We keep the chapters in this part at order-of-magnitude, so this sheet is the one place the volatile names and prices live. When it disagrees with a provider's docs, the docs win.

Embeddings and vector stores

A small team picks one embedding model and one store, and both are painful to swap later, so choose once and write the choice down.

OpenAI text-embedding-3-large is the safe default with the widest integration support.
Voyage AI (now part of MongoDB) is the pick when retrieval quality is your bottleneck.
Cohere Embed v4 is strong on multilingual work and pairs naturally with Cohere Rerank.
BGE-M3 is the open-weight choice when you self-host.
pgvector fits when you already run Postgres and hold well under a hundred million vectors.
Pinecone fits when you want a managed service and zero operations work.
Qdrant and Weaviate fit when you want open source, metadata filtering, and native hybrid search.
Chroma fits for local prototypes and evals.
Turbopuffer sits on object storage and fits when the bill at scale is the problem.

Keyword, hybrid, and rerankers

Production search stacks almost always combine lexical and vector retrieval, then rerank the merged list.

BM25 keyword search is still the workhorse; Elasticsearch and OpenSearch are its common homes, and most vector stores now ship it natively.
Hybrid fusion means running both retrievers and merging with reciprocal rank fusion, keeping a few dozen candidates.
Rerankers then reorder those candidates: Cohere Rerank and Voyage rerank-2.5 are the common managed picks, ZeroEntropy's zerank models lead some current leaderboards, and open cross-encoders like the BGE rerankers cover self-hosting.

Agentic retrieval

Every major provider now lets the model call search as a hosted tool, which turns retrieval into a loop the model drives.

OpenAI offers web search and file search tools in the Responses API; file search is a managed vector store on their side.
Anthropic offers web search and web fetch tools on the Claude API, and you can return your own retrieval as cited search result blocks.
Google offers Grounding with Google Search plus the File Search tool on the Gemini API, a managed RAG service with citations built in.

Memory tooling

Memory is retrieval pointed at the model's own past, and these are the current approaches.

Provider features: Anthropic's memory tool lets Claude read and write a memory store you host; ChatGPT ships memory as a product feature rather than an API primitive.
Mem0 is a managed memory layer that extracts what is worth keeping from each conversation.
Zep builds a temporal knowledge graph (Graphiti) for facts that change over time.
Letta, descended from MemGPT, is an agent runtime where the agent manages its own memory.
Plain files that the agent reads and writes remain a legitimate v1.

Context features worth knowing

Three pricing levers change the economics of context, all at order-of-magnitude level.

Prompt caching: repeated prefix tokens cost about a tenth of normal input price on current Anthropic and OpenAI models, so cache the system prompt and the documents and pay full price only for the question.
Long-context tiers: standard windows sit in the hundreds of thousands of tokens; million-token tiers exist and typically bill at roughly double past a threshold.
Batch discounts: work that can wait a day costs about half price, and the discount stacks with caching.

Prices and names rot fast, so check the provider's current docs, and this sheet's date, before trusting either.