The 2026 Enterprise LLM Inference Stack: Vendor Map & Evaluation Grid
Definitive market landscape evaluating vLLM, TensorRT-LLM, SGLang, Triton Inference Server, AWS Bedrock, Azure AI, GCP Vertex, and SageMaker across TCO, TTFT latency, and lock-in risk.
The 2026 Enterprise LLM Inference Stack: Vendor Map & Evaluation Grid
A decision-maker's guide to selecting, hosting, and optimizing LLM serving infrastructure.
Market Segment Overview
High-throughput LLM inference has split into two primary architectures:
- Self-Hosted Open-Source Serving Engines (vLLM, TensorRT-LLM, SGLang, Triton) running on private GPU clusters or Kubernetes (EKS/GKE).
- Fully Managed Enterprise Cloud APIs (AWS Bedrock, Azure OpenAI, GCP Vertex AI) providing serverless endpoints with compliance SLAs.
Architectural Comparison Matrix
| Provider / Engine | TTFT Latency (p99) | Throughput ($/1M Tokens) | Memory Management | Lock-in Risk | Best Used For | |---|---|---|---|---|---| | vLLM (v0.10+) | ~120ms | Low ($0.15 - $0.40) | PagedAttention V2 | Zero (Open Source) | High-concurrency self-hosted API services | | NVIDIA TensorRT-LLM | ~90ms | Lowest (Max FP8 / H100 utilization) | In-flight Batching + TRT | Medium (NVIDIA CUDA bound) | Maximum throughput on dedicated Hopper/Blackwell clusters | | SGLang | ~110ms | Low (RadixTree prefix caching) | RadixTree KV reuse | Zero (Open Source) | Complex multi-turn agentic workflows with shared system prompts | | Triton Server | ~140ms | Medium | Dynamic Batching (BLS) | Zero (Open Source) | Heterogeneous multi-model production deployments (PyTorch + ONNX + TensorRT) | | AWS Bedrock | ~220ms | Managed Premium | Serverless | High (AWS Ecosystem) | Enterprise turn-key deployments requiring SOC2/HIPAA compliance out of the box | | Azure AI / OpenAI | ~200ms | Managed Premium | Provisioned Throughput Units (PTU) | High (Microsoft Azure) | Enterprise Microsoft 365 ecosystem integration & GPT-4 class models |
Key Executive Insights
- Prefix Caching is the New Bottleneck: For agentic applications where prompt context is repeated across tool calls, SGLang and vLLM RadixTree KV caching reduce prefill latency by up to 70%.
- Hopper & Blackwell Hardware Optimization: Enterprise teams running dedicated H100/B200 clusters achieve 2.4x higher token throughput per dollar using TensorRT-LLM with FP8 quantization compared to unoptimized PyTorch runners.
- Hybrid Routing Strategy: Top-performing engineering orgs route lightweight classification and RAG requests to self-hosted vLLM clusters while reserving cloud APIs for complex reasoning tasks.