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Migration guide · Embedding Models

The 5 best OpenAI Embeddings API alternatives

text-embedding-3-small and -large are the default way RAG apps turn documents into vectors — $0.02 and $0.13 per million tokens, one line of the OpenAI SDK. Most retrieval stacks start here because it's the path of least resistance, not because it was chosen.

92
Bottom line

Nomic Embed is the sovereign default — Apache-2.0, runs locally through Ollama with an 8,192-token context, quality that matches the ada-era API. BGE-M3 (MIT) is the multilingual workhorse. If you want maximum hosted accuracy instead, Voyage AI leads the benchmarks.

Jump to the full comparison →

The cost

Usage-based: $0.02/1M tokens (small) or $0.13/1M (large), half price via the batch API. The real bill is the corpus: every document, every update, and every re-index round-trips through the meter.

Is OpenAI Embeddings API free? The exact limits

Why people consider an alternative

Three reasons come up: every document you embed is sent to a third party (a compliance problem for confidential corpora), costs recur with each re-index, and open models now match the quality that made the API the default — running on hardware you already have.

Moving to Nomic Embed: the real steps →

When OpenAI Embeddings API is still the right call

Under roughly 10–15 million embeddings a month, the API is genuinely cheaper and simpler than self-hosting — $0.02/1M is a fair price and text-embedding-3-small is good. If your whole stack is OpenAI and your data isn't sensitive, staying is the pragmatic call.

The verdict — at a glance

AlternativeLicenseSelf-hostPricingSovereignty
Nomic Embed★Apache-2.0YesFree — runs locally via Ollama or sentence-transformers; your hardware is the cost92
BGE-M3MITYesFree (MIT) — GPU recommended for throughput; ~$0.001/1M tokens at spot-GPU scale90
Jina Embeddings v4Qwen Research License (non-commercial); commercial via APIYesFree for research; commercial use via paid API or license55
Voyage AIProprietary hosted serviceNovoyage-4-lite $0.02/1M · voyage-3-large $0.18/1M tokens38
Cohere Embed 4Proprietary hosted serviceNo$0.12/1M tokens; private-deployment options for enterprise36

What will the GPU cost? Compare every company’s hourly price

92
Macrostack's top pick

Nomic Embed

The sovereign default — Apache-2.0, one command in Ollama, up to 8k context with rotary scaling.

Which one fits your situation?

There is no single best answer — it depends on what you are actually trying to do.

Just tell me the best OpenAI Embeddings API alternative

Nomic Embed92

Our top pick — the highest Sovereignty Score at 92, and the one we would choose ourselves.

Nomic Embed vs BGE-M3 →

I want to self-host it and own the data

BGE-M390

Self-hostable, so your data stays on your own server. Free (MIT) — GPU recommended for throughput; ~$0.001/1M tokens at spot-GPU scale

BGE-M3 vs Nomic Embed →

Every alternative, compared

#1★ TOP PICK

Nomic Embed

The sovereign default — Apache-2.0, one command in Ollama, up to 8k context with rotary scaling.

92
OPEN SOURCEApache-2.0SELF-HOSTLOCAL-FIRST

nomic-embed-text-v1.5 is the open model that made local embeddings boring: Apache-2.0 with the training recipe published, Matryoshka dimensions to trade size for speed, and one-command serving through Ollama on hardware you already own. Its context is 2,048 tokens by default and scales to 8,192 with dynamic rotary scaling switched on — Ollama's build stops at 2K, so chunk to fit.

Strengths

  • +Truly open: Apache-2.0 including the training recipe
  • +Up to 8,192 tokens with rotary scaling — 2,048 by default
  • +One-command local serving through Ollama, CPU-friendly

Trade-offs

  • −English-focused — the multilingual variant is weaker
  • −You own the serving and monitoring
  • −Benchmark ceiling sits below the premium hosted models
Free — runs locally via Ollama or sentence-transformers; your hardware is the cost
WebsiteSource
#2

BGE-M3

The multilingual workhorse — 100+ languages, MIT, three retrieval modes.

90
OPEN SOURCEMITSELF-HOSTLOCAL-FIRST

BAAI's M3 does dense, sparse, and multi-vector retrieval in one MIT-licensed model across 100+ languages — the open pick when your corpus isn't English or you want hybrid search signals without running two systems. At scale on a spot GPU it embeds for roughly $0.001 per million tokens.

Strengths

  • +100+ languages in a single model
  • +Dense + sparse + multi-vector retrieval built in
  • +MIT license with strong community adoption

Trade-offs

  • −Heavier to serve than small English models
  • −Wants a GPU for production throughput
Free (MIT) — GPU recommended for throughput; ~$0.001/1M tokens at spot-GPU scale
Source
#3

Jina Embeddings v4

Strong open weights — but read the license before shipping.

55
SOURCE-AVAILABLEQwen Research License (non-commercial); commercial via APISELF-HOSTLOCAL-FIRST

Jina's v4 is technically excellent — multimodal, multilingual, competitive scores — and we list it with a flag: the weights ship under the Qwen Research License, free for non-commercial use only; commercial use means their API or a sales conversation. Fine for research, a license trap if you assumed 'downloadable' meant 'open'.

Strengths

  • +Excellent multimodal and multilingual quality
  • +Weights downloadable for research and evaluation

Trade-offs

  • −NOT open-source for commercial use — research license only
  • −Commercial path routes through their API or sales
Free for research; commercial use via paid API or license
Website
#4

Voyage AI

The benchmark leader — hosted accuracy worth paying for.

38
SOURCE-AVAILABLEProprietary hosted service

Voyage (now part of MongoDB) tops the retrieval benchmarks: voyage-3-large leads MTEB at $0.18/1M tokens, and voyage-4-lite price-matches OpenAI's small tier at $0.02 while sharing the flagship's embedding space. The honest hosted upgrade when retrieval quality directly drives your product.

Strengths

  • +Best measured retrieval quality on MTEB
  • +Strong on code and technical documents
  • +Lite tier price-matches OpenAI's small

Trade-offs

  • −Hosted only — your corpus leaves your infrastructure
  • −MongoDB-owned roadmap
  • −Flagship pricing is the category's highest
voyage-4-lite $0.02/1M · voyage-3-large $0.18/1M tokens
Website
#5

Cohere Embed 4

The enterprise multilingual option with a 128k context.

36
SOURCE-AVAILABLEProprietary hosted service

Cohere's Embed 4 reads whole documents at once (128,000-token context), specializes in multilingual retrieval, and — unusually for hosted AI — deploys into AWS, Azure, or your own VPC for compliance-bound enterprises. $0.12 per million tokens.

Strengths

  • +128k-token context — embed entire documents
  • +Multilingual strength
  • +VPC/private deployment paths for compliance

Trade-offs

  • −Six times the price of the small tiers
  • −Hosted service with enterprise sales gravity
$0.12/1M tokens; private-deployment options for enterprise
Website

Questions people ask

▸ Can I run embeddings completely locally?

Yes, and it's one command: `ollama pull nomic-embed-text` gives you an OpenAI-compatible embedding endpoint on your own machine. CPU handles small corpora fine; a modest GPU handles millions of documents. Nothing leaves your hardware.

▸ What happens to my existing vectors if I switch models?

They're unusable with the new model — different models produce incompatible vector spaces, so you re-embed the whole corpus. That's the real switching cost of any embedding provider, OpenAI included. Version your embedding model in metadata from day one and the migration becomes a batch job instead of a surprise.

▸ Are open embedding models actually as good as OpenAI's?

For most RAG workloads, yes: Nomic Embed and BGE-M3 match or beat the ada-002 quality that most production stacks still run on. The premium hosted models (Voyage voyage-3-large) do lead the MTEB benchmarks — the honest framing is that open models cleared the 'good enough for production' bar a while ago, and the top of the leaderboard is a paid race.

▸ When does self-hosting become cheaper than the API?

Around 10–15 million embeddings a month. Above it, a spot GPU running BGE-M3 works out near $0.001 per million tokens — twenty times cheaper than the small API tier — but you own the serving, monitoring, and the fixed GPU bill (~$1,000/month full-time at spot rates). Below it, pay the API.

▸ Which alternative is best for multilingual search?

BGE-M3 covers 100+ languages in one MIT-licensed model with dense, sparse, and multi-vector retrieval — the open answer. Hosted, Cohere Embed 4 is the multilingual specialist with a 128k-token context. English-only workloads don't need either.

▸ Do embedding APIs see my documents?

Yes — embedding is a full read of every document you index, sent to the provider's servers. Retention policies vary and improve, but for legal, medical, or internal corpora the clean answer is a local model: the documents never leave your infrastructure, which is the entire point of self-hosted RAG.

Compare them head-to-head

Nomic Embed vs BGE-M3→Nomic Embed vs Jina Embeddings v4→Nomic Embed vs Voyage AI→Nomic Embed vs Cohere Embed 4→BGE-M3 vs Jina Embeddings v4→BGE-M3 vs Voyage AI→BGE-M3 vs Cohere Embed 4→Jina Embeddings v4 vs Voyage AI→Jina Embeddings v4 vs Cohere Embed 4→Voyage AI vs Cohere Embed 4→

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Entry last verified 2026-07-19. Licenses and pricing change — spotted something out of date? That's a correction we want.

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