Amazon Titan Text Embeddings V2
How TheRouter serves this differently from the vendor
AWS serves Titan Text Embeddings V2 on the Bedrock bedrock-runtime endpoint with Invoke, text input, embedding output, an 8K-token / 50,000-character input ceiling, model id amazon.titan-embed-text-v2:0, and no cross-region inference profile on the current model card.
TheRouter serves the same embedding model as amazon/titan-embed-v2 through /v1/embeddings with OpenAI-compatible client syntax. Operational price, context, modality, and supported-parameter fields come from the live TheRouter catalog; the curated page documents AWS source-of-truth behavior and leaves snippets capped until an operator-funded verification run records execution.
API guide
Embeddings request
Generate embeddings for a text input with configurable dimensions and optional normalization. TheRouter routes your request transparently to Amazon Bedrock with fully OpenAI‑compatible syntax.
curl https://api.therouter.ai/v1/embeddings \
-H "Authorization: Bearer $THEROUTER_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "amazon/titan-embed-v2",
"input": "Your text to embed here.",
"dimensions": 256,
"encoding_format": "float"
}'Dimension and normalization policy
Pick 256, 512, or 1024 dimensions per index and keep the choice stable. Normalization should also be set deliberately because it changes vector distance behavior in the downstream database.
curl https://api.therouter.ai/v1/embeddings \
-H "Authorization: Bearer $THEROUTER_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "amazon/titan-embed-v2",
"input": ["First passage", "Second passage"],
"dimensions": 512,
"normalize": true
}'Fact ledger — every claim on this page traces here
| source | URL | retrieved | |
|---|---|---|---|
| Release date | Amazon Bedrock docs ↗ | 2026-08-23 | verified |
| Output dimensions | docs.aws.amazon.com ↗ | 2026-08-23 | verified |
| Vector normalization | docs.aws.amazon.com ↗ | 2026-08-23 | verified |
| Supported languages | docs.aws.amazon.com ↗ | 2026-08-23 | verified |
| Max input characters | docs.aws.amazon.com ↗ | 2026-08-23 | verified |
| Training cutoff | — | — | unknown |
| Tokens‑to‑characters ratio (English) | docs.aws.amazon.com ↗ | 2026-08-23 | verified |
| MTEB (Massive Text Embedding Benchmark) weighted average | AWS ML blog ↗ | 2026-08-23 | verified |
| MTEB reranking | aws.amazon.com ↗ | 2026-08-23 | verified |
| MIRACL multilingual | aws.amazon.com ↗ | 2026-08-23 | verified |
| Amazon Titan Text Embeddings V2 now available in Amazon Bedrock | AWS News Blog ↗ | 2026-08-23 | verified |
| Get started with Amazon Titan Text Embeddings V2: A new state‑of‑the‑art embeddings model on Amazon Bedrock | AWS Machine Learning Blog ↗ | 2026-08-23 | verified |
| What are the practical accuracy‑storage trade‑offs between 256, 512, and 1024 dimensions? | aws.amazon.com ↗ | 2026-08-23 | to verify |
| Does Titan V2 support cross‑language search (e.g., query in German against a corpus in Korean)? | docs.aws.amazon.com ↗ | 2026-08-23 | to verify |
| Why does Bedrock throttle embedding models by RPM instead of TPM? What’s the performance implication? | docs.aws.amazon.com ↗ | 2026-08-23 | to verify |
| What’s the difference between Titan V2 and its predecessor Titan G1 (V1)? | aws.amazon.com ↗ | 2026-08-23 | to verify |