Updated AI & Embedding Cost Tool

Embedding Cost Calculator

Estimate embedding API cost for OpenAI and custom providers. Enter token counts or dataset size, choose a model such as text-embedding-3-small or text-embedding-3-large, or define your own price per 1K or 1M tokens to see total cost, per-request cost, and normalized rates.

OpenAI Models Custom Providers Tokens & Dataset Size Budget Planning

Calculate Embedding API Cost

This Embedding Cost Calculator lets you estimate how much you will spend on vector embeddings using both built-in OpenAI pricing and fully custom pricing for any provider or model. Start with a token count or approximate from dataset size, then choose a model and pricing unit to see the total cost and per-token rates.

Select a built-in OpenAI embedding model or configure your own provider and price. For maximum accuracy, use a real token count from your logs or a token counting tool. The dataset-size tab uses a standard heuristic of 256 tokens per kilobyte (1 token ≈ 4 characters).

If provided, cost per request will be estimated as total cost ÷ requests.

If you know the exact number of tokens across all embedding calls, enter it here. The calculator normalizes everything to “USD per 1K tokens” and “USD per 1M tokens” so you can compare different providers.

Default 256 tokens per KB (1 token ≈ 4 characters). Adjust if you know your data density.

This tab converts dataset size to an approximate token count using a configurable “tokens per KB” density. For mixed-language or code-heavy datasets, you may want to adjust the default density based on empirical measurements from a small sample.

Embedding Cost Calculator – Plan and Compare Your Vector Budget

Modern search, retrieval-augmented generation (RAG) and recommendation systemsy heavily on vector embeddings. While embeddings areatively cheap per request, large datasets and constant indexing can add up. The Embedding Cost Calculator from MyTimeCalculator helps you estimate and compare the cost of generating embeddings across OpenAI and custom providers before you commit to a pipeline.

With a few inputs—either total tokens or approximate dataset size—you can see the total cost, normalized cost per 1K and per 1M tokens, and optional cost-per-request estimates. The calculator is designed for quick budgeting, capacity planning and “what-if” scenarios when evaluating different models or vendors.

1. Built-in OpenAI Pricing and Custom Models in One Tool

The calculator supports two main pricing modes:

  • OpenAI mode: Choose a built-in embedding model such as text-embedding-3-small or text-embedding-3-large. The calculator uses the current public pricing in USD per 1M tokens and derives per-1K token rates automatically.
  • Custom mode: Define any provider and model name and specify a price either per 1K tokens or per 1M tokens. The calculator normalizes this to both units so you can compare it side-by-side with OpenAI or other services.

This mixed design means you can use the same interface for OpenAI, other commercial APIs, or even internal models where you have your own estimated marginal cost per token.

2. Token-Based vs Dataset-Based Cost Estimation

There are two natural ways to think embedding cost, and the calculator provides a dedicated tab for each:

  • Token-based estimation: If you already know the total number of tokens you will embed across all requests, simply enter that value. This is the most accurate method and is ideal once you have usage logs or token counts from a tokenizer.
  • Dataset-based estimation: When you are still designing your system and only know the total dataset size (for example, “a 250 MB document collection”), the calculator converts size to an approximate token count using a configurable “tokens per KB” density. This is perfect for early-stage planning.

In both cases, the calculator displays the estimated total cost and normalized per-token rates, so you can quickly see whether a given model fits your budget.

3. How to Use the Embedding Cost Calculator

  1. Choose the pricing mode. Select OpenAI if you want to use built-in pricing for text-embedding-3-small or text-embedding-3-large. Select Custom provider / model if you have your own per-token price.
  2. Configure the model or custom price. In OpenAI mode, just pick a model from the dropdown. In custom mode, enter a descriptive name and specify the price per 1K or 1M tokens.
  3. Pick a tab depending on what you know. If you know the total tokens, use the From Token Count tab. If you only know dataset size, use the From Dataset Size tab.
  4. Enter your usage numbers. For tokens, type the total tokens and optionally the number of embedding requests. For dataset size, enter the size and unit (KB, MB, GB) and optionally adjust the tokens per KB density.
  5. Click the calculate button. The calculator will instantly show total cost, cost per 1K and cost per 1M tokens, plus a short natural-language summary of your scenario.
  6. Iterate with different models or prices. Change the model, provider or price assumptions to see how your budget changes. This is especially helpful when comparing open-source versus hosted solutions.

4. Planning Real Projects With Embedding Cost Estimates

Even though per-token prices are small, embedding-heavy workloads can involve millions or billions of tokens. Some practical scenarios where the Embedding Cost Calculator is useful include:

  • RAG and knowledge bases: Estimating the one-time cost of embedding all documents in a knowledge base, plus the ongoing cost of embedding new content as it is added.
  • Search and recommendation systems: Understanding how many users, products or content items you can index within a fixed monthly budget.
  • Multi-tenant SaaS platforms: Allocating a portion of customer subscription fees to cover embedding costs, and designing fair usage limits.
  • Vendor comparisons: Comparing OpenAI pricing to alternative providers or internally hosted models, using a common per-1K and per-1M token baseline.

5. Understanding Per-1K and Per-1M Token Pricing

Many providers publish prices per 1K tokens, while others use per-1M token pricing or show both. The calculator always converts prices to both units to avoid confusion. This helps answer questions such as:

  • How much more expensive is model A versus model B for the same number of tokens?
  • What is the effective cost per 1M tokens for a provider that only publishes per-1K token pricing?
  • How much would my costs change if my token volume doubles?

Because embedding workloads often run at scale, even small differences in per-token pricing can matter when you are planning for millions of tokens per day or per month.

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Embedding Cost Calculator FAQs

Frequently Asked Questions

Quick answers to common questions estimating embedding API cost, pricing units, dataset-based approximation and how to interpret the calculator outputs.

Token-based estimation uses the actual number of tokens you plan to embed, which is the most accurate method. Dataset-based estimation starts from file size and converts it into an approximate token count using a “tokens per KB” density. If you can, use token-based inputs from a tokenizer or API logs. Use dataset-based estimation when you only know the size of your corpus and want a quick order-of-magnitude budget.

The default density of 256 tokens per kilobyte is a practical rule of thumb based on 1 token being 4 characters. Real datasets can deviate from this depending on language, formatting and the proportion of whitespace or markup. For higher accuracy, measure a small sample of your data with a tokenizer, compute the actual tokens per KB and update the density field in the calculator accordingly.

Yes. Switch to the “Custom provider / model” mode, enter your provider and model name, and specify a price per 1K or 1M tokens. The calculator will treat this the same way as OpenAI pricing and show normalized cost per 1K and per 1M tokens. This makes it straightforward to compare OpenAI to alternative APIs or self-hosted models where you have an internal cost estimate per token.

If you enter the number of embedding requests, the calculator divides the total cost by that number to show an average cost per request. This is especially helpful when you want to think in terms of “cost per user action” or “cost per document indexed” rather than purely per-token. Keep in mind that individual requests may vary in size; the per-request value is an average based on your total tokens and request count.

Embedding API pricing typically only covers the cost of generating vectors from text. Once embeddings are generated, you still need to store them in a database or vector store and pay any associated storage and query fees. The Embedding Cost Calculator focuses on the embedding step only; for a complete budget you should also include storage, retrieval and any additional model calls used for RAG or ranking.

It is a good idea to recalculate cost whenever you significantly change your data volume, model choice or usage pattern. For example, re-run estimates when adding a new content source, switching embedding models or scaling to new regions. Token prices can also change over time, so checking current provider pricing and updating the calculator assumptions periodically will keep your budget projections realistic.

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