# FastEmbed vs Voyage AI

| Attribute | FastEmbed | Voyage AI |
|---|---|---|
| **Vioscale score** | 46.6 (31% (low)) | 41.6 (20% (low)) |
| activity.commits_last_30d | 13 | - |
| adoption.dependent_repos | 4 | - |
| adoption.github_stars | 3,166 | - |
| deployment.options | `{"on_prem":true,"self_hosted":true}` | `{"cloud":true,"hybrid":true}` |
| description.long | 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. | 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. |
| 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 | 1 | 2 |
| integrations.list | `[{"name":"Qdrant"}]` | `[{"name":"vector databases"},{"name":"LLMs"}]` |
| language.primary | Python | - |
| license.spdx | Apache-2.0 | - |
| pricing.model | - | commercial |
| release.cadence_days | 33 | - |
| release.history | `[{"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"},{"url":"https://github.com/qdrant/fastembed/releases/tag/v0.2.5","date":"2024-03-20T13:44:11Z","type":"stable","version":"v0.2.5"},{"url":"https://github.com/qdrant/fastembed/releases/tag/v0.2.4","date":"2024-03-13T18:26:53Z","type":"stable","version":"v0.2.4"}]` | - |
| 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: Vioscale. Generated 2026-09-01T15:16:15.165Z. "-" = undocumented, not absent.*