Verdict
For genuinely agentic traffic - long shared system prompts, tool schemas repeated on every turn, multi-turn loops - SGLang is the specialist: its RadixAttention radix-tree cache is built precisely for repeated-prefix reuse, and 2026 benchmark roundups consistently show its largest margins on exactly that traffic shape (≈29% higher throughput on ShareGPT-style multi-turn runs, with the original RadixAttention paper's up-to-6.4x figure applying only to extreme prefix-sharing workloads like RAG and batched agent loops). vLLM is the generalist: broader model/hardware coverage, a larger ecosystem, and 2026 release velocity that keeps shipping serving-stack features - v0.24.0 landed June 29, 2026, on top of a v0.21 cycle that added KV-cache offloading (Hybrid Memory Allocator, Mooncake) and Model Runner V2. On unique-prompt, high-concurrency traffic the engines are within 2-4% of each other - inside run-to-run variance - so nobody should migrate for that. And if your deployment is a fixed NVIDIA-only model where every millisecond of latency matters, TensorRT-LLM still beats both on raw p95 TTFT in third-party benchmarks. Decide on your measured prefix-cache hit rate, not on anyone's headline tokens/sec.
The architectural difference is real and maps directly onto agent traffic. vLLM's automatic prefix caching hashes fixed-size KV blocks, so reuse is aligned to block boundaries; SGLang's RadixAttention maintains a radix tree over token sequences, matching shared prefixes at finer granularity and evicting LRU-style - the design introduced in the SGLang paper (arXiv 2312.07104) that reported up to 6.4x throughput on prefix-heavy workloads. In 2026 third-party benchmarks the pattern is consistent in direction and much more modest in magnitude on mixed traffic: a widely-reproduced H100/Llama-3.1-8B measurement (originating with PremAI, reported by aimultiple.com and techsy.io) put SGLang at ~16,215 tok/s vs vLLM's ~12,553 tok/s (~29%); a 2026 three-way engine benchmark (news.creeta.com) measured p50 TTFT at 10 concurrent requests of 112ms for SGLang vs 120ms for vLLM (TensorRT-LLM at 105ms); and on unique-prompt workloads with no shared prefixes the same comparison literature calls the engines effectively identical, with SGLang's edge shrinking to ~2-5% - inside run-to-run variance (spheron.network's 2026 vLLM-vs-SGLang comparison; markaicode's Llama-3.3-70B FP8 H100 run measured 2.5-4.6%). Meanwhile the freshness facts favor taking vLLM's velocity seriously: the project's GitHub releases listed v0.24.0 on June 29, 2026, with the team previewing DeepSeek's DSpark speculative decoding in nightly ahead of v0.25, following a v0.21 cycle (367 commits) that shipped KV offloading via Hybrid Memory Allocator and Mooncake, RayExecutorV2 by default, and Model Runner V2 with Qwen3.5 and Mamba support (vLLM release notes and project updates). Every number above is workload-dependent; the honest recommendation is a one-day bench on your own agent traces measuring cache hit rate and TTFT, because the spread between 'parity' and '6.4x' is entirely a function of how much prefix your traffic shares.
Decision Table
| Criterion | Edge | Explanation |
|---|---|---|
| Repeated-prefix agent traffic (system prompts, tool schemas, multi-turn loops) | SGLang (RadixAttention engine with radix-tree prefix reuse) Best edge | This is SGLang's home game. RadixAttention's radix tree matches shared prefixes at token-sequence granularity across requests, so a fleet of agent workers sharing a 5K-token system prompt + tool schema head hits cache on nearly the whole head, cutting prefill work and TTFT on every turn. The RadixAttention paper (arXiv 2312.07104) reported up to 6.4x throughput on prefix-heavy workloads, and 2026 roundups still credit SGLang's biggest margins to exactly this traffic: ~29% on shared-prefix multi-turn benchmarks (PremAI's H100/Llama-3.1-8B measurement, ~16,215 vs ~12,553 tok/s, as reported by aimultiple.com and techsy.io). vLLM's block-hash automatic prefix caching also reuses shared heads, but block-boundary alignment means a prefix ending mid-block reuses less than radix matching does - a difference that compounds when agents append short turns to long shared heads. |
| Unique-prompt, high-concurrency throughput | Tie | When prompts don't share prefixes, the architectural edge disappears: spheron.network's 2026 comparison describes unique-prompt workloads as effectively identical, with SGLang's ~2-4% edge falling within run-to-run variance, and markaicode's Llama-3.3-70B FP8 H100 benchmark measured SGLang just 2.5-4.6% ahead. That is inside (or barely outside) run-to-run variance for serving benchmarks. If your workload is batch document processing or single-shot completions, engine choice should be driven by ops fit and model support, not these numbers. |
| Ecosystem breadth, model coverage, and feature velocity | vLLM (PagedAttention engine with block-hash automatic prefix caching) Best edge | vLLM is the default integration target for the open-model ecosystem - new model architectures typically land there first or simultaneously with release - and its 2026 cadence is heavy: v0.24.0 tagged June 29, 2026; the v0.21 cycle alone shipped 367 commits including KV-cache offloading (Hybrid Memory Allocator, Mooncake integration), RayExecutorV2 by default, Model Runner V2 with Qwen3.5/Mamba support, and an optimized Blackwell MLA backend; and DSpark speculative decoding was already running in nightly ahead of v0.25 per project updates. It also has the broader hardware story (NVIDIA, AMD, TPU, AWS Neuron, Ascend via plug-in backends). SGLang's development is vigorous but concentrated; when a brand-new architecture drops, vLLM support is the safer assumption. |
| TTFT for interactive agent loops | SGLang (RadixAttention engine with radix-tree prefix reuse) Best edge | Measured gaps are modest but consistently favor SGLang at comparable settings: news.creeta.com's 2026 three-way H100 benchmark measured p50 TTFT at 10 concurrent requests of 112ms for SGLang vs 120ms for vLLM, and spheron.network's three-way benchmark places SGLang's p95 TTFT below vLLM's at every concurrency level (e.g. 1,450ms p95 for vLLM at 100 concurrent, with SGLang in between vLLM and TensorRT-LLM). The mechanism is the same prefix story - cached heads mean less prefill before the first token - so the advantage grows with shared-prefix ratio and shrinks toward zero on cold prompts. For an interactive agent that streams its reasoning, tens of milliseconds per turn compounds across a loop, but it is not by itself a migration-grade gap. |
| Absolute lowest latency on a fixed NVIDIA model | Tie | A third party wins this row: TensorRT-LLM. In multi-engine 2026 benchmarks (spheron.network's vLLM vs TensorRT-LLM vs SGLang three-way; news.creeta.com's H100 comparison), TensorRT-LLM delivers the lowest p50 and p95 TTFT at every concurrency level - e.g. 1,280ms p95 vs vLLM's 1,450ms at 100 concurrent requests, and 105ms p50 vs 120ms at 10 - once you pay its compilation and per-model engine-build tax. If your deployment is one stable model on NVIDIA GPUs with a hard latency SLO - a production copilot with a fixed 8B router model, say - compile it with TensorRT-LLM and accept the inflexibility. vLLM and SGLang trade that last increment of speed for the ability to swap models with a config change. |
| Debuggability of agent behavior (deterministic replay) | SGLang (RadixAttention engine with radix-tree prefix reuse) Best edge | SGLang shipped a deterministic inference mode (announced on the LMSYS/SGLang blog in September 2025, building on Thinking Machines' batch-invariant kernels) that makes outputs reproducible across runs regardless of batching - genuinely useful when debugging why an agent took a branch, reproducing a failure trace, or running RL-style training loops where sampler nondeterminism poisons comparisons. vLLM offers seeded sampling but batch composition can still perturb results at the numerics level. Niche today, but agent teams doing failure forensics or eval-grade reproducibility will feel the difference. Verify current status in each project's docs before depending on it - this corner of both engines moves quickly. |
| Operational maturity and deployment surface | vLLM (PagedAttention engine with block-hash automatic prefix caching) Best edge | vLLM's larger deployment base means more battle-tested Helm charts, more Kubernetes operators (including the production-stack project), more provider-managed offerings, and more Stack Overflow surface when something breaks at 2 a.m. Its 2026 additions - P/D-style disaggregation work, KV offloading to CPU/remote via Mooncake - target exactly the long-context, many-session pain agent platforms hit at scale. SGLang runs serious production workloads too, but the median ops question has a faster answer in the vLLM ecosystem. |
Choose vLLM (PagedAttention engine with block-hash automatic prefix caching) if...
- Teams serving a rotating cast of open-weight models who need new architectures supported the week they drop.
- Non-NVIDIA or mixed-hardware deployments: AMD ROCm, TPUs, AWS Neuron, Ascend - vLLM's plug-in backend story has no SGLang equivalent at the same breadth.
- Agent platforms hitting long-context memory pressure that want KV offloading (Hybrid Memory Allocator, Mooncake) and disaggregation options from the engine rather than custom infrastructure.
- Ops teams that value the largest community, deployment tooling, and managed-provider support surface in OSS serving.
Choose SGLang (RadixAttention engine with radix-tree prefix reuse) if...
- Agent fleets where most requests share long prefixes - coding-agent workers with a common system prompt + tool schema head, RAG over shared document contexts, batched eval runs.
- Interactive multi-turn assistants where TTFT per turn compounds and cached-head prefill is the biggest lever.
- Teams that need deterministic, replayable inference for agent debugging, evals, or RL training loops.
- Structured-generation-heavy workloads (constrained decoding, JSON schemas) that benefit from SGLang's programmatic frontend as well as its runtime.
Decision rules
- 01Measure before migrating: replay a day of real agent traces against both engines and record prefix-cache hit rate, p95 TTFT, and tokens/sec. If your shared-prefix ratio is low, the engines are within ~2-4% and the migration is not worth it.
- 02If your traffic is loop-heavy with fat shared heads (long system prompt + tool schemas re-sent every turn), default to SGLang - radix-tree prefix reuse is the architectural fit, and the ~29% shared-prefix margin in 2026 benchmarks is the realistic (not 6.4x) expectation for mixed multi-turn traffic.
- 03If you need day-one support for whatever model ships next quarter, or non-NVIDIA hardware (AMD, TPU, Neuron), default to vLLM - ecosystem breadth is its durable advantage and v0.24-era velocity shows no sign of slowing.
- 04If you serve one fixed model on NVIDIA with a hard latency SLO, benchmark TensorRT-LLM before either - it wins raw p95 TTFT in third-party three-way benchmarks at the cost of per-model compilation.
- 05Whichever engine you pick, keep the context head byte-stable (system prompt, tool schema serialization, even whitespace) - both caches key on exact token sequences, and a timestamp interpolated into the system prompt silently zeroes your hit rate.
- 06Don't trust any 2025-era benchmark including the 6.4x figure as a deployment prediction: both engines' numbers move release to release (vLLM v0.24.0 shipped June 29, 2026; SGLang iterates comparably fast), so re-bench on current versions with your own traces.
Migration Notes
- Both engines expose OpenAI-compatible endpoints, so the app-side change is usually a base URL - the migration work is in benchmarking, deployment manifests, and tuning flags, not application code.
- Re-tune, don't port, your launch flags: block sizes, cache limits, and scheduling knobs don't map one-to-one, and defaults differ enough to swing benchmarks - a mis-tuned target engine will lose to a tuned incumbent regardless of architecture.
- Validate prefix-cache behavior explicitly after migrating: log cache-hit metrics from the engine (both expose them) for a week of real traffic; the win you migrated for should be visible there before you decommission the old fleet.
- If you rely on quantized checkpoints, confirm the exact quant format (AWQ, GPTQ, FP8 flavors) is supported at equal quality on the target engine - format coverage differs and is the most common silent-quality regression in engine swaps.