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The LLM Embedding Cost Calculator estimates the total expense of generating vector embeddings for text data using commercial API models such as OpenAI text-embedding-3-small, text-embedding-3-large, and the legacy ada-002. Embeddings convert text into dense numerical vectors (typically 256 to 3072 dimensions) that capture semantic meaning, enabling similarity search, clustering, and retrieval-augmented generation (RAG) pipelines. As of 2025, OpenAI prices text-embedding-3-small at $0.02 per million tokens and text-embedding-3-large at $0.13 per million tokens, while the older ada-002 remains available at $0.10 per million tokens. Alternatives include Cohere Embed v3 at roughly $0.10 per million tokens and free open-source models like BGE, E5, and GTE that require self-hosted GPU infrastructure. This calculator is used daily by machine learning engineers building semantic search systems, product teams adding AI-powered recommendations, and data engineers designing ETL pipelines that must re-embed documents whenever models are updated. A typical enterprise knowledge base of one million documents at 500 tokens each totals 500 million tokens, costing just $10 with text-embedding-3-small but $65 with text-embedding-3-large. Understanding these cost differences is essential for choosing the right quality-to-cost ratio. Beyond the raw embedding API cost, this calculator also factors in chunking overhead (overlapping chunks can increase token counts by 10 to 30 percent), re-embedding frequency for document updates, and the complementary cost of storing vectors in a database like Pinecone, Weaviate, or pgvector. By modeling the full pipeline, teams can make informed decisions about model selection, chunk sizes, and update cadences that keep costs predictable as data volumes grow.
Total Embedding Cost = (Number of Documents x Average Tokens per Document x (1 + Overlap Ratio)) / 1,000,000 x Price per 1M Tokens. For example, embedding 200,000 documents at 400 tokens each with a 15 percent overlap using text-embedding-3-small at $0.02 per 1M tokens: Total Tokens = 200,000 x 400 x 1.15 = 92,000,000 tokens. Cost = (92,000,000 / 1,000,000) x $0.02 = 92 x $0.02 = $1.84.
- 1Begin by counting the total number of documents or text chunks in your corpus. This could be product descriptions, support articles, PDF pages, or any text unit you want to make searchable. If you have raw documents that have not yet been chunked, estimate how many chunks each will produce based on your target chunk size (commonly 256 to 512 tokens) and overlap settings.
- 2Determine the average token count per chunk. You can use the OpenAI tokenizer tool or the tiktoken Python library to measure exact token counts for sample documents. A rough rule of thumb is that one token equals approximately four English characters or 0.75 words. For multilingual corpora, token counts may be 1.5 to 2 times higher than English equivalents due to how byte-pair encoding handles non-Latin scripts.
- 3Select your embedding model and note its pricing tier. OpenAI text-embedding-3-small costs $0.02 per million tokens, text-embedding-3-large costs $0.13 per million tokens, and ada-002 costs $0.10 per million tokens. The small model produces 1536-dimension vectors by default (configurable down to 256), while the large model outputs 3072 dimensions. Higher dimensions generally yield better retrieval quality at the cost of more storage and slower similarity searches.
- 4Configure your chunking overlap ratio. Most RAG implementations use 10 to 20 percent overlap between consecutive chunks to preserve context at chunk boundaries. An overlap ratio of 0.15 means each chunk shares 15 percent of its tokens with the next chunk, effectively increasing your total token count by that same percentage. This is a frequently overlooked cost multiplier.
- 5Calculate the one-time embedding cost by multiplying total tokens (including overlap) by the model price. Then estimate your monthly re-embedding cost based on what fraction of your corpus changes each month. If 5 percent of documents are updated monthly, your recurring cost is 5 percent of the initial embedding cost. Some teams also re-embed their entire corpus when migrating to a newer model version for improved quality.
- 6Factor in vector storage costs that complement the embedding expense. Pinecone charges $0.096 per pod-hour for its s1 pod type, Weaviate Cloud starts at $25 per month for small clusters, and self-hosted pgvector on a modest cloud VM costs $50 to $150 per month. The total cost of ownership for embeddings includes both generation and ongoing storage and querying.
- 7Review the final cost breakdown, which shows per-document cost, total one-time cost, estimated monthly recurring cost, and vector storage cost. Use this to compare models and make a data-driven decision. Many teams find that text-embedding-3-small provides 95 percent or more of the retrieval quality of text-embedding-3-large at 85 percent lower cost, making it the default choice for most production workloads.
Total tokens with overlap equal 50,000 times 300 times 1.10, which is 16,500,000 tokens. Dividing by one million and multiplying by $0.02 gives $0.33. This demonstrates how affordable embedding small to medium corpora has become with modern pricing.
Two million documents at 600 tokens with 20 percent overlap yields 1,440,000,000 tokens total. At $0.13 per million tokens, the cost is $187.20 for the full corpus. While not trivial, this is a one-time cost that can be amortized over months of production use.
Migrating from ada-002 to text-embedding-3-small for 500,000 documents saves approximately 80 percent on embedding costs. The newer model also offers better retrieval benchmarks, making migration both cheaper and higher quality.
Non-Latin scripts typically inflate token counts by 50 to 100 percent compared to English. The 700 tokens per document already accounts for this inflation. Total cost is 300,000 times 700 times 1.15 divided by 1,000,000 times $0.13, equaling $31.40.
E-commerce companies embed millions of product descriptions to power semantic search features that understand natural language queries like "warm waterproof jacket for hiking" rather than requiring exact keyword matches. A major retailer with 5 million products at an average of 200 tokens each would spend just $20 using text-embedding-3-small to embed their entire catalog, enabling search experiences that significantly increase conversion rates and average order value.
Legal technology firms embed case law databases containing hundreds of thousands of court opinions to enable attorneys to find relevant precedents through natural language queries. A typical legal corpus of 500,000 documents at 800 tokens each with 20 percent overlap costs approximately $9.60 with text-embedding-3-small. This one-time investment replaces manual legal research that previously took hours per case, delivering an enormous return on investment.
Customer support platforms embed their entire knowledge base of help articles, FAQ entries, and past ticket resolutions to automatically suggest relevant answers when customers submit new tickets. A company with 100,000 support articles can embed them for under $1 with text-embedding-3-small, then use cosine similarity to match incoming questions against existing solutions. This typically deflects 40 to 60 percent of tickets before they reach a human agent.
Healthcare technology companies embed medical literature and clinical guidelines to build retrieval-augmented generation systems that help physicians quickly find evidence-based answers to clinical questions. A database of 2 million research abstracts at 400 tokens each costs approximately $16 to embed. The vector representations enable semantic matching that understands medical synonyms and related concepts in ways that traditional keyword search cannot.
When embedding very short texts such as product titles or search queries that
When embedding very short texts such as product titles or search queries that are only 5 to 20 tokens long, the cost per document becomes negligible (under $0.0000004 each with text-embedding-3-small), but the total API call overhead can become the bottleneck. OpenAI embedding endpoints accept batches of up to 2048 texts per request, and batching short texts together dramatically improves throughput from hundreds to tens of thousands of embeddings per second. Always batch short texts rather than sending individual API calls.
For corpora exceeding 100 million documents, the cost calculation must account
For corpora exceeding 100 million documents, the cost calculation must account for API rate limits and the time dimension. OpenAI embedding endpoints have rate limits measured in tokens per minute, and embedding 100 million documents at 500 tokens each (50 billion tokens) at a rate limit of 5 million tokens per minute would take nearly 7 days of continuous processing. At this scale, self-hosting an open-source model on multiple GPUs is typically more practical and cost-effective, even accounting for infrastructure complexity.
When building multilingual embedding systems, be aware that token counts vary significantly across languages.
A 100-word passage in English might use 130 tokens, while the same meaning expressed in Japanese could require 200 to 250 tokens due to character-level tokenization. This means embedding costs for Japanese, Chinese, Korean, Thai, and Arabic corpora can be 50 to 100 percent higher than equivalent English corpora. Budget accordingly and consider language-specific embedding models that may tokenize more efficiently for your target languages.
| Model | Provider | Price per 1M Tokens | Default Dimensions | Max Tokens | MTEB Score |
|---|---|---|---|---|---|
| text-embedding-3-small | OpenAI | $0.02 | 1536 | 8191 | 62.3% |
| text-embedding-3-large | OpenAI | $0.13 | 3072 | 8191 | 64.6% |
| text-embedding-ada-002 | OpenAI | $0.10 | 1536 | 8191 | 61.0% |
| embed-v3 (English) | Cohere | $0.10 | 1024 | 512 | 64.5% |
| Gemini text-embedding-004 | $0.00625 | 768 | 2048 | 66.3% | |
| BGE-large-en-v1.5 | Open Source | Self-hosted | 1024 | 512 | 63.6% |
| E5-large-v2 | Open Source | Self-hosted | 1024 | 512 | 62.7% |
Which embedding model offers the best cost-to-quality ratio?
For most production use cases, OpenAI text-embedding-3-small delivers the best balance of quality and cost at $0.02 per million tokens. It scores within 2 to 5 percentage points of text-embedding-3-large on standard retrieval benchmarks like MTEB while costing 85 percent less. The large model at $0.13 per million tokens is worth the premium only when working with highly specialized domains where every percentage point of retrieval accuracy matters, such as legal or medical search.
How many tokens does a typical document contain?
A standard 500-word English document contains approximately 625 to 700 tokens using BPE tokenization. For RAG applications, documents are typically chunked into 256 to 512 token segments with 50 to 128 token overlap. One token is roughly four English characters or 0.75 words. Non-Latin scripts like Chinese, Japanese, and Korean can use 1.5 to 2 times more tokens per word due to how byte-pair encoding handles their character sets.
Should I use the small or large embedding model?
Start with text-embedding-3-small for development and initial production deployment. Measure your retrieval quality metrics such as recall at k and mean reciprocal rank on your specific data. If these metrics are below your quality threshold, upgrade to text-embedding-3-large and compare. In practice, fewer than 20 percent of teams find the quality improvement from the large model justifies the 6.5 times higher cost.
How does embedding cost compare to vector database storage cost?
Embedding generation is typically a one-time or infrequent cost, while vector storage is an ongoing monthly expense. For one million 1536-dimension vectors, the raw storage is about 6 GB. In Pinecone, this might cost $70 to $100 per month depending on your pod configuration. The initial embedding cost with text-embedding-3-small for the same million documents would be around $10 to $15, a one-time charge. Over a year, vector storage costs usually exceed embedding generation costs by 5 to 10 times.
Can I reduce embedding costs by lowering vector dimensions?
Yes, text-embedding-3-small and text-embedding-3-large both support Matryoshka dimension reduction, allowing you to truncate vectors to fewer dimensions without re-embedding. Reducing from 1536 to 256 dimensions cuts storage and search costs by about 83 percent with only a modest decrease in retrieval quality. This technique is especially useful for large-scale applications where storage and search latency costs dominate the total cost of ownership.
Is it cheaper to self-host open-source embedding models?
Self-hosting models like BGE-large or E5-large-v2 on a cloud GPU becomes cost-effective at scale. A single A10G GPU instance at approximately $1 per hour can embed roughly 1,000 to 2,000 documents per second. At 10 million documents, self-hosting costs about $5 to $10 in compute time versus $2 to $13 via API. However, you also bear the costs of infrastructure management, monitoring, and scaling. The break-even point is typically around 50 to 100 million tokens per month for ongoing workloads.
How often should I re-embed my documents?
Re-embed documents only when the content changes materially or when you migrate to a new embedding model. For content updates, implement incremental re-embedding that processes only changed documents rather than the entire corpus. Most teams re-embed their full corpus once or twice per year when upgrading to a newer model version. Setting up a change detection pipeline that flags modified documents for re-embedding keeps costs proportional to your actual content churn rate.
プロのヒント
Use text-embedding-3-small with Matryoshka dimension reduction to 256 dimensions for prototyping. This gives you vectors that are 6 times smaller than the default 1536 dimensions, drastically cutting storage and search costs while retaining approximately 90 percent of retrieval quality. You can always re-embed at full dimensionality for production if your evaluation metrics demand it.
ご存知でしたか？
The entire English Wikipedia, containing approximately 6.7 million articles with roughly 4.4 billion tokens, can be fully embedded using text-embedding-3-small for about $88. This means creating a complete semantic search engine over all of human knowledge curated on Wikipedia costs less than a single dinner at a mid-range restaurant.