{"uid":"cap_4qYFHHE27Fw6Z_FP6zGOU","slug":"aws-bedrock-embeddings-api-x402-ee53f8ad","name":"AWS Bedrock Embeddings API (x402)","description":"Pay-per-call Amazon Bedrock proxy on x402 and MPP.","url":"https://aws-bedrock-agent-api.vercel.app/api/embeddings?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"input":{"anyOf":[{"type":"string","minLength":1},{"type":"array","items":{"type":"string","minLength":1},"maxItems":64,"minItems":1}],"description":"Text or list of texts to embed"},"model":{"type":"string","maxLength":200,"minLength":1,"description":"Azure embedding deployment from GET /api/models."}}},"responseSchema":null,"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_h8urX_bTxIU-_hZ1k-F3L","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":"Converts text or batches of text into vector embeddings using Amazon Bedrock models, accessible via pay-per-call with USDC micropayments.","exampleAgentPrompt":"Embed this text using the AWS Bedrock Titan embedding model: 'Quantum computing leverages superposition to solve complex problems faster than classical computers.' — I need the vector representation for a semantic search index.","exampleUseCases":[{"title":"Semantic search index ingestion","prompt":"Take these 10 product descriptions and embed them all using the AWS Bedrock Titan model so I can store them in my vector database for semantic search."},{"title":"RAG pipeline document encoding","prompt":"I'm building a retrieval-augmented generation system — can you embed this paragraph using the Bedrock embedding model so I can match it against user queries later?"},{"title":"Text similarity scoring","prompt":"Embed both of these two customer reviews using the AWS Bedrock embedding model so I can compare their vector similarity and see how related they are."}],"resultDescription":"Returns dense vector embeddings (floating-point arrays) representing the semantic content of the input text(s). Each input string maps to one embedding vector usable for similarity search, clustering, classification, or retrieval-augmented generation.","failureModes":["Invalid or unsupported model name returns an error — use GET /api/models to list valid options","Empty string input rejected due to minLength:1 constraint","Batch exceeding 64 items rejected due to maxItems:64 constraint","Payment failure or insufficient USDC balance blocks the request","Malformed input schema (wrong type) returns a validation error","Network timeout if the upstream Bedrock model is slow to respond"],"whenToPreferThis":"Choose this endpoint when you need text embeddings from Amazon Bedrock models (e.g. Titan Embeddings) without managing your own AWS credentials or infrastructure, and you want pay-per-call micropayment billing via USDC/x402 rather than a subscription. Ideal for agents that need on-demand embeddings for RAG pipelines, semantic search, or similarity tasks.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-12T06:41:23.401Z","isFirstParty":false,"canonicalSlug":"aws-bedrock-embeddings-api-x402-ee53f8ad"}