How to reduce RAG latency and costs: an optimization guide

When an engineering team ships a RAG prototype to 50 beta testers, everything looks fast and inexpensive. The embedding API calls cost fractions of a penny, vector searches execute in 50 milliseconds, and the LLM responds in two seconds.

Then the system launches to 100,000 monthly active users. Suddenly, cloud bills surge into five figures, embedding endpoints encounter rate limits during peak traffic hours, and users complain that the assistant takes five to eight seconds to answer basic questions.

Scaling RAG requires moving past naive pipeline implementations. In 2026, high-throughput production systems apply aggressive latency and cost optimization techniques across all four stages of the RAG pipeline.

In any business application, user questions follow a power-law distribution: 20% of common questions account for 80% of total query volume. "How do I reset my password?", "What is your refund policy?", and "Where is my invoice?" are asked thousands of times per day.

Traditional HTTP key-value caching (like Redis matching exact query strings) fails because users phrase identical questions differently ("How do I get my money back?" vs "Can I request a refund?").

Semantic Caching solves this by maintaining a vector index of previously answered questions. When a new prompt arrives, the system embeds it and checks the semantic cache. If a cached question matches with a cosine similarity > 0.96, the system immediately returns the cached answer. This bypasses the vector search and the LLM entirely, responding in under 20 milliseconds at zero marginal cost.

• Implement semantic caching with a high confidence threshold (0.95 - 0.98) to prevent false-positive matches
• Memoize query embeddings: cache the query-to-vector calculation in Redis so recurring queries do not call embedding APIs
• Set cache expiration (TTL) based on document update frequencies (e.g. invalidate cache when documents are edited)

Storing millions of 1536-dimensional vectors using 32-bit floating-point numbers (`float32`) requires massive amounts of expensive RAM. For example, 10 million float32 vectors consume over 60 GB of raw memory just for the vectors.

Vector Quantization compresses these vectors with minimal loss in search accuracy:

Scalar Quantization (SQ8) converts 32-bit floats into 8-bit integers (`int8`), cutting memory consumption by 75% and speeding up vector distance calculations by up to 3x with virtually no loss in retrieval accuracy.

Product Quantization (PQ) clusters sub-vectors into codebooks, reducing memory usage by 90% or more, allowing millions of vectors to be searched entirely in high-speed CPU cache.

A major cause of high RAG bills is using your most expensive frontier model (like GPT-4o or Claude 3.5 Sonnet) for every intermediate task: query routing, query rephrasing, document grading, and final answer generation.

Model Tiering implements an intelligent division of labor. Use a fast, sub-cent model (like an open-weight 8B model or low-cost cloud tier) for query classification, pronoun condensation, and chunk relevance scoring. Only pass the final, validated context to your top-tier model for response synthesis.

Finally, implement Context Trimming. When five chunks are retrieved, they often contain redundant document headers, repeated introductory sentences, or boilerplate navigation text. A lightweight regex or tokenizer filter strips repetitive tokens before prompt assembly, reducing prompt token counts by 20% to 30%.

Before scaling your AI assistant, review your optimization checklist:

• Deploy a semantic cache in Redis or PostgreSQL for recurring high-volume questions
• Enable Scalar Quantization (SQ8) on your pgvector or vector database indexes
• Use small, fast models for query routing, decomposition, and relevance evaluation
• Cap the maximum number of retrieved tokens passed to the final generation prompt
• Stream token outputs to users to minimize perceived latency