OSS high-throughput LLM inference engine with PagedAttention. Apache-2.0. Originated at UC Berkeley; standard backend for self-hosted LLM serving. Supports continuous batching, tensor parallelism, quantization (AWQ, GPTQ, FP8).
vLLM is the dominant OSS LLM inference engine for production deployments — Apache-2.0 licensed, originated at UC Berkeley, and the de facto serving layer for self-hosted Llama, Mistral, Qwen, DeepSeek, and dozens of other open-weight models. Its PagedAttention algorithm delivers 3-24x throughput vs naive implementations, and continuous batching keeps GPU utilization high under variable request load. Pick vLLM when you control the inference infrastructure, need OpenAI-compatible APIs, and want the strongest production-tested OSS posture for serving open-weight models at scale.
vLLM is a serving runtime, not a managed service. From a Trust Before Intelligence lens, that means trust comes from your deployment posture — vLLM itself doesn't sign BAAs, doesn't hold SOC 2, and offers no native authorization model. Authentication, audit logging, and per-tenant policy enforcement must come from L5 governance (LiteLLM proxy with virtual keys, Cerbos, OpenFGA, Envoy/Kong API gateway in front). The trust signals vLLM does provide are operational: Prometheus metrics on every dimension that matters (queue depth, KV cache utilization, time-to-first-token, throughput), OpenAI-compatible API surface that's vendor-neutral, and Apache-2.0 license that won't relicense out from under you.
Continuous batching + PagedAttention deliver 3-24x throughput vs naive implementations per the original Berkeley paper. P95 time-to-first-token under 1s for typical Llama-class models on appropriate GPUs; per-token latency 20-50ms after first token. Cap rule N/A.
OpenAI-compatible chat completions API, plus native HF model loading by name. Tools/function-calling support across most popular models. Rich enough that drop-in replacement of OpenAI clients works for many workloads. Cap rule N/A.
No native auth model — server is open by default. Deployment-driven authentication via reverse proxy. Cap rule applied: P-low for inference servers without engine-level authorization. ABAC + RBAC must come from L5.
Runs on any NVIDIA GPU (Volta+ for FP16, Hopper for FP8), AMD MI-series via ROCm, Intel Gaudi via plugins, AWS Inferentia via Neuron. Multi-cloud (AWS, GCP, Azure, on-prem, K8s-native). Strongest A in the LLM Inference category.
Per-request token counts, model metadata, generation parameters captured. No native lineage — request-to-output traceability requires app-layer instrumentation. Cap rule applied: no native lineage caps at 3.
Prometheus metrics out of the box (request rate, queue depth, KV cache hit rate, time-to-first-token p50/p95/p99, decode throughput). Request logs with full input/output. Per-request cost computable from token counts. Cap rule N/A.
G1=N (no engine-level ABAC), G2=N (request logs are local; not formal access audit), G3=N (inference engine, not workflow), G4=N (no model versioning surface beyond what you bake into deployment), G5=N (LLM threat modeling lives at L5), G6=N (no compliance mapping). 0/6 -> 2.
O1=Y (Prometheus exporter is comprehensive), O2=N (no native distributed tracing — comes from app), O3=Y (per-request token counts enable cost attribution), O4=Y (queue depth + latency alarms catch incidents fast), O5=N, O6=N. 3/6 -> 4 lenient (observability is among vLLM's strongest points).
A1=Y (sub-second TTFT), A2=Y (request streaming for incremental output), A3=Y (KV cache reuse via PagedAttention), A4=Y (multi-replica deployment behind a load balancer), A5=Y (production deployments at large scale documented), A6=Y (continuous batching is parallel by design). 6/6 -> 4 (capped to 4 because single-instance has no native HA — needs orchestrator-layer redundancy).
L1=N, L2=N, L3=N, L4=N, L5=Y (model name + version + tokenizer registry as terminology resource), L6=N. 1/6 -> 3 lenient (model registry richness lifts L; HF Hub integration adds breadth).
S1=Y (deterministic at fixed sampling params), S2=Y (typed completion fields), S3=N (no cross-replica consistency check; output may drift across instances at non-zero temperature), S4=Y (typed request/response), S5=N (no built-in content quality validation; that's L5 guardrails), S6=Y (Prometheus metrics flag throughput/latency anomalies). 5/6 -> 4.
Best suited for
Compliance certifications
vLLM the project holds no compliance certifications. Compliance comes from the substrate: AWS GovCloud, Azure Gov, GCP Assured Workloads for FedRAMP; HIPAA via VPC-isolated deployment in BAA-signing infrastructure; SOC 2 via your own org's audit. The vLLM API server has no native auth — proxies (LiteLLM, Envoy, Kong) provide authentication boundary.
Use with caution for
Choose TGI for tighter Hugging Face Hub integration and HF-native deployment patterns. vLLM wins on raw throughput in most benchmarks; TGI wins on HF ecosystem ergonomics. Both are Apache-2.0, both production-grade. TGI fits better when HF Inference Endpoints are part of the broader strategy.
View analysis →Choose SGLang for agent-heavy and structured-output workloads — RadixAttention prefix-cache reuse outperforms vLLM on multi-turn agent traces. vLLM wins on operational maturity and breadth of model support; SGLang wins on agent-specific throughput. Newer; smaller community.
View analysis →Choose Ollama for developer laptops and on-device inference — wraps llama.cpp for trivial setup. vLLM wins on production throughput at scale; Ollama wins on developer ergonomics and CPU/Apple Silicon support. Different tools for different layers of the stack.
View analysis →Choose OpenAI's API when you don't want to operate inference infrastructure, need GPT-4 class capability, and accept the SaaS posture. vLLM wins on cost-at-scale, data sovereignty, and model choice (open-weight only); OpenAI wins on operational simplicity and frontier-model access.
View analysis →Role: L4 LLM Inference engine. Serves open-weight models via OpenAI-compatible API. The runtime that sits behind L7 agent frameworks and in front of L1 vector stores in RAG architectures.
Upstream: Receives requests from L7 agent runtimes (LangGraph, CrewAI, AutoGen) and L4 RAG frameworks (LangChain, LlamaIndex, Haystack). Ingests model weights from HF Hub, S3, or local disk at startup.
Downstream: Returns completions to callers; emits Prometheus metrics consumed by L6 observability (Prometheus/Grafana, SigNoz, VictoriaMetrics). Per-request logs feed L6 LLM evaluation (Promptfoo, LangSmith, Langfuse, Arize).
Mitigation: Always deploy behind an authenticating proxy: LiteLLM with virtual keys, Envoy with JWT auth, Kong with API key plugin, or a service mesh. Test that the bare vLLM port is not externally reachable. Use NetworkPolicies or security groups to enforce.
Mitigation: Pair vLLM with Outlines, Guidance, or Instructor for guaranteed-structured output. Validate every tool-call response with a Pydantic model. For high-stakes tool calls, require LLM self-consistency (retry, confirm) before execution.
Mitigation: Set max-num-seqs and gpu-memory-utilization explicitly (don't accept defaults). Monitor KV cache hit rate via Prometheus. Pre-load test under expected p99 concurrency. Use admission control (queue depth threshold returns 503) to shed load gracefully.
Mitigation: Run task-specific evals on the quantized model BEFORE production deploy. Maintain a canary deployment running unquantized for A/B comparison. Watch task accuracy metrics in production via Promptfoo or similar.
Mitigation: Run at least 2 replicas behind a load balancer. Use KServe or BentoML for K8s-native autoscaling and rolling updates. Monitor replica health; alert on single-replica conditions.
vLLM in EKS within AWS GovCloud; AWS provides the HIPAA BAA and FedRAMP authorization for the substrate. vLLM serves Llama-class models on-prem-equivalent (no data leaves the BAA boundary). LiteLLM proxy in front handles auth + audit log to the SIEM.
vLLM cluster with model-per-tenant sharding via KServe. LiteLLM virtual keys map to per-tenant quotas + budgets. Per-tenant usage flows to billing via token counts in Prometheus.
Ollama is a better fit — wraps llama.cpp for one-command model run on Apple Silicon or CPU. vLLM requires GPU for meaningful throughput; overhead isn't justified for low-traffic prototyping.
This analysis is AI-generated using the INPACT and GOALS frameworks from "Trust Before Intelligence." Scores and assessments are algorithmic and may not reflect the vendor's complete capabilities. Always validate with your own evaluation.