{"uid":"cap_EyYxvcIlMJGBAmKKRdiN2","slug":"cortexcloud-batch-embeddings-api-063c10ad","name":"CortexCloud Batch Embeddings API","description":"OpenAI-compatible AI and data API for agents. Pay per call in USDC on Base via x402 — no API keys, no subscriptions, no lock-in.","url":"https://api.cortexcloud.org/x402/v1/embeddings/batch","method":"GET","headers":{},"bodySchema":{"type":"object","properties":{"input":{"type":"array","items":{"type":"string"},"description":"List of texts (max 100) to embed"},"model":{"type":"string","description":"Embedding model id"}}},"responseSchema":{"type":"object","format":"application/json","example":{"data":[{"index":0,"object":"embedding","embedding":[]}],"usage":{"total_tokens":0,"prompt_tokens":0}}},"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.005","displayCostAsset":"USDC","priceDynamic":false,"priceHint":null,"priceStatus":"priced","priceSource":"probe","requiresHandshake":false,"reviewCount":0,"rating":{"score":"0.00","successRate":"0.00","reviews":0,"stars":null,"state":"unrated"},"availabilityStatus":"unknown","priceObserved":null,"sessionDeposit":null,"pricing":{"kind":"static","summary":"$0.005/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.005","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.005","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_HHpxOpoAfzVsQUAGHCIvd","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.005","costPer":"request","priority":0,"asset":"0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Generates vector embeddings for a batch of up to 100 text strings in a single call, returning dense float arrays compatible with OpenAI embedding format","exampleAgentPrompt":"Embed these 50 product descriptions into vectors using the text-embedding-3-small model so I can load them into my vector database for semantic search.","exampleUseCases":[{"title":"RAG pipeline document ingestion","prompt":"I have a list of 80 documentation paragraphs I need to embed — can you batch them all into vectors using text-embedding-3-small so I can store them in Pinecone for retrieval?"},{"title":"Semantic similarity scoring","prompt":"Take these 30 customer support tickets and convert them to embeddings so I can cluster similar issues together — use the most accurate embedding model available."},{"title":"Duplicate content detection","prompt":"I've got 60 product listing descriptions and need to find near-duplicates — can you vectorize all of them in one shot so I can run cosine similarity across the results?"}],"resultDescription":"Returns a JSON object with a data array where each item contains an index, object type ('embedding'), and the embedding float array for the corresponding input text. Also includes a usage object with total_tokens and prompt_tokens counts for the batch.","failureModes":["Input array exceeds 100 items — batch must be split into smaller chunks","Invalid or unsupported model ID — returns error indicating unknown model","Payment failure via x402 — call rejected if USDC payment on Base is not completed","Empty input array — may return empty data array or validation error","Texts too long for the model's context window — individual items may be truncated or rejected"],"whenToPreferThis":"Choose this endpoint when you need to vectorize multiple texts in a single round-trip, saving latency and cost compared to calling a single-embedding endpoint repeatedly. It is ideal for RAG ingestion pipelines, semantic search index building, or clustering tasks where you have 2–100 texts ready to embed at once. The OpenAI-compatible response format means it slots directly into existing tooling. The pay-per-call USDC model on Base is preferable when you want no subscription overhead and programmatic micropayment billing for agents.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-09-15T00:52:21.092Z","isFirstParty":false}