What is the cheapest embeddings API?

Usually an open embedding model (such as BGE, E5 or nomic) on a low-cost inference host, rather than a premium hosted model. Several providers also include embeddings in free tiers.

Do open embedding models match paid ones?

For many retrieval tasks, yes. Strong open models are often competitive with hosted ones on real RAG workloads at a fraction of the cost.

How do I cut embeddings costs in RAG?

Embed once and store the vectors, avoid recomputation, use a smaller dimension where quality allows, and tune chunk size so you are not embedding redundant text.

The Cheapest Embeddings API for RAG in 2026

Q: Can I get embeddings for free?

Yes. Some providers include embeddings in their free tiers or let you spend signup credits on them, and open models run on free inference tiers. See the catalog for current offers.

Embeddings are cheap per call but add up fast in RAG. Here is how to pick the cheapest embeddings API in 2026, when an open model wins, and how to cut re-embedding costs.

Embeddings look cheap. A single call costs a tiny fraction of a cent, which is exactly why teams stop thinking about them, then watch the bill grow as they re-embed the same corpus over and over. Here is how to pick the cheapest embeddings API in 2026 and keep retrieval costs flat as you scale.

For RAG storage itself, pair this with free vector databases and embeddings for RAG.

What drives embeddings cost

Embeddings are billed per token of input, so your spend is a function of three things:

How much text you embed (corpus size plus every new document).
How often you re-embed (model changes, chunking changes, accidental recomputation).
The model and its dimension (larger, higher-dimension models can cost more per token and more to store and search).

The first two are usually where the money goes, not the per-token rate.

The providers to compare

The common hosted embedding options include OpenAI, Google (Gemini), Cohere, Mistral and Voyage, alongside open models served by many inference hosts. They differ in price per token, maximum input length, supported dimensions and quality on retrieval benchmarks.

Do not assume the model you already use for generation has the cheapest or best embeddings. Embeddings are a separate decision, and a cheaper provider or an open model often matches a premium one closely enough for retrieval.

Open embedding models are the cost lever

Strong open embedding models (for example the BGE, E5 and nomic families) are inexpensive to run and often competitive with hosted models on real retrieval tasks. Served on a cheap inference host, they can cut embeddings spend dramatically, and some providers include them in free tiers. Compare per-model prices in the rankings.

Cut embeddings spend

Embed once, store forever. Persist vectors and never recompute unless the model or chunking changes. Re-embedding the same text is pure waste.
Use a smaller dimension where quality allows. Some models support shortened embeddings (Matryoshka-style) that store and search faster at a small quality cost.
Chunk deliberately. Oversized or overlapping chunks multiply token counts. Tune chunk size to your content.
Cache at the query side too. Repeated queries can reuse cached query embeddings.

Free embeddings to start

Several LLM providers include embeddings in their free tiers or let you spend signup credits on them, and open models run on free inference tiers. The current, dated list is in the catalog, and free AI API credits covers the signup offers.

Bottom line

The cheapest embeddings API is usually an open model on a cheap host, embedded once and cached hard, at the smallest dimension your retrieval quality tolerates. Start on free tiers, compare options in the rankings, and build the rest of your RAG stack from the catalog with a free account.

Related: free vector databases for RAG.