Technical reference

Choosing an Embedding Model for an AI Knowledge Stack

OpenAI's text-embedding-3-small is usually the right answer. Here's when it isn't, and what to pick instead.

The Short Answer

Recommended Default: OpenAI text-embedding-3-small

For most implementations of an embedding model AI knowledge base, OpenAI text-embedding-3-small provides the optimal balance of cost and performance. It utilizes 1,536 dimensions and is priced at $0.02 per million tokens, making it highly accessible for small to mid-sized projects.

The model maintains high MTEB scores and typical latency under 100ms. At a personal or small-team scale, operational costs often average around $2 per month depending on the frequency of index updates. While larger models exist, the diminishing returns in retrieval accuracy for general datasets rarely justify the increased cost.

# Example OpenAI embedding call
import openai

response = openai.Embedding.create(
  input="Knowledge base query here",
  model="text-embedding-3-small"
)

Exceptions to this recommendation are narrow, typically limited to requirements for extreme data sovereignty or highly specialized domain retrieval where proprietary models like Voyage AI outperform generalist options.

When to Use Nomic Embed Instead

Open-Source and Local Deployment

Nomic Embed v1.5 (768 dimensions) serves as the primary alternative for those avoiding vendor lock-in or managing sensitive data. It can be deployed locally via tools like LM Studio, removing per-token API costs entirely after infrastructure overhead.

Local deployment is critical when processing massive volumes—exceeding 100 million tokens per day—where API costs become prohibitive. Additionally, Nomic allows for sub-10ms local latency, which is essential for real-time developer test loops or edge computing environments.

Performance Trade-offs

While Nomic Embed offers strong long-context performance, general query quality typically trails proprietary models by approximately 5% on MTEB benchmarks. However, this gap closes when the model is fine-tuned on domain-specific corpora for a specialized embedding model AI knowledge base.

  • Data Sovereignty: No data leaves the local environment.
  • Cost: Zero marginal cost per token.
  • Context: Strong support for long documents (up to 8,192 tokens).

When to Use Cohere or Voyage AI

Specialized Retrieval and Multilingual Support

Voyage AI's voyage-3 ranks as the top embedding model for AI knowledge bases in 2026. It outperforms competitors on MTEB retrieval benchmarks, particularly in law, finance, and code. With a price point of $0.06 per million tokens and support for 32K context, it offers superior accuracy-to-cost ratios compared to OpenAI's large models.

Cohere embed-v3/v4 is the preferred choice for multilingual requirements, supporting over 100 languages. It integrates tightly with Cohere's reranking pipeline to improve final result precision in RAG workflows.

Comparative Model Capabilities

Model Primary Strength Max Context MTEB Score (Approx)
Voyage AI voyage-3 Domain Accuracy (Law/Code) 32,000 65-67
Cohere embed-v3/v4 Multilingual Coverage 512 63
OpenAI text-emb-3-large General Ecosystem 8,191 64

Operational Choices

Vector Storage and Maintenance

Selecting an embedding model AI knowledge base requires calculating the storage overhead associated with vector dimensions. Higher dimensionality increases memory usage in vector databases like Pinecone or Milvus.

Estimated storage costs per 1 million vectors (using float32):

  • OpenAI (1536d): ~6GB
  • Cohere (1024d): ~4GB
  • Nomic (768d): ~3GB

Migration and Normalization

Vectors are not interchangeable between models. Switching from OpenAI to Voyage AI requires a full re-embedding of the entire knowledge base, as each model maps semantics to different coordinate spaces.

To maintain retrieval quality, ensure cosine similarity is used for normalization across the vector set. When implementing these systems, developers should use a versioning system for their indices to avoid downtime during model migrations.

# Example: Calculating Cosine Similarity
from sklearn.metrics.pairwise import cosine_similarity

similarity = cosine_similarity([vector_a], [vector_b])
Appendix · Questions

Reference: common questions

What's the best embedding model for an AI knowledge base?
Voyage AI voyage-3 (or voyage-3-large) is currently the top choice, leading MTEB retrieval benchmarks with a score of ~65-67. It outperforms OpenAI and Cohere in accuracy—specifically on legal, finance, and code data—while maintaining a low cost of $0.06 per million tokens.
Is OpenAI text-embedding-3-small good enough for my knowledge base?
It is an excellent choice for rapid prototyping due to its low $0.02/1M token price point. However, if your RAG pipeline requires high precision on complex domain data or long documents, voyage-3 provides significantly better retrieval accuracy and a larger 32K context window.
Can I use Nomic Embed locally for my AI knowledge base?
Yes, Nomic Embed (v1.5/v2) is a leading open-source alternative designed for self-hosting. While it trails proprietary models like Voyage on MTEB scores, it offers strong long-context performance and eliminates API costs if you have the infrastructure to host it.
How do I switch embedding models without re-indexing everything?
You generally cannot switch models without re-indexing because different models map text to different vector spaces. To minimize downtime, run a parallel indexing process with the new model (e.g., moving from OpenAI to Voyage AI) and swap the production pointer only after the new index is fully populated.
What's the cost of embedding 1 million documents?
Cost depends on token count per document rather than document count. Using a high-performance model like Voyage AI voyage-3, you would pay $0.06 per million tokens; OpenAI text-embedding-3-large is more expensive at $0.13 per million tokens.
Does Cohere outperform OpenAI for retrieval in knowledge bases?
Cohere embed-v3/v4 is highly competitive, particularly for multilingual applications supporting over 100 languages. However, on general MTEB retrieval benchmarks, it typically scores around 63, trailing Voyage AI's ~67 and slightly behind OpenAI's maximum quality models.
Should I use 768 or 1536 dimensions for my embeddings?
Lower dimensions like 768 (Nomic) or 1,024 (Voyage AI) reduce storage costs and speed up query latency. Higher dimensions like OpenAI's 3,072 can capture more nuance but increase the memory footprint of your vector database; use Matryoshka embeddings if you need to shrink dimensions without losing significant accuracy.
How much storage does pgvector need per embedding?
Storage depends on the dimensionality and data type (e.g., float32). A 1,024-dimension vector using float32 typically requires 4KB per row; utilizing int8 quantization, as seen in some Voyage AI implementations, can significantly reduce this footprint while maintaining retrieval performance.
What is Voyage AI and when is it worth switching to it?
Voyage AI provides state-of-the-art embedding models optimized for RAG. It is worth the switch if you are seeing 'hallucinations' caused by poor retrieval or if you have long documents that exceed OpenAI's 8k token limit, as Voyage supports up to 32K context.
Can I fine-tune embedding models for my specific domain?
While most users rely on pre-trained models, some providers allow fine-tuning to improve domain-specific retrieval (e.g., medical or legal). If fine-tuning isn't an option, choosing a model like voyage-3 that already ranks high in specialized MTEB categories is the best alternative.
How does embedding quality affect retrieval in a knowledge base?
Embedding quality determines how accurately a query matches the relevant document chunk. Higher MTEB scores correlate to better NDCG@10, meaning the most relevant information is more likely to appear in the top results, directly reducing LLM hallucinations and improving RAG reliability.