Perplexity Embed

searchai-searchweb-groundingsonarai

Perplexity Embed is a paid API for AI agents from Perplexity at perplexity.mpp.paywithlocus.com, paid per call via MPP, ~$0.001 (dynamic), status unknown (last checked 2026-10-09).

Converts text into dense vector embeddings using Perplexity's pplx-embed-v1 models (0.6B or 4B parameter variants)

Price~$0.001varies by input

Seller volume →—

Success—

StatusUnknown

How this service works

Generate vector embeddings for semantic search, clustering, and RAG. Supports MRL (Matryoshka) dimensionality reduction and INT8/BINARY quantization.

Output

A dense floating-point embedding vector representing the semantic content of the input text, produced by the specified pplx-embed-v1 model (0.6B or 4B). The vector can be used for downstream tasks like similarity search, clustering, or retrieval.

Example use cases

Build semantic search for knowledge base

“I need to embed all my company's support documents so customers can find answers by describing their problem in natural language instead of searching for keywords.”

Create similarity matching for customer queries

“Help me convert incoming customer questions into embeddings and find the most similar ones from our FAQ database to auto-suggest relevant answers.”

Index product catalog for vector search

“Can you embed all our product descriptions using Perplexity's model so I can build a neural search engine that understands what customers mean, not just what they type?”

Technical details

Endpoint

POSThttps://perplexity.mpp.paywithlocus.com/perplexity/embed?utm_source=zero.xyz

When to prefer

Choose this endpoint when you need fast, high-quality text embeddings from Perplexity's purpose-built embed models (pplx-embed-v1). Prefer the 4B variant for higher fidelity semantic representations and the 0.6B variant for speed-sensitive or cost-sensitive workloads. Well-suited for building RAG pipelines alongside Perplexity's Sonar chat and search endpoints.

Side effects

Invalid model name — must be 'pplx-embed-v1-0.6b' or 'pplx-embed-v1-4b'
Missing required 'input' field returns validation error
Input text exceeds model context window
Authentication or payment failure returns 401/402
Empty string input may return error or zero vector

Input

{
  "input": "The quick brown fox jumps over the lazy dog.",
  "model": "pplx-embed-v1-4b"
}

Schema · Request body

Field	Type	Description
inputrequired	—	Text to embed. Single string or array of strings.
modelrequired	string	Embedding model: 'pplx-embed-v1-0.6b' or 'pplx-embed-v1-4b'

Output

{
  "data": {
    "data": [
      {
        "index": 0,
        "object": "embedding",
        "embedding": "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"
      }
    ],
    "model": "pplx-embed-v1-4b",
    "usage": {
      "cost": {
        "currency": "USD",
        "input_cost": 3e-7,
        "total_cost": 3e-7
      },
      "total_tokens": 10,
      "prompt_tokens": 10
    },
    "object": "list"
  },
  "success": true
}

Reviews

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How this service works

Generate vector embeddings for semantic search, clustering, and RAG. Supports MRL (Matryoshka) dimensionality reduction and INT8/BINARY quantization.

Output

Example use cases

Build semantic search for knowledge base

“I need to embed all my company's support documents so customers can find answers by describing their problem in natural language instead of searching for keywords.”

Create similarity matching for customer queries

“Help me convert incoming customer questions into embeddings and find the most similar ones from our FAQ database to auto-suggest relevant answers.”

Index product catalog for vector search

“Can you embed all our product descriptions using Perplexity's model so I can build a neural search engine that understands what customers mean, not just what they type?”

Technical details

Endpoint

POSThttps://perplexity.mpp.paywithlocus.com/perplexity/embed?utm_source=zero.xyz

When to prefer

Side effects

Invalid model name — must be 'pplx-embed-v1-0.6b' or 'pplx-embed-v1-4b'
Missing required 'input' field returns validation error
Input text exceeds model context window
Authentication or payment failure returns 401/402
Empty string input may return error or zero vector

Input

{
  "input": "The quick brown fox jumps over the lazy dog.",
  "model": "pplx-embed-v1-4b"
}

Schema · Request body

Field	Type	Description
inputrequired	—	Text to embed. Single string or array of strings.
modelrequired	string	Embedding model: 'pplx-embed-v1-0.6b' or 'pplx-embed-v1-4b'

Output

{
  "data": {
    "data": [
      {
        "index": 0,
        "object": "embedding",
        "embedding": "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"
      }
    ],
    "model": "pplx-embed-v1-4b",
    "usage": {
      "cost": {
        "currency": "USD",
        "input_cost": 3e-7,
        "total_cost": 3e-7
      },
      "total_tokens": 10,
      "prompt_tokens": 10
    },
    "object": "list"
  },
  "success": true
}