# FastEmbed vs Jina Embeddings

**Leader by vioscaleAI score:** FastEmbed

| Attribute | FastEmbed | Jina Embeddings |
|---|---|---|
| **vioscaleAI score** | 56.9 (54% (medium)) | 50.4 (35% (low)) |
| activity.commits_last_30d | 71 | - |
| adoption.dependent_repos | 4 | - |
| adoption.github_stars | 3,244 | - |
| content.faq | `[{"answer":"A Python library that creates vector embeddings from text and images using efficient, quantized model weights optimized for speed and local inference. Supports multiple embedding architectures including multilingual models and outperforms OpenAI's Ada-002 in accuracy. It is indexed under Embeddings Apis.","source":"https://qdrant.github.io/fastembed/","question":"What is FastEmbed?","confidence":0.6},{"answer":"Yes. FastEmbed can be deployed on-premise and self-hosted, so it does not have to run on the vendor's infrastructure.","source":"https://qdrant.github.io/fastembed/","question":"Can FastEmbed be self-hosted?","confidence":0.6},{"answer":"Yes. FastEmbed is published under the Apache-2.0 licence, a permissive licence that generally allows commercial use and modification. Licence terms can change between releases, so verify against the repository for the version you intend to use.","source":"https://github.com/qdrant/fastembed","question":"Is FastEmbed open source?","confidence":0.95}]` | `[{"answer":"A multi-modal embedding API supporting text, images, audio, and video with support for multiple languages, long context windows, and flexible deployment options including cloud APIs and on-premises infrastructure. It is indexed under Embeddings Apis.","source":"https://jina.ai/embeddings","question":"What is Jina Embeddings?","confidence":0.6},{"answer":"Jina Embeddings offers a free tier, so you can start without paying. Pricing changes often, so verify at source before relying on it.","source":"https://jina.ai/embeddings","question":"Is Jina Embeddings free?","confidence":0.6},{"answer":"Jina Embeddings supports the web and a command-line interface. Platforms we have not confirmed are simply not listed here rather than ruled out.","source":"https://jina.ai/embeddings","question":"What platforms does Jina Embeddings support?","confidence":0.6},{"answer":"Yes. Jina Embeddings can be deployed cloud / SaaS, on-premise, air-gapped and self-hosted, so it does not have to run on the vendor's infrastructure.","source":"https://jina.ai/embeddings","question":"Can Jina Embeddings be self-hosted?","confidence":0.6},{"answer":"We have confirmed 6 integrations for Jina Embeddings, including Claude, GPT-4o, Milvus, Elastic, Llama.cpp and MLX. This is what we could verify from public sources, so the vendor may support others we have not indexed.","source":"https://jina.ai/embeddings","question":"What does Jina Embeddings integrate with?","confidence":0.6}]` |
| deployment.options | `{"cloud":true,"self_hosted":true}` | `{"cloud":true,"on_prem":true,"air_gapped":true,"self_hosted":true}` |
| description.long | FastEmbed is a Python library that generates text and image embeddings using ONNX Runtime, eliminating the need for large PyTorch dependencies. It supports various embedding models including sparse embeddings, late-interaction models, and cross-encoders, making it suitable for serverless and resource-constrained environments. | Embedding models that process multiple data types in a shared vector space, supporting deployment options from managed cloud APIs to self-hosted and on-premises infrastructure. Available in multiple model sizes optimized for different performance and resource requirements. |
| features.capabilities | `{"pricing_model":"free_open_weights","architecture_model":"open_weights_local","cross_encoder_reranker_api_offered":true,"mit_or_apache_permissive_oss_license":true,"late_interaction_architecture_colbert_v2":true,"multi_modal_image_and_text_joint_embeddings":true,"massive_context_window_greater_than_8k_tokens":true,"multilingual_support_across_100_plus_languages":true,"binary_and_scalar_quantization_for_memory_reduction":true,"task_specific_prefixes_query_vs_document_instructions":true}` | `{"architecture_model":"hybrid","cross_encoder_reranker_api_offered":true,"mit_or_apache_permissive_oss_license":false,"multi_modal_image_and_text_joint_embeddings":true,"massive_context_window_greater_than_8k_tokens":true,"multilingual_support_across_100_plus_languages":false,"zero_data_retention_training_opt_out_enterprise":true,"binary_and_scalar_quantization_for_memory_reduction":true,"task_specific_prefixes_query_vs_document_instructions":true,"matryoshka_representation_learning_for_dimension_truncation":true}` |
| integrations.count | 3 | 6 |
| integrations.list | `[{"name":"Qdrant"},{"name":"Langchain"},{"name":"Llama Index"}]` | `[{"name":"Claude"},{"name":"GPT-4o"},{"name":"Milvus"},{"name":"Elastic"},{"name":"Llama.cpp"},{"name":"MLX"}]` |
| language.primary | Python | - |
| license.spdx | Apache-2.0 | - |
| market.availability | - | `{"primaryMarkets":["US"],"availabilityScope":"global","availableCountries":[],"notAvailableCountries":[]}` |
| platform.support | - | `{"web":true}` |
| pricing | `{"type":"open_source","freeTier":true,"sourceUrl":"https://qdrant.github.io/fastembed/","retrievedAt":"2026-09-13T20:06:10.444Z"}` | `{"type":"hybrid","summary":"Pay-as-you-go API pricing or subscription billing through Elastic Inference Service. Free tier available.","currency":"USD","freeTier":true,"sourceUrl":"https://jina.ai/embeddings","retrievedAt":"2026-10-02T16:15:58.807Z"}` |
| pricing.free_tier | yes | yes |
| pricing.model | open_source | freemium |
| pricing.price_level | free | low |
| pricing.transparent | - | no |
| release.cadence_days | 33 | - |
| release.history | `[{"url":"https://github.com/qdrant/fastembed/releases/tag/v0.9.0","date":"2026-10-07T16:39:40Z","type":"stable","version":"v0.9.0"},{"url":"https://github.com/qdrant/fastembed/releases/tag/v0.8.1","date":"2026-09-22T20:52:04Z","type":"stable","version":"v0.8.1"},{"url":"https://github.com/qdrant/fastembed/releases/tag/v0.8.0","date":"2026-03-23T17:14:03Z","type":"stable","version":"v0.8.0"},{"url":"https://github.com/qdrant/fastembed/releases/tag/v0.7.4","date":"2025-12-05T12:18:02Z","type":"stable","version":"v0.7.4"},{"url":"https://github.com/qdrant/fastembed/releases/tag/v0.7.2","date":"2025-08-25T15:05:56Z","type":"stable","version":"v0.7.2"},{"url":"https://github.com/qdrant/fastembed/releases/tag/v0.7.1","date":"2025-06-16T09:06:43Z","type":"stable","version":"v0.7.1"},{"url":"https://github.com/qdrant/fastembed/releases/tag/v0.7.0","date":"2025-05-13T14:32:19Z","type":"stable","version":"v0.7.0"},{"url":"https://github.com/qdrant/fastembed/releases/tag/v0.6.1","date":"2025-04-10T13:51:36Z","type":"stable","version":"v0.6.1"},{"url":"https://github.com/qdrant/fastembed/releases/tag/v0.6.0","date":"2025-02-26T13:55:32Z","type":"stable","version":"v0.6.0"},{"url":"https://github.com/qdrant/fastembed/releases/tag/v0.5.1","date":"2025-01-20T10:43:31Z","type":"stable","version":"v0.5.1"},{"url":"https://github.com/qdrant/fastembed/releases/tag/v0.5.0","date":"2024-12-24T19:53:25Z","type":"stable","version":"v0.5.0"},{"url":"https://github.com/qdrant/fastembed/releases/tag/v0.4.2","date":"2024-11-13T13:41:59Z","type":"stable","version":"v0.4.2"},{"url":"https://github.com/qdrant/fastembed/releases/tag/v0.4.1","date":"2024-10-21T20:30:12Z","type":"stable","version":"v0.4.1"},{"url":"https://github.com/qdrant/fastembed/releases/tag/v0.4.0","date":"2024-10-21T18:19:56Z","type":"stable","version":"v0.4.0"},{"url":"https://github.com/qdrant/fastembed/releases/tag/v0.3.5","date":"2024-08-23T18:16:09Z","type":"stable","version":"v0.3.5"},{"url":"https://github.com/qdrant/fastembed/releases/tag/v0.3.4","date":"2024-07-17T15:31:05Z","type":"stable","version":"v0.3.4"},{"url":"https://github.com/qdrant/fastembed/releases/tag/v0.3.1","date":"2024-06-17T18:05:55Z","type":"stable","version":"v0.3.1"},{"url":"https://github.com/qdrant/fastembed/releases/tag/v0.3.0","date":"2024-06-05T17:11:17Z","type":"stable","version":"v0.3.0"},{"url":"https://github.com/qdrant/fastembed/releases/tag/v0.2.7","date":"2024-05-03T19:56:28Z","type":"stable","version":"v0.2.7"},{"url":"https://github.com/qdrant/fastembed/releases/tag/v0.2.6","date":"2024-04-01T15:30:37Z","type":"stable","version":"v0.2.6"}]` | - |
| security.vulnerabilities | `{"count":0,"source":"https://advisories.ecosyste.ms/api/v1/advisories?ecosystem=pypi&package_name=fastembed&per_page=100","last_12m":0,"max_severity":null}` | - |

## Capabilities (Embeddings Apis)

| Capability | FastEmbed | Jina Embeddings |
|---|:--:|:--:|
| **Capabilities** |  |  |
| Architecture model | Open weights local | Hybrid |
| Matryoshka representation learning for dimension truncation | - | ✓ |
| Multilingual support across 100 plus languages | ✓ | ✗ |
| Binary and scalar quantization for memory reduction | ✓ | ✓ |
| Cross encoder reranker API offered | ✓ | ✓ |
| Late interaction architecture colbert v2 | ✓ | - |
| Massive context window greater than 8k tokens | ✓ | ✓ |
| Multi modal image and text joint embeddings | ✓ | ✓ |
| Task specific prefixes query vs document instructions | ✓ | ✓ |
| Zero data retention training opt out enterprise | - | ✓ |
| SOC2 type ii | - | - |
| Mit or apache permissive oss license | ✓ | ✗ |
| Pricing model | Free open weights | - |

*Source: vioscaleAI. Generated 2026-10-16T00:38:29.842Z. "-" = undocumented, not absent.*