Embedding Estimator
AI embedding calculator online. Estimate tokens and vector dimensions for embedding models. Plan vector DB storage—free embeddings tool.
Sample Text (for token estimation)
0 tokensTokens
0
per text
Vector Dimensions
1,536
float32 values
Vector Size
6.00 KB
per embedding
Cost per Embed
$0.00
openai
Model Comparison
| Model | Dimensions | Size/Vector | $/1M tokens |
|---|---|---|---|
| text-embedding-4-large openai | 4,096 | 16.00 KB | $0.15 |
| text-embedding-3-large openai | 3,072 | 12.00 KB | $0.13 |
| text-embedding-3-small openai | 1,536 | 6.00 KB | $0.02 |
| text-embedding-ada-002 openai | 1,536 | 6.00 KB | $0.1 |
| Cohere Embed v3 cohere | 1,024 | 4.00 KB | $0.1 |
| Gemini Embedding 001 google | 3,072 | 12.00 KB | $0.15 |
| Gemini Text Embedding google | 768 | 3.00 KB | $0.025 |
About Embeddings
- • Embeddings convert text to fixed-size vectors for similarity search
- • Higher dimensions often mean better quality but more storage
- • Vectors are stored as float32 (4 bytes per dimension)
- • Pricing as of 2026-07-08 - check providers for current rates
Features
- Calculate precise vector database storage requirements (in MB/GB) based on dimensionality and precision
- Built-in presets for top embedding models: text-embedding-3-large, text-embedding-ada-002, and Cohere English
- Dynamic memory overhead estimation mapping HNSW (Hierarchical Navigable Small World) index costs
- Toggle between Float32, Float16, and Int8 (quantization) to see exact compression savings
- Calculate scaling costs for managed cloud vector databases like Pinecone, Milvus, and Qdrant
- Estimate token generation costs alongside raw storage byte sizes
Common Use Cases
- Architecting the infrastructure and cloud budgeting for enterprise Retrieval-Augmented Generation (RAG) pipelines
- Determining whether a vector dataset can fit entirely in the RAM of an AWS EC2 or DigitalOcean droplet
- Evaluating the trade-off between using a high-dimension model (OpenAI 3072-dim) versus a fast, local model (MiniLM 384-dim)
- Estimating the massive cost reductions achieved by applying scalar quantization (Int8) to billions of vectors
- Planning data migration times by calculating the raw byte size of the exported JSON/Parquet vector files
The Mathematics of Vector Storage
Embeddings are numerical representations (arrays of floats) of text. By mapping text to multi-dimensional space, computers can perform semantic searches (finding text with similar meaning, rather than exact keyword matches).
Calculating Raw Storage Size:
- Dimensionality: The number of values in the vector. OpenAI's
text-embedding-3-smallhas 1536 dimensions. - Precision: The standard is
Float32, which takes 4 bytes per number. - Formula:
Dimensions * Precision Bytes = Size Per Vector. - Example: 1536 * 4 bytes = 6,144 bytes (~6.1 KB) per vector.
The Indexing Overhead: Storing the raw vectors on disk is cheap. However, to search them quickly, Vector Databases build an Approximate Nearest Neighbor (ANN) index in RAM (usually an HNSW graph). This graph adds a massive 20% to 60% memory overhead on top of your raw data size, meaning a 10GB dataset often requires 15GB of expensive server RAM.
Examples
Valid - text-embedding-3-large (Uncompressed)
Dimensions: 3072
Precision: Float32 (4 Bytes)
Raw Vector Size: ~12.2 KB
1 Million Docs: ~12.2 GB RAM Valid - all-MiniLM-L6-v2 (Local)
Dimensions: 384
Precision: Float32 (4 Bytes)
Raw Vector Size: ~1.5 KB
1 Million Docs: ~1.5 GB RAM Valid - Quantization Savings
Dimensions: 1536
Precision: Int8 (1 Byte)
Raw Vector Size: ~1.5 KB (75% savings!)Frequently Asked Questions
Does the length of the text (tokens) affect the final byte size of the vector?
No! This is a common misconception. Whether you embed a single word like "apple" or a massive 500-word paragraph, the resulting vector array will always be the exact same fixed size (e.g., exactly 1536 numbers). The text length only affects the initial API cost to generate the embedding.
What is Quantization (Float32 vs Int8)?
Quantization is a compression technique used by advanced databases (like Qdrant or Milvus). By converting 4-byte Float32 numbers into 1-byte Int8 integers, you reduce your total RAM usage by exactly 75%. While this introduces a tiny loss in search precision (usually < 1%), it is mathematically necessary to affordably store hundreds of millions of vectors.
Why do Vector Databases need so much RAM?
To perform semantic search in milliseconds across millions of records, the vector index must be kept in volatile RAM. If the database has to read from a standard SSD (page faulting), search latency spikes from 10ms to thousands of milliseconds, breaking the application.
💡 Tips
- If you are building a hobby project, use a smaller model like `all-MiniLM-L6-v2` (384 dims). The storage is 4x smaller than OpenAI, and search speeds are significantly faster.
- Always over-provision your server RAM by at least 30% beyond your raw vector size to account for the HNSW indexing overhead.