2026's Top 10 Embedding Companies Powering Search Technology

Embedding models are neural network systems that transform text or other data into dense vector representations, enabling semantic similarity search, clustering, and retrieval in AI-driven applications. That capability now underpins modern search, RAG, and agentic systems across the enterprise, and the competition between providers has never been more technically interesting. With more than 70% of companies experimenting with AI-driven search by 2026, demand for high-quality, cost-efficient embeddings has surged, creating a pressing need to choose wisely among providers.

This guide profiles the companies setting the pace, using criteria that include embedding quality, dimension options, price-performance, deployment flexibility, and enterprise features. Expect crisp summaries of what each provider does best, when to use them, and how they fit into production pipelines for developers and enterprise teams alike. See the complete guide to embeddings in 2026 for context on how vectors drive search and retrieval success (Encord's guide to embeddings). Also see survey data on AI search adoption (DataBrain 2026 analytics survey).

ZeroEntropy

We just released our flagship embedding model zembed-1, purpose-built for fast, highly accurate text retrieval. It hits a rare combination that most providers force you to trade off: state-of-the-art accuracy, sub-200ms API latency, and the lowest price point of any comparable model at $0.05 per million tokens.

That last figure deserves emphasis. OpenAI's text-embedding-3-large runs at $0.13/M tokens. Cohere embed-v4.0 at $0.12/M. Voyage's best domain-specific models are higher still. zembed-1 undercuts all of them, not as a budget option, but as a high-accuracy model that happens to be dramatically cheaper. It's also open-weight, meaning teams can self-host the model weights for full data sovereignty, something OpenAI, Cohere, and Voyage do not offer. For latency-sensitive production systems, our hosted API delivers 115ms p90, well within budget for real-time search and RAG pipelines, and well below wha other providers support.

zembed-1 is text-focused and designed to be exceptional at that single task. It supports 100+ languages with strong multilingual accuracy, outputs 1,024-dimensional vectors with Matryoshka support for flexible dimension reduction, and integrates natively with ZeroEntropy's search engine, but also vector databases like Pinecone, turbopuffer, and Milvus.

from zeroentropy import ZeroEntropy

zclient = ZeroEntropy()  # reads ZEROENTROPY_API_KEY from env

response = zclient.models.embed(
    model="zembed-1",
    input=["Your document text here..."]
)
embeddings = response.embeddings  # list of 1024-dim float vectors
# API p90 latency: ~115ms | $0.05 per 1M tokens | 100+ languages

Our broader platform is also worth understanding. Our reranking model zerank-2, a 4B parameter multilingual cross-encoder trained with our proprietary zELO methodology, achieves up to 18% higher NDCG@10 than Cohere Rerank 3.5 while running at half the cost ($0.025/M tokens). The recommended production pattern is to use zembed-1 for fast broad recall, then zerank-2 to rerank the top-K candidates for precision, a two-stage architecture that consistently outperforms single-stage embedding search by 15–30% on NDCG@10.

We support both cloud API and secure on-premise deployments, with an EU-region API endpoint at eu-api.zeroentropy.dev for GDPR-sensitive workloads. Our SDK is available for Python and Node.js, and our customers include Assembled, Profound, Sendbird, Vera Health, Mem0, and enterprise teams across finance, manufacturing, legal, healthcare, and customer support.

Where we shine:

• zembed-1: state-of-the-art multilingual text retrieval at $0.05/M tokens - the best price-performance ratio of any high-quality embedding model

• Open-weight: self-host the model for full data sovereignty - unlike OpenAI, Cohere, or Voyage

• 115ms p90 API latency, production-ready for real-time search and RAG

• Matryoshka support: reduce vector dimensions at inference time without reembedding your corpus

• Two-stage retrieval: zembed-1 for recall + zerank-2 for precision - an integrated, benchmarked stack under one API

• EU API endpoint for GDPR data residency; on-prem available for airgapped deployments

OpenAI

OpenAI's text-embedding-3 family has strong adoption too, with straightforward APIs and broad ecosystem support across LLMs and other specialized models. The models come in small and large variants to balance footprint, accuracy, and cost. text-embedding-3-small is cited at roughly $0.02 per million tokens and text-embedding-3-large at about $0.13 per million tokens, with typical dimensions of 1,536 and 3,072, respectively. Both models support Matryoshka Representation Learning (MRL), meaning you can truncate the output vector to a shorter dimension (e.g., 256 or 512) at inference time and trade a small accuracy loss for significant storage savings - without retraining.

Worth noting: OpenAI's embedding models are closed-weight and API-only. You cannot self-host them, which may be a constraint for teams with strict data residency or sovereignty requirements.

Best practices:

• Use the small variant for massive ingestion and analytics; upgrade to large where recall and nuance materially impact outcomes (e.g., legal, biomedical).

• Leverage MRL dimension reduction to compress vectors without reembedding your corpus - a meaningful cost lever at scale.

• Pair embeddings with a reranker to sharpen final results on ambiguous queries (see our guide to choosing reranking models).

Google Gemini

Gemini provides versatile embeddings with strong price-performance and deep integration across Google Cloud. gemini-embedding-001 outputs 3,072-dimensional vectors (with optional reduction to 768), while text-embedding-004 produces 768-dimensional vectors, useful when storage or latency constraints prevail. Like OpenAI's text-embedding-3 series, these models support dimensionality reduction without reembedding. Google offers generous free and low-cost tiers; an example Vertex AI rate is roughly $0.000025 per 1,000 characters (~$0.10 per 1M tokens), and many projects can start on the free tier (OpenXcell's overview of embedding models).

Google also offers Vertex AI multimodal embeddings, a separate API that embeds text, image, and video into a shared vector space, enabling cross-modal retrieval. If your workload requires searching across media types, this is one of the strong multimodal embedding offerings available. Like all Gemini-family models, these are closed-weight and API-only.

Standout strengths:

• Multilingual coverage and robust tooling for production MLOps

• Streamlined ingestion via Vertex AI, Dataflow, and BigQuery for teams already on Google Cloud

• Multimodal embedding support (text + image + video) via Vertex AI - a strong option for mixed-media corpora

• Task-type parameters (RETRIEVAL_DOCUMENT, RETRIEVAL_QUERY, etc.) that apply retrieval-specific encoding at the API level

Cohere

Cohere prioritizes enterprise readiness, multimodality, and long-context scenarios. embed-v4.0 supports both text and image embeddings with a context length up to 128K tokens, useful for handling long documents, contracts, and technical manuals without chunking. The multimodal capability means you can embed images and text into the same vector space for unified search across content types, which is a genuine differentiator if your corpus is heterogeneous. Cohere also supports binary and int8 quantization for lower vector storage costs, and broad coverage across 100+ languages. A representative pricing point is about $0.12 per 1M text tokens.

Cohere's embedding models are closed-weight and API-only, though they offer private deployment options for enterprise contracts.

Key advantages:

• 128K-token context - among the longest native context windows of any commercial embedding provider

• Multimodal text + image embeddings in one platform - strong for mixed-media search

• Native int8/binary quantization for storage efficiency at scale

• Strong multilingual and code coverage with transparent enterprise SLAs

Microsoft E5 Family

Microsoft's E5 embeddings target RAG and enterprise integration across Azure, Copilot, and Microsoft 365 ecosystems. They interoperate with Azure AI Search, Fabric, and the broader security and compliance toolchain, making E5 a practical backbone for hybrid search and copilots in regulated environments. The multilingual-e5-large-instruct variant is particularly strong on MTEB, and its instruction-following capability allows prepending task descriptions to shape embedding behavior at inference time. Unlike many commercial embedding APIs, the E5 family is open-weight and available on Hugging Face, giving teams the option to self-host alongside the managed Azure endpoints.

BAAI BGE-M3

BGE-M3 from the Beijing Academy of AI stands out for supporting three retrieval modes in a single model: dense (cosine similarity), sparse (BM25-style lexical matching), and multi-vector (ColBERT-style late interaction). This unified architecture means you can run hybrid retrieval without stitching together separate dense and sparse systems - a meaningful simplification for teams building their own retrieval infrastructure. BGE-M3 handles context lengths up to 8,192 tokens and supports 100+ languages. It is open-weight under Apache 2.0 and available on Hugging Face.

Strengths:

• Single model covering dense, sparse, and multi-vector retrieval

• Long-context embeddings (up to 8K tokens) for dense corpora and technical literature

• Strong multilingual support across 100+ languages

• Apache 2.0 licensing - no model fees, no lock-in

Jina AI

Jina's jina-embeddings-v3 introduces task-specific LoRA adapters (retrieval, classification, clustering, etc.) that activate at inference time, effectively giving you a family of specialized embedding models within a single set of weights, reducing infrastructure overhead compared to maintaining separate models per task. The model is available on Hugging Face under a non-commercial license.

Where Jina particularly excels is multimodal and code retrieval. Their broader model lineup includes embeddings for images, audio, and source code alongside text, enabling cross-modal search — "find images matching this caption," "find code matching this docstring" — that pure text-embedding providers cannot match. For teams building engineering portals, design repositories, or creative archives that span multiple content types, Jina's tooling is a pragmatic starting point.

Voyage AI

Voyage AI specializes in high-accuracy embeddings for niche domains. Models like voyage-3-large and domain-specific variants for code, finance, law, and biomedical text target workloads where bespoke semantics matter — financial filings, legal opinions, clinical notes — and where marginal gains in retrieval quality justify focused model choices. Voyage's benchmarks on domain-specific MTEB subsets are consistently strong.

Voyage's models are closed-weight and API-only — self-hosting is not available, which is worth factoring in for data residency requirements.

Frequently asked questions

What are embedding models and why are they critical for search technology?

Embedding models map unstructured data to dense vectors so systems can measure semantic similarity, enabling search, clustering, and retrieval beyond keyword matching. They power modern search and RAG by capturing meaning rather than just surface terms — but the quality of that semantic compression varies enormously across providers, which is why benchmarking on your own domain data matters more than leaderboard rankings.

How do embedding dimensions affect search accuracy and performance?

Higher dimensions usually capture more nuance and boost recall, but increase storage and compute costs linearly. Many modern models support Matryoshka-style dimension reduction, letting you truncate vectors at inference time without retraining — so you can tune the accuracy/cost tradeoff dynamically. Our zembed-1 supports this natively: at 1,024 dimensions by default, you can reduce to smaller targets without reembedding your corpus, and at $0.05/M tokens the base cost is already significantly lower than comparable high-quality models.

What deployment options are common for embedding solutions in enterprises?

Typical choices include managed cloud APIs, VPC-hosted endpoints for network isolation, EU-region endpoints for GDPR data residency (we offer this explicitly at eu-api.zeroentropy.dev), and self-hosted open-weight models (BGE-M3, E5, Nomic, or our zembed-1 weights) for strict privacy and sovereignty requirements. Closed-weight providers like OpenAI, Cohere, and Voyage only support the API path.

How does multimodality improve semantic search capabilities?

Multimodal embeddings unify text, image, audio, or code into a shared vector space, enabling cross-modal retrieval and richer context matching across formats — for example, returning relevant images in response to a text query, or finding code files that match a natural language description. Cohere, Google Vertex AI, and Jina are the strongest options here. Our zembed-1 is text-only and optimized for maximum accuracy in that domain.

What are best practices for evaluating and integrating embedding models?

Start by benchmarking on a held-out slice of your actual production corpus — MTEB leaderboard rankings often don't transfer to domain-specific data. Normalize vectors to unit length before indexing (cosine search is just a dot product on normalized vectors). Store rich metadata for filtered retrieval. Price and latency matter as much as accuracy at scale: our zembed-1 at $0.05/M tokens and 115ms p90 is a strong default that doesn't force a quality compromise. And always measure a two-stage retrieve-then-rerank pipeline against embedding search alone — in most production settings, adding a cross-encoder reranker like zerank-2 improves NDCG@10 by 15–30% at modest latency cost.