[Amazon](/ai-gateway/models/labs/amazon)

# Titan Text Embeddings V2

Titan Text Embeddings V2 is Amazon's text embedding model tuned for retrieval-augmented generation (RAG). Vectors only; no output token charge. You can choose 256-, 512-, or 1024-dimensional output vectors, with support for 100+ languages. Your use is subject to Amazon's [Terms](https://aws.amazon.com/service-terms/) & [Privacy](https://aws.amazon.com/privacy/) Policies.

[Use with AI Gateway](https://vercel.com/d?to=%2F%5Bteam%5D%2F%7E%2Fai%3Futm_source%3Dgateway-model-page%26utm_campaign%3Dai-gateway-models&title=Get+Started+with+Vercel+AI+Gateway) [View docs](https://vercel.com/docs/ai-gateway)

```
1import { embed } from 'ai';
2

3const result = await embed({
4  model: 'amazon/titan-embed-text-v2',
5  value: 'Sunny day at the beach',
6})
```

[Read docs](https://vercel.com/docs/ai-gateway/sdks-and-apis/ai-sdk)

[Overview](/ai-gateway/models/titan-embed-text-v2) [About](/ai-gateway/models/titan-embed-text-v2/about) [Providers](/ai-gateway/models/titan-embed-text-v2/providers) [Similar](/ai-gateway/models/titan-embed-text-v2/similar) [FAQ](/ai-gateway/models/titan-embed-text-v2/faq)

## [Copy link to heading](#providers)Providers

Route requests across multiple providers. Copy a provider slug to set your preference. Visit the [docs](/docs/ai-gateway/provider-options) for more info. Using a provider means you agree to their terms, listed under Legal.

| Provider |
| --- |

| Context | Input | Capabilities | ZDR | No Training | Release Date |  |
| --- | --- | --- | --- | --- | --- | --- |

| ![bedrock logo](/vc-ap-vercel-marketing/_next/image?url=https%3A%2F%2F7nyt0uhk7sse4zvn.public.blob.vercel-storage.com%2Fdocs-assets%2Fstatic%2Fdocs%2Fai-gateway%2Flogos%2Famazon%2520bedrock.png&w=48&q=75) [Bedrock](/ai-gateway/models/providers/bedrock) Legal:[Terms](https://aws.amazon.com/service-terms/)•[Privacy](https://aws.amazon.com/privacy/) |  | $0.02/M |  |  |  | 04/30/2024 |  |
| --- | --- | --- | --- | --- | --- | --- | --- |

## [Copy link to heading](#more-models-by-amazon)More models by Amazon

All

Text

Code

| Model |
| --- |

| Context | Latency | Throughput | Input | Output | Cache | Web Search | Capabilities | Providers | ZDR | No Training | Release Date |  |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |

| ![amazon logo](/vc-ap-vercel-marketing/_next/image?url=https%3A%2F%2F7nyt0uhk7sse4zvn.public.blob.vercel-storage.com%2Fdocs-assets%2Fstatic%2Fdocs%2Fai-gateway%2Flogos%2Famazon%20bedrock.png&w=48&q=75) [amazon/nova-2-lite](/ai-gateway/models/nova-2-lite) | 1M | 0.4s | 211tps | $0.30/M | $2.50/M | Read:$0.07/M Write:— | — | +1 | ![bedrock logo](/vc-ap-vercel-marketing/_next/image?url=https%3A%2F%2F7nyt0uhk7sse4zvn.public.blob.vercel-storage.com%2Fdocs-assets%2Fstatic%2Fdocs%2Fai-gateway%2Flogos%2Famazon%2520bedrock.png&w=48&q=75) |  |  | 12/02/2025 |  |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| ![amazon logo](/vc-ap-vercel-marketing/_next/image?url=https%3A%2F%2F7nyt0uhk7sse4zvn.public.blob.vercel-storage.com%2Fdocs-assets%2Fstatic%2Fdocs%2Fai-gateway%2Flogos%2Famazon%20bedrock.png&w=48&q=75) [amazon/nova-lite](/ai-gateway/models/nova-lite) | 300K | 0.3s |  | $0.06/M | $0.24/M |  | — |  | ![bedrock logo](/vc-ap-vercel-marketing/_next/image?url=https%3A%2F%2F7nyt0uhk7sse4zvn.public.blob.vercel-storage.com%2Fdocs-assets%2Fstatic%2Fdocs%2Fai-gateway%2Flogos%2Famazon%2520bedrock.png&w=48&q=75) |  |  | 12/03/2024 |  |
| ![amazon logo](/vc-ap-vercel-marketing/_next/image?url=https%3A%2F%2F7nyt0uhk7sse4zvn.public.blob.vercel-storage.com%2Fdocs-assets%2Fstatic%2Fdocs%2Fai-gateway%2Flogos%2Famazon%20bedrock.png&w=48&q=75) [amazon/nova-micro](/ai-gateway/models/nova-micro) | 128K | 0.4s |  | $0.04/M | $0.14/M |  | — |  | ![bedrock logo](/vc-ap-vercel-marketing/_next/image?url=https%3A%2F%2F7nyt0uhk7sse4zvn.public.blob.vercel-storage.com%2Fdocs-assets%2Fstatic%2Fdocs%2Fai-gateway%2Flogos%2Famazon%2520bedrock.png&w=48&q=75) |  |  | 12/03/2024 |  |
| ![amazon logo](/vc-ap-vercel-marketing/_next/image?url=https%3A%2F%2F7nyt0uhk7sse4zvn.public.blob.vercel-storage.com%2Fdocs-assets%2Fstatic%2Fdocs%2Fai-gateway%2Flogos%2Famazon%20bedrock.png&w=48&q=75) [amazon/nova-pro](/ai-gateway/models/nova-pro) | 300K | 0.3s | 152tps | $0.80/M | $3.20/M |  | — |  | ![bedrock logo](/vc-ap-vercel-marketing/_next/image?url=https%3A%2F%2F7nyt0uhk7sse4zvn.public.blob.vercel-storage.com%2Fdocs-assets%2Fstatic%2Fdocs%2Fai-gateway%2Flogos%2Famazon%2520bedrock.png&w=48&q=75) |  |  | 12/03/2024 |  |

## [Copy link to heading](#about-titan-text-embeddings-v2)About Titan Text Embeddings V2

Titan Text Embeddings V2 is a Bedrock embedding model for enterprise retrieval: RAG, semantic search, multilingual indexes, and general text or code chunks. It accepts a maximum of 8,192 tokens per input (about 50,000 English characters as a rough guide) and returns a dense vector for cosine or similar similarity search.

You choose 256-, 512-, or 1024-dimensional output. Titan Text Embeddings V2 reports that 512-dimensional vectors keep about 99% of the accuracy of 1024-dimensional vectors, and 256-dimensional vectors keep about 97%, which trims storage and often latency on large indexes.

V2 adds improved unit-vector normalization options for similarity scoring. Titan Text Embeddings V2 was pre-trained on 100+ languages and on code, so one index can cover mixed-language corpora when your evaluation says recall stays high enough.

## [Copy link to heading](#what-to-consider-when-choosing-a-provider)What To Consider When Choosing a Provider

- Configuration: Select your output dimension before deploying to production. Once vectors are stored in your vector database at a given dimension, changing the size requires reindexing your entire corpus.
- Zero Data Retention: Zero Data Retention is available for this model. It is offered on a per-provider and model basis. See the [documentation](https://vercel.com/docs/ai-gateway/security-and-compliance/zdr) for details.
- Authentication: AI Gateway authenticates requests using an [API key](https://vercel.com/docs/ai-gateway/authentication-and-byok#api-key-authentication) or [OIDC token](https://vercel.com/docs/ai-gateway/authentication-and-byok#oidc-token-authentication). You do not need to manage provider credentials directly.

## [Copy link to heading](#when-to-use-titan-text-embeddings-v2)When to Use Titan Text Embeddings V2

### Best for

- Cost-efficient RAG pipelines: High-accuracy embedding model with flexible vector dimensions
- Storage-constrained knowledge bases: The 512-dimension option retains about 99% accuracy at lower storage cost
- Multilingual document collections: A single embedding model covers 100+ languages without separate per-language models
- Bedrock-integrated search: Semantic search and document reranking workflows integrated with Amazon Bedrock
- General-purpose code search: Code search and classification tasks that benefit from a dense embedding rather than a code-specific model

### Consider alternatives when

- Multimodal embedding requirements: Amazon Nova Multimodal Embeddings may be more appropriate for text, image, and video in the same vector space
- Code-specific retrieval: A model tuned specifically for code retrieval outperforms general-purpose text embeddings
- Longer input token limit: English-only retrieval corpora may benefit from an embedding model with a larger token input capacity

## [Copy link to heading](#conclusion)Conclusion

Titan Text Embeddings V2 lets you trade storage and latency against accuracy by picking vector width, with normalization tuned for typical RAG similarity. It fits Bedrock-centric RAG stacks you already run through AI Gateway.