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Amazon Titan Embed Text v2

Up to 30%

Embed text at scale with Amazon Titan Embed Text v2 on LLM.API—ideal for search, RAG, and clustering via the OpenAI-compatible embeddings API.

Start Using API

What is Amazon Titan Embed Text v2?

Choose Amazon Titan Embed Text v2 when your pipeline needs dense representations instead of generated prose. AWS Bedrock Titan Embed Text v2. 1024 default dimensions, configurable to 256/512/1024. 8192-token input window. LLM.API exposes it with OpenAI-compatible embedding calls.

Providers

LLM.API routes Amazon Titan Embed Text v2 to the providers below, with discounted effective rates versus list price.

List price by provider ($ / 1M tokens)

InputOutput
aws-bedrock$0.02 in
$0 out

Provider list prices; the LLM.API discount applies on top.

ProviderPricingContextCapabilities
aws-bedrock30% offin $20; out $0 per 1M tokens8K tokens—

Prices and availability from the LLMAPI catalogue, updated nightly. Last updated 21 Sept 2026.

Try this model

Test Amazon Titan Embed Text v2 right here — free to start.

Amazon Titan Embed Text v2
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Code snippet

Call Amazon Titan Embed Text v2 through the OpenAI-compatible API — POST /v1/embeddings.

python
from openai import OpenAI

client = OpenAI(
    api_key="YOUR_LLMAP_KEY",
    base_url="https://api.llmapi.ai/v1",
)

resp = client.embeddings.create(
    model="amazon.titan-embed-text-v2:0",
    input="Semantic search starts with strong embeddings.",
)
print(len(resp.data[0].embedding))
{
  "model": "amazon.titan-embed-text-v2:0",
  "input": "Semantic search starts with strong embeddings."
}

5 Core Capabilities

  • Classification features

    Vectors can feed lightweight classifiers and routing logic. Tuned to how teams typically call Amazon Titan Embed Text v2.

  • Multilingual text

    Embeds content across languages depending on the underlying model family. Relevant for `amazon.titan-embed-text-v2:0` workloads on LLM.API.

  • Retrieval readiness

    Fits RAG pipelines that need stable document and query embeddings. Tuned to how teams typically call Amazon Titan Embed Text v2.

  • Cross-document matching

    Supports near-duplicate detection and topical grouping. Reflects Amazon positioning for this endpoint.

  • Low-latency lookups

    Suitable for online retrieval when paired with a vector store. Tuned to how teams typically call Amazon Titan Embed Text v2.

6 Most Valuable Use Cases

  • Duplicate and near-duplicate content detection with Amazon Titan Embed Text v2
  • Anomaly grouping in incident descriptions
  • Recommendation features based on text similarity with Amazon Titan Embed Text v2
  • RAG retrieval for grounded chat answers
  • Lead and account similarity scoring from notes with Amazon Titan Embed Text v2
  • Semantic search over docs, tickets, and wikis

Why Build on LLM.API?

One unified API. Every major model. Built-in reliability, cost control, and observability.

  • Intelligent AI Routing

    Automatically route each request to the best model across providers based on latency, cost, and quality—without changing your integration or redeploying code.

    One endpoint, every model.

  • Cost-Aware Execution

    Control spend with per-request cost estimation, smart model selection, and centralized quotas so teams can experiment fast without runaway bills or manual tracking.

    More performance, less spend.

  • Resilient Fallback Flows

    Define automatic, provider-agnostic fallbacks to keep your app up during outages, rate limits, or timeouts—no brittle failover logic scattered through your codebase.

    Never go dark on users.

  • Deep LLM Observability

    Trace every call across providers with logs, metrics, and request replay so you can debug, tune prompts, and optimize model choices from one unified dashboard.

    See every token, everywhere.

  • Task-Level Orchestration

    Describe tasks, not models. LLM.API maps them to the right tools, models, and prompts so you ship complex AI workflows with minimal glue code.

    Think tasks, not models.

  • High-Throughput Batch APIs

    Process millions of inferences efficiently with optimized batch pipelines, concurrency controls, and retry logic—all behind the same simple interface you use for single calls.

    Scale from 1 to millions.

Why run Amazon Titan Embed Text v2 on LLM.API?

  • Unified AI Routing

    Production: Reach Amazon Titan Embed Text v2 and sibling models through one OpenAI-compatible endpoint.

  • Cost Control

    Production: Compare provider price points and keep spend visible as you scale Amazon Titan Embed Text v2.

  • Reliability Layer

    Retry and route across configured providers when a single upstream blips.

  • Observability

    Trace prompts, tokens, and errors for Amazon Titan Embed Text v2 alongside the rest of your stack.

  • Drop-in SDKs

    Keep using familiar OpenAI client patterns with base URL https://api.llmapi.ai/v1.

  • Model Breadth

    Production: Swap Amazon Titan Embed Text v2 for chat, media, or embedding alternatives without rewriting auth.

When to Use — When NOT to Use

Use it if...

  • Throughput and cost matter more than generative prose quality (Amazon Titan Embed Text v2)
  • You need consistent vectors from an OpenAI-compatible embeddings API (Amazon Titan Embed Text v2)
  • You are building search, RAG, or clustering on text (Amazon Titan Embed Text v2)

Avoid if...

  • You only need OCR or speech features
  • You need generative answers instead of vectors
  • You require cross-model vector compatibility without re-indexing

COMMUNITY

What developers say about Amazon Nova models

Summarised from publicly published developer and community reviews of this model family. Opinions are the sources’, not LLM.API’s, and may not be specific to Amazon Titan Embed Text v2.

  • Reviews consistently call Nova cheap and underrated — best-in-class pricing with tight AWS/Bedrock integration.
  • Amazon's own positioning, echoed by commentators, is that benchmark position matters less than cost and deployment fit.
  • Developers pick Nova for high-volume, cost-sensitive workloads already running inside AWS.

SOURCES

Frequently Asked Questions

  • What modalities does Amazon Titan Embed Text v2 support?

    Amazon Titan Embed Text v2 accepts text and produces embedding according to its architecture metadata on LLM.API.

  • Can Amazon Titan Embed Text v2 generate paragraphs of text?

    No. Amazon Titan Embed Text v2 returns embeddings (vectors). Pair it with a chat model if you need generated language.

  • Can I use tools or structured outputs with Amazon Titan Embed Text v2?

    Tooling support varies; for pure embedding models, prefer the modalities listed rather than assuming chat tools.

  • What are limitations of Amazon Titan Embed Text v2?

    Like other API models, Amazon Titan Embed Text v2 can be wrong, incomplete, or uneven on edge cases. Validate outputs for high-stakes use. Media/OCR/STT models additionally depend on input quality.

  • How do I call Amazon Titan Embed Text v2 via API?

    Send OpenAI-compatible requests to https://api.llmapi.ai/v1 with model "amazon.titan-embed-text-v2:0" and your LLM.API key. See the code snippet on this page.

  • How is Amazon Titan Embed Text v2 priced on LLM.API?

    Listed pricing metadata shows: In $0.02 / 1M tokens. LLM.API may offer discounted effective rates (illustrative ~30% callout vs list when available).. Confirm live rates in the LLM.API dashboard or docs before production budgeting.

  • Does Amazon Titan Embed Text v2 support streaming?

    Streaming depends on the active provider; check the providers table on this page for flags.

  • What is Amazon Titan Embed Text v2?

    AWS Bedrock Titan Embed Text v2. 1024 default dimensions, configurable to 256/512/1024. 8192-token input window. On LLM.API it is addressed as `amazon.titan-embed-text-v2:0`.

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