# Amazon Bedrock Titan Embeddings vs FastEmbed

**Leader by vioscaleAI score:** Amazon Bedrock Titan Embeddings

| Attribute | Amazon Bedrock Titan Embeddings | FastEmbed |
|---|---|---|
| **vioscaleAI score** | 63.8 (35% (low)) | 56.9 (54% (medium)) |
| activity.commits_last_30d | - | 71 |
| adoption.dependent_repos | - | 4 |
| adoption.github_stars | - | 3,244 |
| content.faq | `[{"answer":"A managed embedding model service within Amazon Bedrock that converts document text into vector representations for semantic search and retrieval tasks, charged per query. It is indexed under Embeddings Apis.","source":"https://aws.amazon.com/bedrock/pricing/?nc2=h_pr_br","question":"What is Amazon Bedrock Titan Embeddings?","confidence":0.6},{"answer":"Amazon Bedrock Titan Embeddings offers a free tier, so you can start without paying. Pricing changes often, so verify at source before relying on it.","source":"https://aws.amazon.com/bedrock/pricing/?nc2=h_pr_br","question":"Is Amazon Bedrock Titan Embeddings free?","confidence":0.6},{"answer":"We have confirmed browser-based access to Amazon Bedrock Titan Embeddings. That is the extent of what we could verify from public sources, so it may well offer desktop or mobile clients we have not indexed.","source":"https://aws.amazon.com/bedrock/pricing/?nc2=h_pr_br","question":"What platforms does Amazon Bedrock Titan Embeddings support?","confidence":0.6},{"answer":"We have only confirmed a cloud / SaaS deployment for Amazon Bedrock Titan Embeddings, so it appears to be vendor-hosted. If a self-hosted option exists we have not found it documented publicly.","source":"https://aws.amazon.com/bedrock/pricing/?nc2=h_pr_br","question":"Can Amazon Bedrock Titan Embeddings be self-hosted?","confidence":0.6},{"answer":"We have independently confirmed SOC 2, ISO 27001, HIPAA and GDPR for Amazon Bedrock Titan Embeddings. Certifications we do not list are ones we have not been able to verify from public sources, which is not the same as Amazon Bedrock Titan Embeddings not holding them. Always confirm compliance directly before you rely on it.","source":"https://aws.amazon.com/compliance/?nc2=h_dsc_aa_cmp","question":"What security certifications does Amazon Bedrock Titan Embeddings have?","confidence":0.7}]` | `[{"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}` | `{"cloud":true,"self_hosted":true}` |
| description.long | A managed embedding model service within Amazon Bedrock that converts document text into vector representations for semantic search and retrieval tasks, charged per query. | 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,"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 |
| license.spdx | - | Apache-2.0 |
| market.availability | `{"primaryMarkets":[],"availabilityScope":"global","availableCountries":[],"notAvailableCountries":[]}` | - |
| platform.support | `{"web":true}` | - |
| pricing | `{"type":"usage","summary":"Usage-based pricing per query; specific rates not disclosed in provided text.","currency":"USD","sourceUrl":"https://aws.amazon.com/bedrock/pricing/?nc2=h_pr_br","retrievedAt":"2026-09-06T16:17:51.375Z"}` | `{"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 | unknown | free |
| 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.fedramp | yes | - |
| security.gdpr | yes | - |
| security.hipaa | yes | - |
| security.iso27001 | yes | - |
| security.pci | yes | - |
| security.soc2 | yes | - |
| security.trust_center | https://aws.amazon.com/compliance/?nc2=h_dsc_aa_cmp | - |
| 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 | Amazon Bedrock Titan Embeddings | FastEmbed |
|---|:--:|:--:|
| **Capabilities** |  |  |
| Architecture model | - | 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 |

*Source: vioscaleAI. Generated 2026-10-16T20:02:25.007Z. "-" = undocumented, not absent.*