layers around each engine
vLLM's documented Kubernetes options include native manifests, Helm, the production-stack, KServe, KubeRay, LeaderWorkerSet, llm-d and NVIDIA Dynamo. SGLang's README lists SpecForge for speculative decoding, RL rollout tooling and deployment through llm-d and Ray Serve. Choose the orchestration layer your platform team already runs, then confirm which of these integrations your target cluster and GPU vendor support.
vLLM monitoring surface
vLLM exposes Prometheus-format metrics at /metrics, covering request counts, token throughput, latency histograms and KV cache use, with configurable histogram buckets. Wire these into your existing Prometheus stack before go-live, since KV cache pressure is often the first signal of capacity trouble. Check whether SGLang's metrics export meets the same alerting needs before you assume parity.
Capability comparison from supplied facts
This table compares vLLM and SGLang on license, hardware, parallelism, caching, API surface, metrics, Kubernetes options, release pace and ecosystem. Where a supplied fact is missing for one engine, the cell tells you what to confirm instead of assuming parity. Read the hardware and parallelism rows first if your accelerators or multi-node plans are already fixed.
| Capability | vLLM | SGLang |
|---|---|---|
| License | Apache-2.0, with no paid edition. Cost comes from accelerator capacity, infrastructure and operating staff. | Apache 2.0 per repository license metadata. Hosted by LMSYS, a non-profit open-source organization. |
| Hardware support | NVIDIA CUDA, AMD ROCm and Intel XPU GPUs; x86, ARM, Apple silicon and IBM Z CPUs; other accelerators via third-party plugins. | NVIDIA B200, H100 and A100 class; AMD MI300X to MI355X; TPU v6e and v7; Intel Arc and Xeon; Apple Silicon; Huawei Ascend; Moore Threads. |
| Parallelism | Tensor parallelism within a node, pipeline parallelism across nodes, data parallel for mixture-of-experts. Multi-node runs use Ray or built-in multiprocessing. | Multi-GPU parallelism is cited in its docs. Confirm the supported modes and multi-node runtime in the SGLang documentation before planning. |
| Caching | Supplied facts do not cover its caching features. Confirm prefix caching and KV cache options in the vLLM docs. | RadixAttention and prefix caching, described in its docs as aids to low-latency, high-throughput inference. |
| OpenAI-compatible endpoints | /v1/completions, /v1/chat/completions, /v1/responses, /v1/embeddings, and audio transcription and translation. | Documented as compatible with the OpenAI API. Confirm each endpoint your applications call before migration. |
| Metrics | Prometheus-format metrics at /metrics, covering request counts, token throughput, latency histograms and KV cache use. | Not covered by supplied facts. Confirm the metrics endpoint, format and coverage in the SGLang documentation. |
| Kubernetes deployment | Native manifests, Helm, production-stack, AIBrix, KServe, KubeRay, LeaderWorkerSet, llm-d and NVIDIA Dynamo. | Distributed as the lmsysorg/sglang Docker image. README points to llm-d and Ray Serve for orchestration. Confirm Kubernetes manifests. |
| Release pace | Minor releases about every two weeks, including v0.31.0 on October 5, 2026. | Releases roughly every two weeks, including v0.5.21 on October 2, 2026. |
| Ecosystem projects | Kubernetes and multi-node deployment projects listed in its docs; hardware plugins live outside the main repository. | SpecForge for speculative decoding, RL rollout tooling, and llm-d and Ray Serve integrations listed in its README. |
Licensing and editions: what is and is not paid
vLLM is released under the Apache-2.0 license, according to its GitHub repository, and the project has no paid edition. The license places no fee on running the engine in production, so licensing is usually settled early and the cost question moves to infrastructure, implementation and operations.
- Apache-2.0 license: The main repository is Apache-2.0 licensed. Ask your legal team how that applies to any patches, wrappers or internal forks you maintain.
- No paid edition: The project does not sell a commercial tier with extra features. Any paid support you buy comes from a third party or from your own staff.
- Community governance: vLLM originated at UC Berkeley and is a community project associated with the PyTorch Foundation. Ask any vendor who sells you support how their roadmap depends on that governance.
- Third-party hardware plugins: Support for some accelerators comes through plugins outside the main repository. Each plugin has its own license and maintenance terms, so review them before you depend on one.
Check the license of every container image, model weight and plugin you deploy with vLLM, because the engine's Apache-2.0 license does not extend to them.
SGLang is released under the Apache 2.0 license, according to the license metadata of its sgl-project/sglang GitHub repository. The project is hosted by LMSYS, which the SGLang README describes as a non-profit open-source organization. No commercial offerings, paid editions or support tiers are documented in the material available for this comparison, so confirm any commercial position directly with the project before you budget for one.
- Apache 2.0 license: The repository metadata gives Apache 2.0 as the license. Have your legal team review the terms against your own use, including any modifications you carry.
- Non-profit hosting: The README places the project under LMSYS, a non-profit open-source organization. Ask the project who maintains the release process and how long each release line is supported.
- Ecosystem projects: The README lists related projects for speculative decoding (SpecForge), RL rollouts and deployment orchestration such as llm-d and Ray Serve. Each has its own license, so check them separately.
- Docker image: SGLang is distributed as the lmsysorg/sglang Docker image. Verify the license of any base image or additional layers you build on top of it.
Ask the project in writing whether any paid edition, support agreement or hosted service exists before you assume the position for your deployment.
Neither project charges a license fee for the serving engine, so run cost comes from four places: accelerator capacity, the surrounding infrastructure, implementation work and the people who operate the platform. Accelerator and infrastructure costs usually recur every month, while implementation is typically a one-time project cost that is followed by ongoing operations.
- Accelerators: GPU or other accelerator capacity is the largest line for most deployments. Its cost rises with model size, concurrency and whether you need redundant capacity.
- Infrastructure: Network, storage, load balancers, a reverse proxy and monitoring all add cost. Multi-node serving adds fast interconnect requirements, which the vLLM parallelism docs address.
- Implementation: Architecture, hardening, integration with identity and logging, and documentation are paid work. DeployOpen delivers this work under a written, fixed scope when a client engages it.
- Operators: Staff time for upgrades, patching, capacity planning and incident response recurs for as long as the platform runs. Upgrade pace matters here, because both projects ship new releases about every two weeks.
When you request a quote, ask for each cost line separately, the assumptions behind it, what is one-time, what recurs and what changes if you add a second site or a new model. DeployOpen does not resell vLLM or SGLang and does not hold partner status with either project.
What drives the running cost of each engine
Accelerator capacity is the largest recurring line for most vLLM and SGLang deployments, and it is paid to your cloud provider, data center or hardware vendor rather than to either project. Both engines are Apache-2.0 licensed with no paid edition, so the license adds nothing to the bill. What moves the GPU spend is how many accelerators you need to hit your latency and throughput targets, which depends on the model, the batch shape, prefix reuse and how well the engine's caching and parallelism settings fit your workload. Benchmark both engines on your own model and hardware before committing capacity, because a configuration that wastes memory on one engine can change the answer. Ask any implementer to show the hardware tests they will run and which accelerator classes they cover.
The one-time costs are installation, cluster setup, benchmarking and hardening. The recurring costs are GPUs, upgrades, monitoring and on-call. Upgrades are where the two engines differ in operating effort. vLLM ships new minor releases about every two weeks, with v0.31.0 published on October 5, 2026, and SGLang has released roughly every two weeks, with v0.5.21 on October 2, 2026. Each upgrade needs a regression run against your model and API clients, and the time for that work recurs for as long as you run the engine. Multi-node runs add cost on the networking side: the vLLM security docs state that node-to-node traffic is unauthenticated and unencrypted by default, so isolating that network, and reviewing it, is part of the build and of every later change to the cluster.
When you request a quote, ask for the following in writing: the hardware and accelerator classes covered by the benchmark, the number of model and configuration combinations tested, how upgrades to new releases are scoped and whether a regression pass is included, a security review of any multi-node networking and the Ray or multiprocessing traffic it carries, and the monitoring the engine's /metrics endpoint will feed. Also ask for handover documentation that names every configuration key, runbook and owner, so your staff can run the upgrade cycle without the implementer on call. Fixed scope with written deliverables makes these costs comparable across engines.
Moving from one engine to the other
This sequence moves a working model-serving setup from one engine to the other without changing client applications, and it ends with evidence from your own workloads that the new engine can take the traffic.
Inventory models, settings and client applications
The platform or ML infrastructure team lists every served model, its weights source, quantization format, tensor and pipeline parallelism settings, context length and any engine-specific flags, along with every application and internal service that calls the endpoint. Record which client calls which path, such as /v1/chat/completions or /v1/embeddings, and which features it relies on, including streaming, tool calls and structured output. The step is done when the team has a single inventory that a second engineer can check against the running environment.
Confirm OpenAI-compatible endpoints per client
Application owners and the platform team check each recorded endpoint against the current documentation for both engines, since both expose OpenAI-style APIs but may differ in which paths, request fields and response details they support for a given model. Test each call pattern against a scratch instance of the candidate engine and note every difference in request shape, error format or unsupported parameter. The step is done when every client has a written list of endpoints confirmed on the new engine and any gaps that need an application change or a documented workaround.
Rebuild launch configuration per model and hardware
The inference engineers rewrite launch scripts for each model on each accelerator type in the fleet, starting from the SGLang Cookbook for SGLang launch commands and from the vLLM documentation for vLLM serve options. Check hardware support for every accelerator in use, since NVIDIA, AMD, TPU and other targets are not all supported in the same way on both engines. Confirm that memory, parallelism and quantization settings produce a stable load on each node. The step is done when each model starts cleanly on its target hardware and the team has versioned launch configuration files in source control.
Rebuild Kubernetes manifests and proxy allowlist
The platform team replaces engine-specific Kubernetes manifests or Helm values, updates health probes and resource requests, and rebuilds the reverse proxy allowlist so that only the endpoints meant for users are reachable. Engine-specific API key handling must be reviewed, because the vLLM security guidance says --api-key does not cover every route and recommends an allowlisting proxy, and SGLang's authentication options should be confirmed in its documentation rather than assumed. Node-to-node traffic for multi-node runs needs its own isolated network in either engine. The step is done when a security reviewer has tested the allowlist from outside the cluster and confirmed that administrative and metrics routes are not exposed to users.
Move metrics, alerts and dashboards
The observability owner maps the metrics that the current dashboards and alerts use, such as request counts, token throughput, latency and KV cache use, to the equivalent metrics exposed by the new engine. Vendor metric names differ, so each alert rule is rewritten and tested against a load test rather than copied. Confirm the scrape configuration reaches the metrics endpoint and that the metrics route is excluded from user-facing access. The step is done when every dashboard panel and alert shows live data from the new engine and the team has documented the metric name changes for on-call staff.
Run a parallel period on your own workloads
The team runs both engines side by side, with a share of production or replayed traffic sent to the new engine, and records latency distributions, throughput, error rates, output quality checks and cost per token on the team's own prompts and models. Benchmarks published by either project use different hardware and request mixes, so they should inform the plan but not replace this comparison. The step is done when the team has a written results report covering each model and client pattern, signed off by the application owners and the security reviewer, with any regressions either fixed or accepted explicitly.
Cut over and keep the rollback path
The team shifts client traffic to the new engine in stages, keeps the previous engine deployable with its launch configuration and manifests, and watches the rebuilt dashboards during each stage. Agree the rollback trigger in advance, such as a defined error rate or latency threshold, and rehearse the rollback. Once the new engine has carried full traffic through an agreed observation window, the old deployment is retired and its configuration archived. The step is done when all clients point at the new engine, the rollback has been tested, and the change record names the owner of ongoing upgrades, since both projects ship releases frequently and each upgrade needs the same re-test.
Where implementation projects usually run long
Implementation projects for either engine usually run long in the GPU stack, not in the serving config. The first risk is hardware that is not on the supported list. vLLM documents NVIDIA CUDA, AMD ROCm and Intel XPU GPUs, plus CPUs and other accelerators reached through third-party hardware plugins outside the main repository. SGLang's README lists its own set, including AMD Instinct MI300X to MI355X, Google TPU v6e and v7, Huawei Ascend NPUs and Moore Threads GPUs. Before you commit, check your exact accelerator, driver and runtime combination against the current installation docs and the SGLang README, and confirm with each vendor which plugin or image version they validate. Budget time for driver and runtime alignment on CUDA, ROCm or other stacks, because a mismatch here can consume more days than any launch flag.
The second overrun is unvalidated model and hardware pairs. Each model needs its own launch tuning, covering parallelism settings, memory limits, quantization choices and caching options. SGLang publishes a Cookbook with launch commands per model and hardware, which helps, but a Cookbook entry is a starting point, not proof that your model runs well on your GPUs at your concurrency. Ask for a written benchmark on your own model and hardware before go-live. Plan for security and operations work that is easy to skip: put the server behind an authenticated reverse proxy that allowlists only user-facing endpoints, because vLLM's --api-key option does not cover /metrics, /health or /pause. Wire Prometheus-format metrics from vLLM's /metrics endpoint into alerting before users arrive, and for multi-node runs, keep node-to-node traffic on an isolated network, since vLLM's security docs say it is unauthenticated and unencrypted by default. Depth on multi-node serving belongs in the single-node vs multi-node topic.
The third overrun is the release train. vLLM ships new minor releases about every two weeks, and SGLang has followed a similar cadence, with v0.5.21 published on October 2, 2026. Frequent releases are useful for new model support but make upgrades a recurring project rather than a one-time install. Agree on a pinned version, a staging test that replays your real prompts and a rollback path before the first production deployment, and decide who owns the upgrade calendar. Ask each vendor or your integrator how they handle breaking changes to OpenAI-compatible endpoints, and budget for re-running benchmarks after every change that touches caching or parallelism.
Questions buyers ask before choosing or hiring help
Which engine costs less to run, vLLM or SGLang?
Neither engine has a license fee, so the difference comes from the drivers around them: accelerator capacity, how well each engine uses that capacity for your model and traffic shape, and the engineering hours to launch, tune and monitor it. Both are Apache-2.0 licensed, and vLLM has no paid edition. Throughput per GPU depends on the model, quantization, prompt lengths and caching pattern, so the honest comparison is a benchmark on your hardware with your traffic. Ask for a written benchmark plan with a fixed model, hardware list and load profile before accepting any cost comparison, including one from DeployOpen.
How long does a first vLLM or SGLang deployment take?
No duration can be stated from the researched facts, so treat any figure you hear as a question to answer in writing. The work runs in phases: hardware and driver readiness, choosing the serving topology (single GPU, multi-GPU in one node, or multi-node), container or Kubernetes packaging, the API gateway and authentication layer, observability, and a load test against your targets. Each phase depends on whether your GPUs are already provisioned and whether the model needs special launch flags. Confirm these inputs with your vendor or in your own environment before scheduling. DeployOpen agrees timelines in the written scope before work starts.
What skills does the team need to run either engine?
Plan for Linux administration, GPU driver and runtime management for your accelerator vendor, container operations, and Kubernetes skills if you schedule serving there. You also need someone who can read serving metrics and write a benchmark plan, since model choice and launch flags change results. Both engines expose an OpenAI-compatible API, so application developers need little new knowledge, but the operator carries the GPU and networking load. Check whether your team has these skills in production, not only in a lab. Where gaps exist, decide early whether to train, hire or bring in implementation help.
Can our team deploy and run vLLM or SGLang without outside help?
Yes, a team can do this alone when it already has GPU administration, container and networking skills and enough time to test releases and tune launches. The trade-offs are time to first stable service, the risk of misconfigured multi-node traffic, and the ongoing load of tracking releases. vLLM's security documentation says node-to-node traffic in multi-node deployments is unauthenticated and unencrypted by default, so an isolated network and a reverse proxy are required from day one. An outside partner can take fixed-scope implementation, hardening and documentation, while your team keeps operational control. Confirm which of these responsibilities you intend to keep.
Is switching from vLLM to SGLang later expensive?
The cost depends mainly on how much of your launch automation and client code is coupled to one engine. Both expose OpenAI-compatible endpoints, so application code that calls the standard chat and completions routes usually needs little change. The bigger work is in launch flags, container images, health checks, metrics dashboards and any engine-specific features your applications rely on. Keep the engine behind your gateway, parameterize launch commands and store benchmark results so a switch can be measured rather than assumed. Ask any implementer to show how the engine is isolated in the deployment code.
How do we verify hardware support before buying accelerators?
Verify against the engine's current installation and hardware documentation for your exact accelerator model, driver and runtime version, not against general claims. vLLM's installation docs list NVIDIA CUDA, AMD ROCm and Intel XPU GPUs, CPUs on several architectures, and other accelerators through third-party plugins outside the main repository. SGLang's README lists NVIDIA, AMD Instinct, Google TPU, Intel, Apple Silicon, Huawei Ascend and Moore Threads hardware. Support for a given chip can differ between engines and between versions, so ask for a proof-of-concept run on the target hardware with your chosen model before purchase. Plugin-based support carries its own maintenance responsibility.
How does the release pace affect upgrades?
Both projects release often: vLLM shipped minor releases about every two weeks in the period listed in the researched facts, and SGLang has also released roughly every two weeks. Frequent releases bring fixes and new model support, but each upgrade can change flags, defaults, kernels and metric names. Pin versions in your deployment, test each candidate against a regression set and a load test, and read the release notes before promotion. Define who approves an upgrade and how you roll back. Ongoing upgrade work can be scoped as a retainer, and the cadence should be written into the agreement.
How DeployOpen helps choose, implement or migrate between engines
DeployOpen is a Chainscore Labs company that plans, implements, hardens, documents and supports self-hosted open-source platforms inside the client's own infrastructure. For vLLM vs SGLang, we start from your models, hardware and traffic pattern, then work through the engine choice, deployment and the operating model your team will run.
How we scope
- We confirm which models, accelerators and API clients your team must support before naming an engine.
- Engagements are fixed-scope, with deliverables and timelines agreed in writing before work starts.
- The written scope states which hardware targets, parallelism layouts and endpoints are included and which are excluded.
How we build
- We deploy the chosen engine in your cloud account, data center or air-gapped network, with your own GPU capacity.
- We put an authenticated reverse proxy in front of the engine, allowlisting only the endpoints meant for users.
- We test OpenAI-compatible client calls, throughput and metrics collection against the agreed acceptance criteria.
How we hand over
- We document launch configuration, hardware assumptions, network isolation and the reasons behind each choice.
- We train the operators on upgrade steps, since both projects ship releases on a short cadence and need planned testing.
- We deliver runbooks for switching or migrating engines, so the decision can be revisited without rebuilding the service.
How we support
- Ongoing support, upgrades and continuous testing are available as a retainer after handover.
- A retainer can cover re-testing when you add a model, change accelerators or move to a new engine release.
- Ask us to confirm which support windows, response terms and upgrade cadence apply before you sign.
