Which embedder should I choose?

Choosing an embedding model is not just about quality. Cost, indexing speed, search latency, dimensions, and domain specialization all matter. In most cases, a smaller, cheaper model will serve you better than the largest available option.

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Available providers

Meilisearch supports a wide range of embedding providers, each with different models, pricing, and strengths:

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Smaller models are often better

Bigger is not always better. In a hybrid search setup, Meilisearch combines keyword results with semantic results using its smart scoring system. Full-text search already handles exact matches very well, so the semantic side only needs to capture general meaning, not every nuance. This means a small, fast embedding model is often enough. The quality difference between a 384-dimension model and a 3072-dimension model is rarely worth the extra cost and latency, especially when the keyword side is already covering precise queries. Prioritize cheaper, faster models unless you have a specific reason to need more dimensions or higher embedding quality. Models like text-embedding-3-small, voyage-4-lite, jina-embeddings-v5-text-nano, or embed-english-light-v3.0 are excellent starting points.

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What to look for

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Cost and rate limits

Embedding providers charge per token or per request. For large datasets, embedding costs add up during indexing. Consider:

• Free tiers: Cloudflare Workers AI and local HuggingFace models have no per-request cost
• Rate limits: free-tier accounts on paid providers may slow down indexing significantly. Meilisearch handles retries automatically, but higher tiers index faster
• Re-indexing: Meilisearch caches embeddings and only re-generates them when document content changes, reducing ongoing costs

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Dimensions

Lower-dimension models are faster to index, use less memory, and produce faster searches. Higher dimensions can capture more semantic nuance but with diminishing returns.

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Domain specialization

Some providers offer models specialized for specific domains:

• Legal, medical, financial: check if your provider has domain-specific models or fine-tuned variants
• Multilingual: if your content is not in English, choose a model with explicit multilingual support (Cohere’s multilingual models, Jina v3/v5, or multilingual BGE models)
• Code: some models are optimized for code search

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Indexing speed

Embedding generation is the main bottleneck during indexing. Two factors affect speed:

• API latency: cloud providers add network round-trip time per batch. Providers with edge networks (Cloudflare) or regional endpoints (Bedrock) can be faster
• Model size: larger models take longer to compute embeddings, even on the provider side

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Maximize performance with composite embedders

If you need the best possible indexing speed and search latency, consider using a composite embedder. This lets you use different models for indexing and search:

• Indexing: use a cloud provider (Cloudflare Workers AI, HuggingFace Inference Endpoints, or any REST API) to generate high-quality embeddings at scale without impacting your Meilisearch server
• Search: use a local HuggingFace model (like BAAI/bge-small-en-v1.5) running inside Meilisearch for near-instant query embedding with zero API latency

This combination gives you the throughput of a cloud API for indexing with the speed of a local model for search. Both models must produce embeddings with the same number of dimensions.

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User-provided embeddings

If you work with non-textual content (images, audio) or already generate embeddings in your pipeline, you can supply pre-computed vectors directly. See search with user-provided embeddings.

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Decision flowchart