# FastEmbed vs Voyage AI

**Leader by vioscaleAI score:** FastEmbed

| Attribute | FastEmbed | Voyage AI |
|---|---|---|
| **vioscaleAI score** | 56.9 (54% (medium)) | 41.9 (37% (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 platform providing advanced embedding and reranking models designed to improve search accuracy and quality in retrieval-augmented generation workflows with support for various data types and deployment scenarios. It is indexed under Embeddings Apis.","source":"https://www.voyageai.com","question":"What is Voyage AI?","confidence":0.6},{"answer":"Not fully, on what we have confirmed. Voyage AI is offered as cloud / SaaS and hybrid, and a hybrid deployment usually means some components can run on your own infrastructure while the rest stays vendor-hosted. We have not confirmed a completely self-hosted option, so check with the vendor if that is a requirement.","source":"https://www.voyageai.com","question":"Can Voyage AI be self-hosted?","confidence":0.6},{"answer":"We have independently confirmed SOC 2 for Voyage AI. Certifications we do not list are ones we have not been able to verify from public sources, which is not the same as Voyage AI not holding them. Always confirm compliance directly before you rely on it.","source":"https://www.voyageai.com","question":"What security certifications does Voyage AI have?","confidence":0.48}]` |
| deployment.options | `{"cloud":true,"self_hosted":true}` | `{"cloud":true,"on_prem":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. | Voyage AI offers neural embedding and reranking models designed to enhance information retrieval and relevance ranking in systems that process large collections of unstructured text. The models are optimized for question-answering and search applications that need to find and rank relevant documents efficiently. |
| 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":"managed_cloud_api","cross_encoder_reranker_api_offered":true,"multi_modal_image_and_text_joint_embeddings":true,"massive_context_window_greater_than_8k_tokens":true,"multilingual_support_across_100_plus_languages":true}` |
| integrations.count | 3 | 1 |
| integrations.list | `[{"name":"Qdrant"},{"name":"Langchain"},{"name":"Llama Index"}]` | `[{"name":"vector databases"},{"name":"LLMs"},{"name":"MongoDB","native":true}]` |
| language.primary | Python | - |
| license.spdx | Apache-2.0 | - |
| pricing | `{"type":"open_source","freeTier":true,"sourceUrl":"https://qdrant.github.io/fastembed/","retrievedAt":"2026-09-13T20:06:10.444Z"}` | - |
| pricing.free_tier | yes | - |
| pricing.model | open_source | commercial |
| pricing.price_level | free | - |
| 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.soc2 | - | yes |
| 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 | Voyage AI |
|---|:--:|:--:|
| **Capabilities** |  |  |
| Architecture model | Open weights local | Managed cloud API |
| 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 2026-10-16T20:13:23.465Z. "-" = undocumented, not absent.*