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text-embeddings-vector-api
x402
text-embeddings-vector-api is a paid API for AI agents from obol-x402.fly.dev, paid per call via x402, $0.002/call, status down (last checked 2026-09-15).
Generates 768-dimensional text embeddings (nomic-embed-text model) on self-hosted GPU, returning OpenAI-shaped [{index, embedding}] vectors for single or batch (up to 16) inputs, cryptographically signed with Ed25519.
Price / call$0.002
Success—
StatusDown
How this service works
Text embeddings over x402 — nomic-embed-text (768-dim) on OUR OWN GPU, never forwarded to a third-party API. OpenAI-shaped data[{index, embedding}] response; batch up to 16 texts in ONE flat-price call. For RAG, semantic search, dedup and memory pipelines. No API key, no account. GET ?text= or POST {input:[...]}. Free: GET /v1/ai/embed/preview. Cryptographically signed (Ed25519) — every response is independently verifiable via /.well-known/obol-pubkey.
Output
Returns an OpenAI-shaped JSON array of objects [{index: number, embedding: number[768]}], one per input text. Each response is cryptographically signed with Ed25519 (verifiable via /.well-known/obol-pubkey). Inference runs on the provider's own GPU — nomic-embed-text model, 768 dimensions per vector.
Example use cases
RAG document ingestion pipeline
“I need to embed these 16 product descriptions into vectors so I can store them in my Pinecone index for semantic search — can you batch embed all of them in one call?”
Semantic deduplication of user queries
“I have two user support messages and I want to check if they're semantically the same before routing — can you embed both texts so I can compute their cosine similarity?”
Memory encoding for AI agent
“Store this conversation summary as a vector in my agent's long-term memory: 'User prefers concise answers and works in fintech.' I need the 768-dim embedding, signed so I can verify it later.”
Technical details
Endpoint
GEThttps://obol-x402.fly.dev/v1/ai/embedWhen to prefer
Choose this endpoint when you need 768-dim nomic-embed-text vectors without signing up for an API key or account, want cryptographically verifiable (Ed25519-signed) embeddings, need to batch up to 16 texts in a single flat-price call ($0.002 USDC), or require embeddings computed on a self-hosted GPU with no third-party data forwarding. Prefer over OpenAI embeddings when privacy or auditability of inference is a concern, or when integrating into an x402 micropayment workflow.
Side effects
- Missing or empty 'text' query param on GET returns error
- Batch exceeds 16 texts — rejected with error
- Payment not provided or insufficient USDC — 402 Payment Required
- Invalid input type or schema mismatch — 400 Bad Request
- GPU inference timeout on very long inputs
- Signature verification failure if pubkey endpoint is unavailable
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| inputrequired | object | |
| output | object |
Tags
x402
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