Enterprise RAG Retrieval & Generation Latency Simulator
Simulate p50, p90, and p99 end-to-end retrieval and generation latency across enterprise architectures.
⏱️ Enterprise RAG Pipeline Latency Simulator
Simulate p50, p90, and p99 end-to-end retrieval and generation latency across enterprise architectures.
Mathematical Methodology & Benchmark Assumptions
1. Pipeline Execution Waterfall Physics
Production enterprise Retrieval-Augmented Generation (RAG) involves sequential and parallelized stages, each contributing discrete latency envelopes:
L_p50 = T_embed + T_vector_search + T_cross_rerank + T_ttft + (N_gen_tokens * T_per_token)
L_p90 = L_p50 * 1.28 + Sigma_network
L_p99 = L_p50 * 1.85 + 2 * Sigma_network2. Empirical Latency Benchmarks by Subsystem
- Dense Vector Search (Top-K=5, 1536-dim):
- Qdrant: In-memory HNSW graphs with SIMD AVX-512 vector distance kernels: 6–10 ms.
- Milvus: Distributed sharded cluster with GPU-accelerated search: 12–16 ms.
- pgvector (Postgres 16): In-process HNSW index with shared buffer overhead: 22–28 ms.
- Pinecone Serverless: Multi-tenant HTTPS round-trip and remote partition lookup: 45–85 ms.
- Cross-Encoder Re-Ranking: Two-stage retrieval utilizing FlashRank (ONNX runtime) or bge-reranker-large:
T = 15ms + (K * 2.4ms). - Generative LLM Serving:
- On-Prem vLLM (DeepSeek-V3 FP8): Time-to-First-Token (TTFT) ~120 ms; token streaming ~192 tok/s (5.2 ms/token).
- On-Prem vLLM (Llama-3.3-70B FP8): TTFT ~90 ms; token streaming ~263 tok/s (3.8 ms/token).
- Cloud Multi-Tenant APIs (GPT-4o / Claude 3.5): TTFT 450–750 ms; token streaming ~70–80 tok/s (12.5–14.0 ms/token).