OpenAI File Search vs Vertex AI RAG Engine vs Pinecone Assistant: Real Managed RAG Cost per 1 Million Queries in 2026

The short answer: OpenAI's File Search and Google's Vertex AI RAG Engine converge on an identical headline rate for the retrieval-and-grounding call itself — $2.50 per 1,000 requests, or $2,500 per 1 million queries — despite being built on completely different infrastructure. Neither of those figures includes the underlying LLM's generation tokens, which are billed separately and usually larger. Pinecone Assistant does not have a comparable published per-query rate at all: its pricing is fully usage-based across context tokens, input tokens, output tokens and storage, with a $50/month minimum on its Standard plan and $500/month on Enterprise, and the exact per-token rates for its core "Assistants Context Tokens Processed" line were not published in the documentation reviewed.

What each vendor bills

OpenAI File Search (OFFICIAL, OpenAI pricing documentation and OpenAI community-confirmed billing). Vector storage: $0.10 per GB per day, with the first 1 GB free. File Search tool calls (via the Responses API): $2.50 per 1,000 calls. This tool-call charge is separate from, and in addition to, the token cost of whichever model generates the final answer from retrieved context — File Search's own fee covers only the retrieval step. A vector store search is defined per call, not per document retrieved, so a query returning 20 chunks and a query returning 2 chunks cost the same $2.50-per-1,000 rate.

Vertex AI RAG Engine (OFFICIAL, Google Cloud documentation). RAG Engine is an orchestration layer with no separate platform fee of its own; it bills à la carte for each component it touches. Embedding generation is billed at whichever embedding model's own rate you configure (see this site's companion embedding-cost comparison). Vector storage uses either a "RAG-managed database" (lightweight, bundled into the RAG Engine's own low-cost tier) or a customer-provided Vertex AI Vector Search index, the latter reported at roughly $700–$800/month for a moderately sized dedicated index — a materially different cost profile from a lightweight managed store. Retrieval alone (the retrieveContexts endpoint, no LLM call) bills only for embeddings and storage, with no separate retrieval fee. Generation with grounding (RAG plus an LLM call using retrieved context) adds $2.50 per 1,000 requests on top of the embedding, storage, and the generation model's own token cost — the same $2.50/1,000 figure OpenAI charges for File Search's tool call, though the two fees cover different scopes of work.

Pinecone Assistant (OFFICIAL for the billing structure, QUOTE-ONLY / UNKNOWN for exact per-token rates). Fully usage-based since an April 2026 pricing change that removed an earlier hourly per-assistant fee. Billed line items: Assistants Context Tokens Processed, Input Tokens, Output Tokens, Evaluation Tokens, and Storage. Starter: $0/month, with monthly allowances (500,000 chat input tokens, 300,000 chat output tokens, 1 GB file storage) that reset each billing period. Builder: $20/month flat, no overages — usage beyond the quota is blocked, not billed, up to 3 GB storage free. Standard: $50/month minimum usage commitment, storage at $3/GB/month beyond that. Enterprise: $500/month minimum. The exact dollar rate for the core "Context Tokens Processed" and "Input/Output Tokens" line items — the charges that would actually scale with 1 million queries — was not published in the documentation reviewed; only the plan minimums and storage rate are confirmed numbers.

Cost of the retrieval/grounding call itself

Formula: cost = (queries ÷ 1,000) × $2.50, for the two vendors with a confirmed per-request rate.

This is not a coincidence worth over-reading: both figures are the fee for the retrieval-and-grounding step specifically, not an all-in RAG cost, and both are separate from the generation model's own token charge, which is typically the larger line in a real deployment (a single grounded response with 2,000 tokens of retrieved context and a 300-token answer can cost several times the $0.0025 retrieval fee on most current mid-tier models).

What is not included in the headline rate

Neither vendor's per-query figure above covers the generation model's token cost. Formula: full query cost = retrieval/grounding fee + generation model cost (input + output tokens). Using an ILLUSTRATIVE mid-tier model at $2 input / $10 output per million tokens, with 2,000 tokens of retrieved context plus a 300-token system/query overhead as input and a 300-token answer as output: generation cost per query is about $0.0076 (2,300 × $2/1M + 300 × $10/1M), which is roughly 3 times the $0.0025 retrieval fee itself. At 1 million queries, that adds about $7,600 on top of the $2,500 retrieval/grounding line — the generation model, not the managed-RAG platform fee, is the dominant cost in a realistic deployment on either OpenAI or Vertex.

Storage cost on a representative 10 GB knowledge base

Formula: OpenAI = (GB − 1 free) × $0.10 × 30 days; Pinecone Standard = (GB − 3 free on Builder, or 0 free on Standard) × $3/GB.

Vertex's storage cost is the one genuine trap in this category: choosing the RAG-managed database keeps storage cheap and bundled, but choosing (or defaulting into, on a scaled-up project) a dedicated Vector Search index moves storage from a rounding error to potentially the single largest line item in the whole RAG budget.

Sensitivity

1. Generation model choice. The token cost of the underlying LLM dominates total query cost on OpenAI and Vertex; a frontier model at 5–10x the illustrative rate used here would make the platform's own retrieval fee close to irrelevant by comparison.
2. Vertex storage tier. RAG-managed database versus dedicated Vector Search index is roughly a 20–30x difference in storage cost at moderate scale.
3. Pinecone Assistant's unpublished token rate. Without a confirmed per-token rate for context and input/output tokens, this article cannot produce a comparable per-million-query figure for Pinecone Assistant; budget it only after requesting a rate confirmation or running a metered pilot.
4. Retrieved-context size. Doubling the context tokens retrieved per query roughly doubles the generation-model cost line, which is already the larger of the two cost components on OpenAI and Vertex.

Budgeting traps

• Treating the $2.50-per-1,000 retrieval fee as the whole cost. On both OpenAI and Vertex, it is a minority of the real per-query cost once generation tokens are included.
• Defaulting into Vertex's dedicated Vector Search index without checking whether the RAG-managed database would suffice. The cost difference is an order of magnitude at moderate corpus sizes.
• Budgeting Pinecone Assistant off its plan minimum alone. The minimum is a floor, not a ceiling; actual token usage at 1 million queries could exceed it substantially, but the exact multiplier is not publicly confirmed.
• Forgetting vector storage grows with corpus size, not query volume. A high-query, small-corpus deployment and a low-query, large-corpus deployment can have very different cost profiles even at the same "1 million queries" headline.

What to ask before you buy

Ask Pinecone directly for the current per-token rate on Assistants Context Tokens Processed and Input/Output Tokens before budgeting any specific query volume, since none of that is published. For both OpenAI and Vertex, model the generation-model token cost separately from the platform's retrieval fee, using your actual expected context size and chosen model, since that is typically the larger number.