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Free Embedding API: Compare Vector Services for RAG and Search

Compare free embedding APIs for RAG, semantic search, and text processing; including dimensions, context length, multilingual support, and free tier details.

Last reviewed: 2026-08-05

Quick answer: Embedding converts text to numerical vectors and is the foundation of semantic search, RAG, and document clustering. Choosing a free embedding API requires evaluating vector dimensions, context length, supported languages, request quotas, post-quota pricing, and data retention policies simultaneously. A service optimized for English may not perform well for Persian text.

What is Embedding and Why Does It Matter?

An embedding model converts input text to a numerical vector with fixed dimensions. These vectors represent semantic similarity: two texts with similar meanings produce closer vectors.

Main use cases:

  • RAG: Retrieve relevant documents before sending to a language model
  • Semantic search: Replace keyword search with meaning-based search
  • Document clustering: Group documents without manual labels
  • Similarity comparison: Compare text similarity between documents

Selection Criteria

Vector Dimensions

  • 384-768 dimensions: Suitable for small to medium projects
  • 1024-1536 dimensions: Suitable for complex projects
  • 3072+ dimensions: Highest quality but more storage and search cost

Context Length

  • For short texts (paragraphs, FAQs), most models are sufficient
  • For long texts (articles, books), longer context models are needed

Language Support

  • Multilingual models generally work well for Persian
  • Quality for low-resource languages may be lower than English

Quota and Pricing

  • Many services offer monthly free quotas
  • Check per-token pricing after quota expires

Data Retention Policy

  • Are embedding requests stored?
  • Is data used for model improvement?

Comparison of Free Embedding APIs

OpenAI text-embedding

  • Dimensions: 1536 (small) or 3072 (large)
  • Free tier: None (requires credit)
  • Advantage: High quality, official documentation

Jina Embeddings

  • Dimensions: 1024 or 768
  • Free tier: 1M tokens/month
  • Advantage: Multilingual support, real free tier

Cohere Embed

  • Dimensions: 1024
  • Free tier: 1000 requests/month
  • Advantage: High quality for long texts

Nomic Embed

  • Dimensions: 768
  • Free tier: Locally runnable via Ollama
  • Advantage: No API needed, complete privacy

Technical Tips for Using Embedding APIs

Chunk Size Selection

  • 256-512 tokens: For FAQs and short questions
  • 512-1024 tokens: For articles and medium documents
  • Over 1024 tokens: Usually degrades quality

Persian Text Tips

  • Normalize Persian text before embedding (half-space, Unicode)
  • Punctuation and Persian numbers may affect quality
  • For Persian search, hybrid search (keyword + semantic) usually outperforms pure embedding

Example: Getting Embeddings with OpenAI-Compatible API

import os
import requests

api_key = os.environ["EMBEDDING_API_KEY"]
base_url = "https://api.groq.com/openai/v1"

response = requests.post(
    f"{base_url}/embeddings",
    headers={"Authorization": f"Bearer {api_key}"},
    json={"model": "nomic-embed-text-v1.5", "input": "Your Persian text here"}
)
embeddings = response.json()["data"][0]["embedding"]

Summary

Choose an embedding API based on actual project needs. Start with a free or local model (Ollama). As the project grows, evaluate quality on real data before upgrading.

Comparison with Self-Hosted Solutions

If your data volume is high or you need maximum privacy, self-hosted solutions like Ollama with models such as nomic-embed-text can be a good choice. The main advantage is independence from external services and no data leaving your infrastructure. The downside is requiring more hardware resources and managing models yourself. For small to medium projects, free APIs are usually more cost-effective. For large-scale production deployments, self-hosted solutions may become more economical once you exceed free tier limits.

Practical Tips for Iranian Developers

  • Always check provider accessibility from Iran before committing to a platform
  • Start with providers that offer genuinely free tiers without requiring credit cards
  • For student projects, try Ollama first before moving to paid APIs
  • Always have a fallback plan for when the primary API is unavailable
  • Test actual embedding quality with your specific Persian content before making architectural decisions

For a comprehensive catalog of providers with verified access status, quotas, and quotas, visit the Free LLM API catalog. For source files and issue reporting, see the GitHub repository.

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