# BAAI FlagEmbedding vs FastEmbed

**Leader by vioscaleAI score:** FastEmbed

| Attribute | BAAI FlagEmbedding | FastEmbed |
|---|---|---|
| **vioscaleAI score** | 48 (45% (low)) | 56.9 (54% (medium)) |
| activity.commits_last_30d | 13 | 71 |
| adoption.dependent_repos | 1 | 4 |
| adoption.github_stars | 12,203 | 3,244 |
| content.faq | `[{"answer":"A library of state-of-the-art embedding models supporting dense, lexical, and cross-encoder retrieval methods for text and image search, available as free and open-source software. It is indexed under Embeddings Apis.","source":"https://github.com/pricing","question":"What is BAAI FlagEmbedding?","confidence":0.6},{"answer":"BAAI FlagEmbedding is open source, so it can be self-hosted and used at no licence cost. It is released under the MIT licence. Pricing changes often, so verify at source before relying on it.","source":"https://github.com/pricing","question":"Is BAAI FlagEmbedding free to use?","confidence":0.6},{"answer":"BAAI FlagEmbedding supports the web and a command-line interface. Platforms we have not confirmed are simply not listed here rather than ruled out.","source":"https://github.com/pricing","question":"What platforms does BAAI FlagEmbedding support?","confidence":0.6},{"answer":"Yes. BAAI FlagEmbedding can be deployed cloud / SaaS, on-premise and self-hosted, so it does not have to run on the vendor's infrastructure.","source":"https://github.com/pricing","question":"Can BAAI FlagEmbedding be self-hosted?","confidence":0.6},{"answer":"Yes. BAAI FlagEmbedding is published under the MIT 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/FlagOpen/FlagEmbedding","question":"Is BAAI FlagEmbedding open source?","confidence":0.95}]` | `[{"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}]` |
| deployment.options | `{"cloud":true,"on_prem":true,"self_hosted":true}` | `{"cloud":true,"self_hosted":true}` |
| description.long | A library of state-of-the-art embedding models supporting dense, lexical, and cross-encoder retrieval methods for text and image search, available as free and open-source software. | 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. |
| 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}` | `{"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}` |
| integrations.count | - | 3 |
| integrations.list | - | `[{"name":"Qdrant"},{"name":"Langchain"},{"name":"Llama Index"}]` |
| language.primary | Python | Python |
| license.spdx | MIT | Apache-2.0 |
| platform.support | `{"cli":true,"web":true}` | - |
| pricing | `{"type":"open_source","summary":"Free and open source under MIT license","freeTier":true,"sourceUrl":"https://github.com/pricing","retrievedAt":"2026-08-21T11:06:33.625Z"}` | `{"type":"open_source","freeTier":true,"sourceUrl":"https://qdrant.github.io/fastembed/","retrievedAt":"2026-09-13T20:06:10.444Z"}` |
| pricing.free_tier | yes | yes |
| pricing.model | commercial | open_source |
| pricing.price_level | free | free |
| pricing.transparent | yes | - |
| release.cadence_days | 104 | 33 |
| release.history | `[{"url":"https://github.com/FlagOpen/FlagEmbedding/releases/tag/v1.4.2","date":"2026-08-24T02:55:16Z","type":"stable","version":"v1.4.2"},{"url":"https://github.com/FlagOpen/FlagEmbedding/releases/tag/v1.4.1","date":"2026-08-23T12:48:07Z","type":"stable","version":"v1.4.1"},{"url":"https://github.com/FlagOpen/FlagEmbedding/releases/tag/v1.4.0","date":"2026-04-22T15:59:10Z","type":"stable","version":"v1.4.0"},{"url":"https://github.com/FlagOpen/FlagEmbedding/releases/tag/v1.3.5","date":"2025-05-28T07:14:20Z","type":"stable","version":"v1.3.5"},{"url":"https://github.com/FlagOpen/FlagEmbedding/releases/tag/v1.3.4","date":"2025-02-07T14:14:41Z","type":"stable","version":"v1.3.4"},{"url":"https://github.com/FlagOpen/FlagEmbedding/releases/tag/v1.3.2-BGE-Update","date":"2024-10-31T16:23:48Z","type":"stable","version":"v1.3.2-BGE-Update"},{"url":"https://github.com/FlagOpen/FlagEmbedding/releases/tag/BGE-M3%26Beacon","date":"2024-02-02T05:57:07Z","type":"stable","version":"BGE-M3&Beacon"},{"url":"https://github.com/FlagOpen/FlagEmbedding/releases/tag/lm-cocktail","date":"2023-11-24T09:22:50Z","type":"stable","version":"lm-cocktail"},{"url":"https://github.com/FlagOpen/FlagEmbedding/releases/tag/1.1","date":"2023-09-28T07:43:03Z","type":"stable","version":"1.1"}]` | `[{"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=flagembedding&per_page=100","last_12m":0,"max_severity":null}` | `{"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 | BAAI FlagEmbedding | FastEmbed |
|---|:--:|:--:|
| **Capabilities** |  |  |
| Architecture model | Open weights local | Open weights local |
| 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 | Free open weights |

*Source: vioscaleAI. Generated 2026-10-16T19:21:57.078Z. "-" = undocumented, not absent.*