{"head":{"title":"RAG Cost Calculator: Cost per Query | OceanLab","description":"Estimate what a RAG app costs: embedding your documents once, the vector database, and the LLM call with retrieved context on every query. Editable.","canonical":"https://oceanlab.in/tools/rag-cost-calculator"},"tool":{"slug":"rag-cost-calculator","path":"/tools/rag-cost-calculator","name":"RAG cost","h1":"RAG cost calculator","lede":"Work out the one-off and monthly cost of a retrieval-augmented (RAG) app, and what each question costs.","category":"ai","categoryTitle":"AI cost and planning","method":["One-off: the corpus is turned into tokens (pages × words per page × tokens per word), chunk overlap is added, and it is embedded once at the model's price.","Monthly: re-embedding the share of documents that change, embedding each question, the vector database, and the LLM call. Each LLM call carries the system prompt (mostly from cache), the question and the retrieved chunks as input, plus the answer as output.","Vector database prices were not verified for any vendor, so they start at 0. Enter your vendor's storage, query and base fees. Storage assumes 4-byte floats plus 50% for the index and metadata."],"sources":[{"label":"Google embedding pricing","url":"https://ai.google.dev/gemini-api/docs/pricing","checkedAt":"2026-10-25"},{"label":"Voyage AI embedding pricing","url":"https://docs.voyageai.com/docs/pricing","checkedAt":"2026-10-25"},{"label":"Anthropic API pricing","url":"https://platform.claude.com/docs/en/about-claude/pricing","checkedAt":"2026-10-23"},{"label":"OpenAI API pricing","url":"https://developers.openai.com/api/docs/pricing","checkedAt":"2026-10-23"},{"label":"Google API pricing","url":"https://ai.google.dev/gemini-api/docs/pricing","checkedAt":"2026-10-23"},{"label":"DeepSeek API pricing","url":"https://api-docs.deepseek.com/quick_start/pricing","checkedAt":"2026-10-23"},{"label":"MiniMax API pricing","url":"https://platform.minimax.io/docs/pricing/overview","checkedAt":"2026-10-23"}],"faq":[{"question":"What makes up the cost of RAG?","answer":"Embedding the documents once, embedding each question, the vector database, and the LLM call, which carries the retrieved chunks as input. The LLM call is usually the largest part."},{"question":"Why is the vector database at zero?","answer":"No vector database's prices were verified, so the defaults are 0. Enter your vendor's storage, query and base fees."},{"question":"How do I lower the cost per query?","answer":"Retrieve fewer or smaller chunks, cache the system prompt, and try a cheaper model if answer quality holds up in testing."},{"question":"Where do the prices come from?","answer":"Each price is read from the provider's official pricing page and shows the date it was checked. Prices that could not be verified are left out, or start at 0 for you to fill in."}],"updatedAt":"2026-10-25"},"related":[{"href":"/tools/embedding-cost-calculator","label":"Embedding cost","detail":"Enter the size of your documents and compare what embedding them costs on each model, now and each month."},{"href":"/tools/llm-api-cost-calculator","label":"LLM API cost","detail":"Enter your monthly tokens and the input, cached and output mix, and compare what each model would cost."},{"href":"/tools/fine-tuning-vs-rag","label":"Fine-tuning vs RAG","detail":"Answer eight questions about your use case to see whether retrieval, fine-tuning, both or a good prompt fits best."}],"studies":[{"meta":{"slug":"kaganek","industry":"ai","projectType":"RAG knowledge assistant","country":"pl","tags":["ai","enterprise"],"compliances":["GDPR","EU AI Act","ISO/IEC 27001","ISO/IEC 42001","Polish labour law"],"cover":{"src":"/case-studies/kaganek/01-home.png","width":1440,"height":1000}},"text":{"en":{"title":"Kaganek - RAG Knowledge Assistant with Citations and Permissions Intact","description":"Designed and built a clickable internal knowledge assistant for a Kraków consulting engineering firm: staff ask in Polish or English and get answers built only from the firm's own SharePoint, Confluence, Google Drive and file share documents, with every claim linked to its source passage and an honest \"I don't know\" when the sources are weak.","category":"AI Agents & Automation / Knowledge Assistant","client":"Modrzew Engineering"}}},{"meta":{"slug":"lukturis","industry":"internal","projectType":"AI knowledge search","country":"lv","tags":["ai","enterprise"],"compliances":["GDPR","Latvian Personal Data Processing Law","EU AI Act","DORA","ISO/IEC 27001"],"cover":{"src":"/case-studies/lukturis/01-home.png","width":1440,"height":1000}},"text":{"en":{"title":"Lukturis - Permission-Aware Enterprise Search Across Seven Internal Tools","description":"Designed and built a clickable enterprise search product for a 600-person Riga software company: one search box over Google Drive, Notion, Jira, Slack, GitHub, HubSpot and the HR policy site, with every result filtered by what the person can already open in the source tool, extractive answer cards that always link to the source, review nudges for content owners and an admin view of connector health and index freshness.","category":"Internal Tools / Enterprise Search","client":"Vējzeme Software"}}},{"meta":{"slug":"mukwa","industry":"legal","projectType":"AI contract review","country":"zm","tags":["ai","enterprise"],"compliances":["Data Protection Act 2021 (Zambia)","LAZ rules of professional conduct","Minerals Regulation Commission Act 2024"],"cover":{"src":"/case-studies/mukwa/01-home.png","width":1440,"height":1000}},"text":{"en":{"title":"Mukwa - AI Contract Review Checked Against the Playbook and Signed Off by a Lawyer","description":"Designed and built a clickable contract review tool for the in-house legal team of a Lusaka copper miner: the AI extracts each clause of a third-party contract, compares it with the company playbook, flags risks with a plain explanation and drafts redlines, while a lawyer decides every flag and only a lawyer with the right authority can sign off.","category":"LegalTech / AI Contract Review","client":"Kabwelume Copper Limited"}}},{"meta":{"slug":"anvesha","industry":"ai","projectType":"Research agent","country":"in","tags":["ai","fintech","enterprise"],"compliances":["SEBI RA Regulations","SEBI PIT Regulations (UPSI)","SEBI LODR","Copyright Act 1957","RFC 9309 (robots.txt)","DPDP Act 2023"],"cover":{"src":"/case-studies/anvesha/01-home.png","width":1440,"height":1000}},"text":{"en":{"title":"Anvesha - Deep-Research Agent for Cited Equity Research","description":"Designed and built a clickable deep-research agent for a SEBI-registered Mumbai research boutique: analysts brief the agent, watch each run stay inside its time, cost and page budget, and get a draft report in which every factual sentence links to a quoted source passage they must accept before it can leave the team.","category":"AI Agents / Research Agent","client":"Mahim Bay Research"}}}],"more":[{"href":"/case-studies/industry/ai","label":"AI agents & automation case studies","detail":"Every OceanLab product build in AI agents & automation."},{"href":"/case-studies/industry/data","label":"Data & analytics case studies","detail":"Every OceanLab product build in Data & analytics."},{"href":"/case-studies","label":"All case studies","detail":"Every OceanLab product build."},{"href":"/ai-automation","label":"AI automation","detail":"AI built into real business workflows, with people in charge."}],"today":"2026-10-25"}