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Open Source Embedding Models in Hybrid AI Deployments

ernie

When organizations look at deploying LLM infrastructure for use cases like AI-powered chat, among others, three main approaches usually come up:

  • Public cloud: outsourcing everything to external providers.
  • Do-it-yourself: running all infrastructure in-house.
  • Hybrid: keeping sensitive data local while offloading heavy inference to specialized cloud providers such as Baseten, Fireworks AI, or Together AI.

The hybrid approach is often the most practical. It gives companies control over their data, ensuring sensitive information stays within their own environment, while still taking advantage of the scalability and performance of external inference clouds.

Most conversations about AI focus on headline-grabbing models like ChatGPT, Claude, or Gemini. But behind the scenes, another type of model makes much of this possible: embeddings. 

Embedding models don’t generate essays or write code. Instead, they convert text, images, or code into dense vectors that capture meaning. This allows a system to recognize that “red sports car” is closer in meaning to “fast automobile” than to “green apple.” It sounds straightforward, but this capability powers some of the most important AI applications today, like semantic search, recommendation engines, anomaly detection, and retrieval-augmented generation (RAG).

And increasingly, organizations are turning to open-source embedding models to build these systems, especially when security, cost, and flexibility are top concerns.

How We Got Here

The idea of embedding isn’t new. Models like Word2Vec and GloVe, developed in the early 2010s, showed how words could be represented in a numerical space. These models captured surprising relationships:

king – man + woman ≈ queen

But they had a limitation: each word had a single embedding, no matter the context. “Bank” meant the same thing whether it referred to a riverbank or a financial institution.

Things changed in 2018, when Google introduced BERT. By using transformers, BERT created embeddings that depended on context. “Bank” in “open a bank account” became distinct from “sit by the river bank.” This made embeddings much more accurate and useful across industries.

Why Open Source

For many companies, open-source embedding models are the preferred choice. The benefits are clear:

    • Transparency: You can inspect the code and training data, which helps with bias detection and compliance.
    • Lower costs: Avoiding commercial API fees can mean big savings, especially at scale.
    • Customization: Models can be fine-tuned on your own data, something most proprietary APIs don’t allow.
    • Community support: Open projects improve quickly thanks to contributions from developers and researchers worldwide.

Popular Open-Source Embedding Models

Several open-source models stand out today:

    • BGE (BAAI): High-performing, multilingual models that often top leaderboards like MTEB.
    • E5 (intfloat): Easy to use and efficient, widely adopted for general-purpose embedding tasks.
    • Nomic Embed: Open models with strong performance, including versions tuned for code.
    • Qwen3 Embeddings (Alibaba): Large, well-supported models backed by enterprise-scale R&D.
    • EmbeddingGemma (Google/Hugging Face): Smaller, efficient models ideal for resource-constrained environments.

Embeddings in Hybrid AI

A growing number of companies are adopting hybrid AI architectures—systems that combine local, secure components with external cloud-based services. Here’s how embeddings fit in:

    1. Data stays local: Sensitive text (customer records, financial data, IP) is never sent to outside APIs.
    2. Local embeddings: Open-source models like BGE or E5 generate embeddings on-prem or in a private cloud.
    3. Vector storage: Tools like PostgreSQL + pgvector keep embeddings and source data together securely.
    4. Semantic search: Queries run locally to find the most relevant documents.
    5. Cloud inference: Only summarized context (not raw sensitive data) is sent to external providers like Together AI, Baseten, or Fireworks for generative tasks.

This setup gives companies the best of both worlds: secure handling of sensitive data and access to powerful cloud-based LLMs when needed.

What’s Next

Embedding models may not be glamorous, but they’re indispensable. They enable search, recommendations, retrieval, and context, all of which generative AI depends on. Open-source options make these tools more accessible, affordable, and customizable than ever.

As hybrid architectures become standard, open-source embeddings will play a central role in how organizations build secure, scalable AI systems. The future of AI isn’t just about bigger models, it’s also about the building blocks that quietly make them useful.

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