SGLang

Name: SGLang
Brand: SGLang

SGLang is an open-source LLM/VLM inference serving framework and Python-embedded DSL that provides low-latency, high-throughput inference, controls for multi-step generation workflows (structured outputs, tool-calling, multi-turn chat with prefix reuse), and a multi-component stack including a runtime, model gateway, and diffusion optimizations.

4 Ref Customers

Website

MarketsMachine Learning Ops (MLOps)

01Competitive Landscape

Narrative analysis

Feature differentiation: - SGLang: focuses on RadixAttention for KV cache reuse and includes components for structured generation and routing. - vLLM: focuses on a PagedAttention approach and positions itself as a broadly applicable serving solution with an expanding ecosystem. Performance comparisons (evidence summary): - Project-authored benchmarks indicate scenarios where SGLang outperforms vLLM; community reports are mixed and show that trade-offs depend on setup, model, and hardware. Pricing comparison: - Both SGLang and vLLM are distributed as open-source software. Real costs are typically driven by GPU infrastructure and engineering effort; managed offerings or commercial support can shift costs toward subscription or support fees, but verified managed-pricing information for SGLang was not identified in the sources reviewed. Adoption trends: - Press coverage in early 2026 discusses increased attention to SGLang and related commercialization. Project-owned landing pages include adoption claims that should be validated independently.

	SGLang	vLLM	TensorRT-LLM
Starting Price	Open-source (Apache-2.0 licensed, no license fee)	Open-source (no license fee)	Commercial NVIDIA stack (vendor-controlled pricing)
Best For	Prefix-heavy chat/agent workloads; structured outputs; teams willing to tune kernels and schedulers	General-purpose high-throughput serving; community ecosystem	NVIDIA-first deployments seeking vendor-optimized kernels and NVIDIA tooling integration
Key Strength	Design focus on RadixAttention and KV cache reuse; features for structured outputs and routing/gateway components	PagedAttention approach and broad community adoption	Deep NVIDIA optimizations and integration with NVIDIA ecosystem
Key Weakness	Reports of documentation and operational maturity issues; startup/compile overhead in some configurations; active change cadence	Some users report installation or documentation issues in certain environments	Potential vendor lock-in to NVIDIA tooling; less portability across non-NVIDIA hardware

01Competitive Landscape

Narrative analysis

Feature differentiation:

SGLang: focuses on RadixAttention for KV cache reuse and includes components for structured generation and routing.
vLLM: focuses on a PagedAttention approach and positions itself as a broadly applicable serving solution with an expanding ecosystem.

Performance comparisons (evidence summary):

Project-authored benchmarks indicate scenarios where SGLang outperforms vLLM; community reports are mixed and show that trade-offs depend on setup, model, and hardware.

Pricing comparison:

Both SGLang and vLLM are distributed as open-source software. Real costs are typically driven by GPU infrastructure and engineering effort; managed offerings or commercial support can shift costs toward subscription or support fees, but verified managed-pricing information for SGLang was not identified in the sources reviewed.

Adoption trends:

Press coverage in early 2026 discusses increased attention to SGLang and related commercialization. Project-owned landing pages include adoption claims that should be validated independently.

	SGLang	vLLM	TensorRT-LLM
Starting Price	Open-source (Apache-2.0 licensed, no license fee)	Open-source (no license fee)	Commercial NVIDIA stack (vendor-controlled pricing)
Best For	Prefix-heavy chat/agent workloads; structured outputs; teams willing to tune kernels and schedulers	General-purpose high-throughput serving; community ecosystem	NVIDIA-first deployments seeking vendor-optimized kernels and NVIDIA tooling integration
Key Strength	Design focus on RadixAttention and KV cache reuse; features for structured outputs and routing/gateway components	PagedAttention approach and broad community adoption	Deep NVIDIA optimizations and integration with NVIDIA ecosystem
Key Weakness	Reports of documentation and operational maturity issues; startup/compile overhead in some configurations; active change cadence	Some users report installation or documentation issues in certain environments	Potential vendor lock-in to NVIDIA tooling; less portability across non-NVIDIA hardware