Embeddings API Pricing 2026 | Cheapest LLM APIs

Direct Answer

OpenAI's text-embedding-3-small costs just $0.02 per 1 million input tokens, making it the cheapest mainstream embedding API in 2026. For indie game developers, startup founders, and app teams building RAG systems, chatbot memory, or knowledge bases on a budget, embedding costs are often overlooked—but they add up fast. This guide breaks down real 2026 pricing across all major providers so you can pick the right API for your product.

Key Takeaways

Cheapest embeddings: OpenAI text-embedding-3-small at $0.02/1M tokens; AWS Bedrock and Hugging Face offer competitive rates starting at $0.02–0.10/1M tokens
Input-only billing: Embedding APIs charge only for input tokens (unlike LLMs), so a 10,000-token knowledge base costs pennies
Volume discounts matter: At 100M+ tokens/month, negotiate directly with providers like Anthropic and Google for custom rates
IntelliVerse-X Gateway alternative: Single API key for Claude, GPT, Gemini, DeepSeek, Qwen, plus embeddings from $0.24/M tokens—no vendor lock-in
Best for RAG: Pair cheap embeddings with a vector database (Pinecone free tier, Supabase pgvector) to build affordable semantic search

What Are Embeddings and Why Do They Matter for Apps?

Embeddings convert text, images, or code into numerical vectors that AI models can understand. When you build a chatbot with memory, a game with AI NPC dialogue, or a knowledge base search, embeddings power the semantic matching behind the scenes. Unlike LLM tokens (which charge per prompt + completion), embeddings only charge for input tokens, so they're dramatically cheaper at scale.

For a US startup in San Francisco or a solo indie dev in Austin, embeddings are the backbone of Retrieval-Augmented Generation (RAG)—the technique that lets your AI remember your company's docs, game lore, or user data without retraining a model. Cost matters here because you'll embed thousands or millions of tokens daily.

2026 Embeddings API Pricing Breakdown

OpenAI (Most Popular)

OpenAI's embedding models lead the market in 2026:

text-embedding-3-small: $0.02 per 1M input tokens (recommended for most apps)
text-embedding-3-large: $0.13 per 1M input tokens (higher quality, slower)
Billing: Input tokens only; no charge for output
Example cost: Embedding a 100,000-token knowledge base costs $0.002 with text-embedding-3-small

Claude (Anthropic)

Anthropic's Claude models include embedding capabilities:

Claude Opus 4.8 embeddings: Custom pricing (contact sales for volume discounts)
Batch processing discount: 50% off if you use Anthropic's Batch API
Best for: Teams already using Claude for LLM tasks (single invoice, unified support)

Google Gemini

Google's Gemini API offers embedding models:

Embedding-001: $0.0001 per 1K input tokens (~$0.10 per 1M tokens)
Embedding-004: Slightly higher cost, better quality
Free tier: 60 requests/minute for testing
Best for: Teams already using Google Cloud (GCP integration, BigQuery sync)

AWS Bedrock

AWS Bedrock's foundation models include multiple embedding providers:

Titan Embeddings: $0.10 per 1M input tokens
Cohere Embed (via Bedrock): $0.10 per 1M input tokens
On-demand pricing: Pay per request; no monthly commitment
Best for: US enterprises using AWS already (IAM integration, VPC isolation)

Hugging Face & Open-Source Alternatives

Hugging Face Inference API and self-hosted options:

Inference API (managed): $0.10–0.50 per 1M tokens depending on model
Self-hosted (free): Download open models like `all-MiniLM-L6-v2` and run locally
Best for: Cost-conscious indie devs; privacy-first teams

Cost Comparison: Real-World Scenarios

Scenario 1: Indie Game Dev (500K Tokens/Month)

Goal: Embed NPC dialogue, quest descriptions, and player memory.

| Provider | Model | Cost/Month | |----------|-------|------------| | OpenAI | text-embedding-3-small | $0.01 | | Google Gemini | Embedding-001 | $0.05 | | AWS Bedrock | Titan | $0.05 | | Self-hosted | all-MiniLM-L6-v2 | $0 |

Winner: Self-hosted for zero cost; OpenAI if you need managed service.

Scenario 2: Startup Building RAG Chatbot (50M Tokens/Month)

Goal: Index customer docs, FAQs, and product knowledge base.

| Provider | Model | Cost/Month | |----------|-------|------------| | OpenAI | text-embedding-3-small | $1.00 | | Google Gemini | Embedding-001 | $5.00 | | AWS Bedrock | Titan | $5.00 | | Anthropic | Claude (custom) | $2–10 (negotiated) |

Winner: OpenAI for simplicity; Anthropic if bundled with LLM tasks.

Scenario 3: Content Studio (500M Tokens/Month)

Goal: Embed video transcripts, articles, and media metadata for semantic search.

| Provider | Model | Cost/Month | |----------|-------|------------| | OpenAI | text-embedding-3-small | $10.00 | | OpenAI | text-embedding-3-large | $65.00 | | AWS Bedrock | Titan | $50.00 | | Anthropic | Claude (custom) | $50–200 (negotiated) |

Winner: OpenAI small model; negotiate directly with AWS or Anthropic at this scale.

How to Minimize Embeddings Costs

1. Batch Your Requests

Instead of embedding one text at a time, batch 100+ texts per API call. This reduces latency and lets you negotiate volume discounts.

2. Cache Embeddings

Store embeddings in a vector database (Pinecone free tier, Supabase pgvector, Weaviate) so you only embed new content. A 100K-token knowledge base embedded once costs $0.002; re-embedding wastes money.

3. Choose the Right Model

text-embedding-3-small: 99% of use cases (RAG, semantic search, chatbot memory)
text-embedding-3-large: Only if you need top-1% retrieval quality (rare)

4. Use Batch APIs

Anthropic's Batch API offers 50% discounts. OpenAI doesn't yet, but watch for updates in late 2026.

5. Self-Host for Privacy & Cost

Download open-source models like `all-MiniLM-L6-v2` (8M parameters, fast, accurate) and run them on your own GPU or CPU. Zero API costs; trade-off: you manage infrastructure.

Why IntelliVerse-X Gateway Is Different

IntelliVerse-X runs the IntelliVerse AI Gateway—one API key for every LLM (Claude, GPT, Gemini, DeepSeek, Qwen) plus video, image, 3D, avatar, and music models, with RAG, knowledge bases, and user memory built on cheap embeddings.

Key benefits for US indie devs and startups:

Single invoice: No vendor lock-in; switch models without changing code
Cheap embeddings: Starting at $0.24/M tokens (competitive with OpenAI)
Built-in RAG: Knowledge bases and user memory included; no extra vector DB cost
Transparent pricing: No hidden fees; pay only for what you use
US-based support: Chat with engineers in your timezone

Frequently Asked Questions

Q: Why are embeddings cheaper than LLMs?

Embeddings only process input tokens—they don't generate output. LLMs like GPT-5.5 charge for both input ($5/1M) and output ($30/1M), making them 100–1000x more expensive per token. For a RAG chatbot, embeddings cost pennies; the LLM response costs dollars.

Q: Can I use free embedding models?

Yes. Open-source models like `all-MiniLM-L6-v2` (Hugging Face) or `nomic-embed-text` are free to self-host. Trade-offs: you manage servers, updates, and quality. For teams without DevOps bandwidth, paid APIs like OpenAI are faster to launch.

Q: What's the cheapest way to build a knowledge base for my game or app?

Use OpenAI text-embedding-3-small ($0.02/1M tokens) + Supabase pgvector (free tier) or Pinecone free tier. Embed your content once, then query for free. Total cost: $0–5/month for most indie projects.

Sources

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Ready to Build?

Get started with cheap, fast embeddings today. Get an IntelliVerse-X AI Gateway API key and start embedding for just $0.24/M tokens—no setup fees, no minimums. Or book a free 30-minute consultation with our team to design a custom RAG pipeline for your game, app, or content studio.

Embeddings API Pricing 2026: Cheapest LLM APIs for Apps, Games & RAG