At this scale, embedding costs are negligible — under $1/month even with the most expensive option. The real cost in RAG is the LLM generation step, not embeddings.

Semantic Search (100,000 documents, 5,000 queries/day)

A larger-scale search application with 100K documents and higher query volume.

Even at 100K documents, the one-time embedding cost is under $7. Monthly query costs stay under $1. Embeddings are cheap — the expensive part is storing and searching the vectors.

High-Volume Classification (1M documents/month)

If you're embedding incoming data at scale (e.g., classifying support tickets, content moderation), costs grow linearly with volume.

At 1M documents/month, the choice matters more. OpenAI small is 5x cheaper than Cohere, and Google is free (if you stay within rate limits).

Quality Comparison

Price isn't the only factor. Here's how the models compare on the MTEB (Massive Text Embedding Benchmark):

• OpenAI text-embedding-3-large: 64.6 MTEB score — top-tier quality, best for complex semantic tasks
• OpenAI text-embedding-3-small: 62.3 MTEB score — excellent value, strong quality for most use cases
• Cohere embed-english-v3.0: 64.1 MTEB score — competitive quality, strong multilingual support
• Google embedding-001: 62.0 MTEB score — good quality, best free option

The quality gap between these models is small (within 3-4 MTEB points). For most applications, the cheapest option works well.

When to Use Each Provider

Use Google (Free) when:

• You're prototyping or building a side project
• Cost is the primary concern
• You need moderate quality (good enough for most RAG and search)
• Your volume stays within rate limits

Use OpenAI text-embedding-3-small when:

• You need the cheapest paid option
• You're already using OpenAI for generation
• You need high throughput without rate limit concerns
• You want the best price-to-quality ratio

Use Cohere when:

• You need multilingual support (100+ languages)
• You're building search specifically (Cohere's search-optimized model)
• You want input-type differentiation (search_document vs search_query)
• You're in the Cohere ecosystem already

Hidden Costs: Vector Storage and Search

Embedding API costs are just one piece of the puzzle. The bigger expenses are often:

• Vector database: Pinecone ($70/mo for 1M vectors), Weaviate Cloud ($25/mo+), or self-hosted (free but requires management)
• Storage: 100K vectors at 1,536 dimensions = ~600MB of storage
• Compute: Similarity search at scale requires CPU/GPU resources
• Re-embedding: When you update your embedding model, you need to re-embed everything

Pro tip: Use OpenAI's dimension reduction parameter (e.g., 256 instead of 1,536) to cut storage costs by 6x with minimal quality loss.

— See if you're overpaying for AI APIs