How do I replace OpenAI embeddings with a cheaper alternative?

Use EmbeddingAdapters. Send your texts to the API with model=qwen06b-te3-adapted and get back 3072-d vectors compatible with openai/text-embedding-3-large at $0.04/1M tokens (69% cheaper). Works with Pinecone, Weaviate, Qdrant, pgvector — no re-indexing needed.

Can I search a Pinecone index built with OpenAI using a different model?

Yes. EmbeddingAdapters translates embeddings from models like Qwen3-0.6B or MiniLM into openai/text-embedding-3-large vector space. Your Pinecone index stays untouched. The adapter translates the query at search time.

How accurate are embedding adapters compared to OpenAI?

In benchmarks, the adapter produces the same top-3 ranked results as openai/text-embedding-3-large on retrieval tasks. MRR@10 of 0.934 (vs OpenAI's 0.960) at quality=0 with zero API calls. Quality routing can match OpenAI exactly.

How do I run embedding adapters locally?

pip install embedding-adapters, then use EmbeddingAdapter.from_registry() to load a v2 adapter. Runs on your GPU at 18,000 tok/s. $10/month subscription for v2 adapters targeting openai/text-embedding-3-large.

What is the EmbeddingAdapters API pricing?

qwen06b-te3-adapted: $0.04/1M tokens (69% cheaper than OpenAI). minilm-te3-adapted: $0.065/1M tokens (50% cheaper). OpenAI text-embedding-3-large costs $0.130/1M tokens. Free tier includes 10,000 tokens.

How do I migrate from OpenAI embeddings to open source?

Use the te3-qwen3-8b-adapted reverse adapter. Send your existing openai/text-embedding-3-large vectors and get back Qwen3-Embedding-8B compatible vectors. Migrate your entire index to open source without re-embedding.

Which embedding models does EmbeddingAdapters support?

Source models: Qwen/Qwen3-Embedding-0.6B, sentence-transformers/all-MiniLM-L6-v2, intfloat/e5-base-v2, gemini/text-embedding-004, openai/text-embedding-3-small. Target models: openai/text-embedding-3-large (3072d), openai/text-embedding-3-small (1536d), Qwen/Qwen3-Embedding-8B (4096d), intfloat/e5-base-v2 (768d), gemini/text-embedding-004 (768d). 16 adapter pairs total.

What is embedding quality routing?

Quality routing scores each query's translation confidence. Low-confidence queries get routed to the provider for a native embedding. Set quality=0 for all-local (cheapest), quality=50 for balanced, or quality=100 for all-provider. The adapter's built-in quality head makes this decision per-text.

~98–99% lower embedding cost · Provider-grade retrieval · Local-first

Generate OpenAI-compatible embeddings locally

Name: EmbeddingAdapters
Author: EmbeddingAdapters

200× faster. 70× cheaper.

Query across embedding spaces

Universal embedding-space translation library. Plug-and-play adapters that map one model's vector space into another — locally, instantly, for free. Learn more →

Get API key See benchmarks →

Adapter

Try it — generate openai/text-embedding-3-large from Qwen3-Embedding-0.6B

Same ranking as OpenAI. 69% cheaper. See how the adapter compares to the raw model below.

—

Dimensions

—

Latency

—

Per-query cost

—

Mode

[ ... ]

Your e-commerce help center is indexed with openai/text-embedding-3-large. A customer searches it.

The adapter finds the right answer. The raw model doesn't.

Indexed help articles (8 docs in Pinecone)

30-day return policy Refund after inspection Exchanges & sizing Package tracking Credit card cash back Cancel payment Store credit Free returns (damaged)

Customer search query

—

EmbeddingAdapters

qwen06b-te3-adapted

openai/text-embedding-3-large

direct — $0.130/1M

Qwen3-0.6B raw

no adapter

—

EmbeddingAdapters cost

—

per 1M tokens

openai/text-embedding-3-large direct

$0.130

per 1M tokens

Adapters typically recover >90% of the target model's retrieval accuracy

Embedding adapters are lightweight neural networks trained to translate one model's vector space into another. On high-confidence queries, the adapted embedding performs similarly to the target — recovering over 90% of its retrieval accuracy with zero API calls.

But not every query translates equally well. Some texts are harder to map across embedding spaces. That's where confidence routing comes in.

Source model

~82%

→

Adapter

~93%

→

Target model

96%

✓ Best case (most queries)

Retrieval matches the target model. Cost: $0 — everything stays local.

⚠ Worst case (edge cases)

Low-confidence queries route to the provider for a native embedding. Cost: one API call — only when needed.

Each query is scored individually. You control the confidence threshold — higher routes more queries to the provider for guaranteed accuracy, lower keeps more local for maximum savings.

The calibrate endpoint analyzes your data and recommends the optimal setting, so you never degrade below your baseline.

Proven on real retrieval benchmarks

Tested on HotpotQA (multi-hop reasoning) and Natural Questions (factoid Q&A). Adapted queries search a corpus embedded with OpenAI text-embedding-3-large — the same setup you'd use in production.

HotpotQA — quality routing closes the gap to OpenAI

Natural Questions — 0.97 R@10 vs OpenAI's 0.98

See full benchmark results →

⚡

Embed millions of documents in minutes

18,000 tokens/second on a single GPU. Process your entire corpus locally without waiting on API rate limits or paying per-token.

🎯

Use confidence scores for intelligent routing

Every text gets a quality score. High-confidence embeddings stay local. Low-confidence ones route to the provider. You control the threshold per-request.

🧠

Train your own adapters for better retrieval

Base adapters not perfect for your domain? Create a custom LoRA that learns from provider fallbacks. Accuracy improves over time, routing costs drop.

Generate OpenAI-compatible embeddings locally

Try it — generate openai/text-embedding-3-large from Qwen3-Embedding-0.6B

Adapters typically recover >90% of the target model's retrieval accuracy

Stop paying per-query for embeddings

Get in touch