Estimate what it costs to embed your document or RAG corpus across OpenAI, Cohere, Google, and open-source models — plus the resulting vector storage footprint.
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Before a retrieval-augmented generation (RAG) system, semantic search index, or recommendation engine can retrieve anything, every chunk of your corpus first has to be converted into a numeric embedding vector — and that conversion is billed per token, just like a chat completion. This embedding cost calculator turns your corpus size (chunk count and average tokens per chunk) into a total embedding cost across five common providers, plus the vector storage footprint those embeddings will occupy once they land in a vector database, so you can budget a RAG pipeline's data-ingestion side before you commit to a model.
Two numbers, from one set of inputs: the dollar cost to run your entire corpus through an embedding API, and the raw disk space the resulting vectors will need once you pick a dimension. Enter your chunk count, average tokens per chunk, chosen embedding model, and target dimension, and the calculator returns total embedding cost, a normalized cost per 1,000 chunks, total tokens processed, and storage size in both megabytes and gigabytes — plus a side-by-side comparison across five models and five dimensions so you're not stuck evaluating one option at a time.
It's built for RAG engineers scoping a new knowledge-base or document-search feature, ML engineers deciding whether a model or dimension upgrade is worth the re-embedding bill, data platform teams sizing a vector database before provisioning it, startup founders budgeting an AI search feature's ingestion cost, and students or researchers learning how embedding pricing and vector storage scale with corpus size.
Embedding cost is often small per token, but multiplying by a corpus of tens or hundreds of millions of tokens turns fractions of a cent into a meaningful line item, especially when a corpus needs periodic re-embedding as documents change. Just as important, and easy to overlook, is the storage side: every embedding vector you generate lives in a vector database indefinitely, so the dimension you choose sets an ongoing infrastructure cost, not just a one-time API charge. Sizing both sides together, the way you would budget a traditional dataset with a Cloud Storage Cost Calculator, helps you avoid picking a high-dimension model for marginal quality gains you didn't actually need.
How this embedding cost calculator turns corpus size into a dollar figure and a storage footprint
Price per 1M Tokens is each embedding model's published rate, shown directly in the model dropdown — from $0.02 for OpenAI text-embedding-3-small up to $0.15 for Google gemini-embedding, or $0 API cost for self-hosted open-source models. The 4-byte figure in the storage formula reflects a standard 32-bit float per dimension, the default precision most vector databases use unless you explicitly quantize to int8 or a lower precision.
The length of the numeric vector a model outputs — 384 to 3072 in this calculator. Higher dimensions can capture more semantic nuance but cost more to store and, for some providers, more to generate.
How you split documents into embeddable pieces. Smaller chunks with overlap improve retrieval precision but increase total chunk count — and therefore total tokens billed — for the same source corpus.
Each vector needs dimension × 4 bytes of raw storage (float32). A 1,536-dimension vector needs 6,144 bytes; a 3,072-dimension vector needs 12,288 bytes — before any database index overhead.
From corpus size to a total embedding cost and storage estimate
Enter the total count of individual text pieces you plan to send to the embedding API — this is your chunk count after splitting documents, not your raw file count.
Enter the average token length of one chunk, based on your actual chunking strategy (sentence, paragraph, or fixed token count) rather than a rough guess.
Select from OpenAI text-embedding-3-small, text-embedding-3-large, Cohere embed-v4, Google gemini-embedding, or a self-hosted open-source option — each shows its price per 1M tokens right in the dropdown.
Select 384, 768, 1024, 1536, or 3072 dimensions. This only affects the vector storage estimate below, not the embedding cost itself.
See total embedding cost, cost per 1,000 chunks, total tokens processed, and estimated vector storage in MB and GB.
Review the model comparison list and the two charts to see how switching embedding models or dimensions changes both cost and storage footprint for the same corpus.
Using the calculator's own default scenario — 50,000 chunks, 300 tokens/chunk, OpenAI text-embedding-3-small, 1,536 dimensions
Suppose you're embedding a support-docs knowledge base of 50,000 chunks, averaging 300 tokens per chunk, using OpenAI's text-embedding-3-small ($0.02 per 1M tokens) at 1,536 dimensions.
Explanation: At this volume, embedding cost is trivial ($0.30 total) because text-embedding-3-small is priced at just $0.02 per 1M tokens — the real budgeting question is storage, not API spend, since 293 MB of raw vectors will need to live in a vector database indefinitely and grow every time the knowledge base is updated. Switching to text-embedding-3-large would push the one-time embedding cost to $1.95 and, at its native 3,072 dimensions, roughly double the storage footprint to around 586 MB — a tradeoff worth making only if retrieval quality genuinely improves for your use case.
What your total embedding cost generally implies
| Total Embedding Cost | What It Generally Means | Recommended Next Step |
|---|---|---|
| Under $10 | Small corpus or prototype-scale knowledge base | Use any model, including a premium one — cost isn't the constraint |
| $10 – $100 | Small production knowledge base or internal search tool | Proceed as planned; recheck cost if the corpus grows quickly |
| $100 – $1,000 | Meaningful corpus, likely tens of millions of tokens | Compare model and dimension tradeoffs before committing |
| $1,000 – $10,000 | Large enterprise corpus or frequent re-embedding cadence | Evaluate self-hosted open-source models and negotiate volume pricing |
| Over $10,000 | Very large corpus, likely billions of tokens | Strongly consider self-hosted embedding infrastructure to remove per-token fees |
If your embedding cost looks tiny but storage looks large: that's normal and common — embedding is usually cheap per token, while the resulting vectors persist in your vector database indefinitely. Budget storage as an ongoing infrastructure line item, not a one-time cost like the embedding API charge.
If you're comparing models at very different price points: weigh the price gap against your retrieval quality requirements. A model 6x more expensive is only worth it if it measurably improves search relevance for your specific documents and queries — test on a sample before committing your whole corpus.
These are cost and storage estimates based on published per-token rates and raw float32 vector size, not exact billing or database figures. Always confirm current pricing and your vector database's actual per-vector overhead before finalizing a budget.
This calculator provides planning estimates only. Actual embedding charges depend on your provider's live pricing and exact tokenizer, and actual vector database storage costs depend on your provider's index overhead, replication, and metadata fields, which are not modeled here. Always verify against your provider's billing dashboard before finalizing a budget.
Where estimating embedding and vector storage cost up front genuinely helps
Project the one-time embedding bill and ongoing storage footprint before building a support-docs or internal-wiki search feature.
Estimate what re-processing your entire corpus will cost when a provider ships a newer or higher-quality embedding model.
Compare how smaller chunks with overlap increase total chunk count, embedding cost, and storage versus larger, non-overlapping chunks.
Run the same corpus across OpenAI, Cohere, and Google pricing to see which provider fits your budget and dimension needs.
Estimate the data-ingestion side of a RAG chatbot separately from the per-query generation cost covered by an LLM cost tool.
Check whether moving from 768 to 1536 or 3072 dimensions is worth the extra storage and, for some providers, extra API cost.
See the $0 API-cost baseline for open-source models like BGE or E5, then budget GPU time separately for high-volume corpora.
Use the MB/GB output as a starting point for your vector database's storage-based pricing tier or self-hosted disk budget.
Estimate how much storage a Matryoshka-style dimension reduction, such as 3072 down to 1024, would save on an existing corpus.
Project per-customer or per-workspace embedding cost for a SaaS product that embeds each tenant's documents separately.
Budget the cost of embedding a paper corpus or dataset for a research project's semantic search or clustering pipeline.
Estimate the cost of re-embedding a document set after a data classification change or legal e-discovery indexing requirement.
What this embedding cost calculator does well, and where it can't replace live billing or database data
Dimensions, price per 1M tokens, and use-case fit across well-known embedding providers
| Model | Dimensions | Price / 1M Tokens | Best Use-Case Fit |
|---|---|---|---|
| OpenAI text-embedding-3-small | 1536 (truncatable) | $0.02 | High-volume RAG where cost per token matters most |
| OpenAI text-embedding-3-large | 3072 (truncatable) | $0.13 | Maximum retrieval quality, technical/code search |
| Cohere embed-v4 | 1024 / 1536 (Matryoshka) | $0.10 | Multilingual and multimodal (text + image) RAG |
| Google gemini-embedding | 3072 (configurable) | $0.15 | Google Cloud-native pipelines, multilingual corpora |
| Voyage AI voyage-3 | 1024 | ~$0.06 (varies by tier) | Domain-tuned retrieval (legal, code, finance) |
| Open-source BGE-large / E5 (self-hosted) | 1024 | $0 API / GPU infra cost | Cost-sensitive, high-volume, or data-residency-restricted deployments |
Summary: This embedding cost calculator gives you an instant, free projection of what it costs to embed a RAG corpus across five common providers, plus the vector storage footprint the resulting embeddings will occupy, so you can budget the data-ingestion side of a retrieval system with real numbers instead of guesswork. Pair it with the AI Token Calculator and Context Window Calculator for a complete RAG cost picture, from ingestion through generation.
Common questions about embedding cost and vector storage estimates
Official documentation to complement this calculator — always verify live rates before finalizing a budget
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