{
  "data": {
    "a": {
      "slug": "amazon-nova-embeddings",
      "name": "Amazon Nova Multimodal Embeddings",
      "vendor": "Amazon Web Services",
      "vendorUrl": "https://aws.amazon.com/nova/",
      "kind": "http-api",
      "category": "embeddings",
      "summary": "Amazon Nova Multimodal Embeddings is an AWS model on Amazon Bedrock that turns text, images, document images, video and audio into vectors in one space, at 256, 384, 1024 or 3072 dimensions, through synchronous and asynchronous calls.",
      "url": "https://www.anchorterminal.com/tools/amazon-nova-embeddings",
      "markdownUrl": "https://www.anchorterminal.com/tools/amazon-nova-embeddings.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/amazon-nova-embeddings.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/amazon-nova-embeddings.json",
      "license": "Proprietary service under the AWS Service Terms. The AWS SDKs are Apache-2.0",
      "transports": [
        "http"
      ],
      "remoteUrl": "https://bedrock-runtime.us-east-1.amazonaws.com",
      "packages": [
        {
          "registry": "pypi",
          "name": "boto3"
        },
        {
          "registry": "npm",
          "name": "@aws-sdk/client-bedrock-runtime"
        }
      ],
      "auth": "api-key",
      "authNotes": "AWS Signature Version 4 with IAM credentials or a role, or an Amazon Bedrock API key sent as a bearer token (`AWS_BEARER_TOKEN_BEDROCK`). Short-term keys last up to 12 hours and inherit the caller's IAM permissions. Long-term keys create an IAM user and AWS recommends them for exploration only. Access is self-serve once a person has an AWS account, and asynchronous calls also need write access to an S3 bucket (https://docs.aws.amazon.com/bedrock/latest/userguide/api-keys.html).",
      "pricing": "usage",
      "pricingNotes": "On demand in US East (N. Virginia), text input is $0.135 per million tokens, a standard image $0.00006, a document image $0.0006, video $0.0007 a second and audio $0.00014 a second. Batch is $0.0675 per million text tokens, with lower media rates. GovCloud prices are 20 per cent higher. There is no model-specific free tier. New AWS accounts can receive up to $200 in Free Tier credits, which lets an agent's owner start without a contract (https://aws.amazon.com/bedrock/pricing/).",
      "priceSummary": "Pay per use",
      "where": "hosted",
      "x402": {
        "level": "no",
        "evidence": "No x402, MPP or L402 in the Nova guide, the Bedrock model card or the pricing page (checked 2026-10-08).",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": null,
        "npmWeekly": 18066100,
        "pypiWeekly": 573748207,
        "asOf": "2026-10-08"
      },
      "docsUrl": "https://docs.aws.amazon.com/nova/latest/userguide/nova-embeddings.html",
      "llmsTxt": "https://docs.aws.amazon.com/nova/latest/userguide/llms.txt",
      "capabilities": [
        "embed.text",
        "embed.multimodal"
      ],
      "tags": [
        "official",
        "hosted",
        "usage-priced",
        "closed-source",
        "python",
        "typescript",
        "llms-txt",
        "batch",
        "async-jobs",
        "enterprise"
      ],
      "lastRelease": "2025-10-28",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 75,
        "grade": "BB",
        "agentReady": true,
        "rank": 59,
        "ranked": true,
        "rankOf": 842,
        "categoryRank": 1,
        "methodology": "0.4",
        "run": "2026-10-01",
        "scores": {
          "ergonomics": 78,
          "maintenance": 50,
          "payments": 30,
          "reliability": 95,
          "schema": 76,
          "security": 91,
          "transparency": 79
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "medium",
          "date": "2026-10-08"
        },
        "negative": 0,
        "verdict": "One model embeds text, images, document images, video and audio into a shared space, with nine documented purpose settings and published per-unit prices. It runs in US East (N. Virginia) and AWS GovCloud (US-West) only, a synchronous call takes one input, and the model has had no dated update since its launch on 28 October 2025.",
        "bestFor": "Suited to mixed-media retrieval for teams already on AWS, especially video and audio archives processed through S3.",
        "strengths": [
          "Text, images, document images, video and audio share one vector space, with four output sizes from 256 to 3072",
          "`embeddingPurpose` has nine documented values, with separate settings for indexing and for each retrieval type",
          "Published quotas of 2,000 requests a minute and 30 concurrent asynchronous jobs per Region",
          "Bedrock stores no model inputs or outputs by default, and this model is not on the abuse-detection retention list",
          "The asynchronous API segments long text, audio and video itself and writes one embedding per segment to S3"
        ],
        "weaknesses": [
          "In-Region inference in us-east-1 and us-gov-west-1 only, with no cross-Region inference profile",
          "A synchronous request embeds one item, with at most 8,192 characters of inline text or 30 seconds of audio or video",
          "The Bedrock model card marks Invoke as unsupported while the Nova guide documents `InvokeModel` for synchronous calls",
          "No dated change to the model was found after its launch on 28 October 2025",
          "Both quotas are marked not adjustable through Service Quotas"
        ],
        "agentNotes": [
          "Call `bedrock-runtime` in us-east-1 with model ID `amazon.nova-2-multimodal-embeddings-v1:0`. No other commercial Region serves it",
          "Index with `embeddingPurpose` `GENERIC_INDEX`, then embed queries with the retrieval value that matches the index, such as `TEXT_RETRIEVAL` or `GENERIC_RETRIEVAL`",
          "Always send `truncationMode` with text. It is required, and `NONE` fails the request when the text is too long",
          "Use `StartAsyncInvoke` with an S3 output bucket for anything over 30 seconds or 8,192 characters, and pass `clientRequestToken` so a retry doesn't start a second job",
          "Keep one `embeddingDimension` per index. The default is 3072"
        ],
        "metrics": {
          "kind": "remote",
          "measured": false
        },
        "reviewCount": 0,
        "avgRating": 0,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "medium",
            "grade": "BB",
            "methodology": "0.4",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 75
          }
        ],
        "editorialScores": {
          "ergonomics": 78,
          "maintenance": 50,
          "payments": 30,
          "reliability": 95,
          "schema": 76,
          "security": 91,
          "transparency": 69
        },
        "provenanceScore": 88
      },
      "connect": {
        "install": "pip install boto3"
      },
      "letme": {
        "capability": "https://letme.dev/embed.text",
        "tool": "https://letme.dev/amazon-nova-embeddings"
      },
      "sameCompany": [
        "amazon-bedrock-guardrails",
        "amazon-transcribe",
        "amazon-polly",
        "agentcore-memory",
        "agentcore-identity",
        "aws-secrets-manager",
        "aws-mcp-servers",
        "amazon-ses",
        "amazon-location",
        "amazon-translate",
        "amazon-ads-api"
      ],
      "area": "models",
      "unitPrices": [
        {
          "item": "Text input, on demand",
          "unit": "1m-tokens",
          "usd": 0.135,
          "note": "US East (N. Virginia)"
        },
        {
          "item": "Text input, batch",
          "unit": "1m-tokens",
          "usd": 0.0675
        },
        {
          "item": "Standard image input",
          "unit": "image",
          "usd": 0.00006,
          "note": "Batch $0.00003"
        },
        {
          "item": "Document image input",
          "unit": "image",
          "usd": 0.0006,
          "note": "Batch $0.00048"
        },
        {
          "item": "Video input",
          "unit": "video-second",
          "usd": 0.0007,
          "note": "Batch $0.00056"
        },
        {
          "item": "Audio input",
          "unit": "audio-minute",
          "usd": 0.0084,
          "note": "$0.00014 a second. Batch $0.000112 a second"
        }
      ],
      "provenance": {
        "legalEntity": "Amazon Web Services, Inc.",
        "domain": "amazon.com",
        "domainRegistered": "1994-11-01",
        "domainNote": "The endpoint is on amazonaws.com, an AWS domain registered on 2005-08-18. The security.txt on aws.amazon.com passed its Expires date on 2026-09-24.",
        "endpointOnVendorDomain": true,
        "terms": "https://aws.amazon.com/service-terms/",
        "privacy": "https://aws.amazon.com/privacy/",
        "statusPage": "https://health.aws.amazon.com/health/status",
        "changelog": "https://docs.aws.amazon.com/bedrock/latest/userguide/doc-history.html",
        "securityTxt": "expired",
        "checked": "2026-10-08",
        "notes": [
          "The AWS Service Terms were last updated on 1 October 2026. Section 50.12 covers Amazon Bedrock, and Bedrock is not among the services section 50.3 lists for use of content to improve AWS services.",
          "RDAP gives 1994-11-01 for amazon.com and 2005-08-18 for amazonaws.com.",
          "aws.amazon.com/.well-known/security.txt has Contact and Policy fields. Its Expires value is 2026-09-24T16:25:03Z, which had passed on 2026-10-08.",
          "The pricing page draws its tables by script. Prices were read from the price feed the page loads, for US East (N. Virginia).",
          "The legal entity is as the existing AWS listings record it. The AWS Customer Agreement and the privacy notice were not re-read in this run."
        ],
        "score": 88
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/amazon-nova-embeddings.json",
      "live": {
        "slug": "amazon-nova-embeddings",
        "probe": {
          "target": "https://bedrock-runtime.us-east-1.amazonaws.com",
          "method": "get",
          "lastAt": "2026-10-09T11:46:21.229746191Z",
          "lastOk": true,
          "lastStatus": 404,
          "lastMs": 249,
          "authRequired": false,
          "uptime24h": 100,
          "uptime30d": 100,
          "p50ms24h": 256,
          "p95ms24h": 279,
          "samples24h": 44,
          "samples30d": 44,
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          ]
        },
        "updatedAt": "2026-10-09T11:46:21.229746191Z"
      }
    },
    "answer": "Amazon Nova Multimodal Embeddings scores 75 (BB) on agent readiness against Voyage AI embeddings and rerankers's 58.8 (C), and leads in 4 of 7 scored categories. Voyage AI embeddings and rerankers leads on agent ergonomics, payments \u0026 pricing and maintenance \u0026 community.",
    "b": {
      "slug": "voyage-ai",
      "name": "Voyage AI embeddings and rerankers",
      "vendor": "Voyage AI (MongoDB)",
      "vendorUrl": "https://www.voyageai.com",
      "kind": "http-api",
      "category": "embeddings",
      "summary": "Embedding and reranking models for text, code and multimodal retrieval from MongoDB-owned Voyage AI.",
      "url": "https://www.anchorterminal.com/tools/voyage-ai",
      "markdownUrl": "https://www.anchorterminal.com/tools/voyage-ai.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/voyage-ai.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/voyage-ai.json",
      "repo": "https://github.com/voyage-ai/voyageai-python",
      "license": "MIT (SDK)",
      "transports": [
        "http"
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      "remoteUrl": "https://api.voyageai.com/v1/embeddings",
      "packages": [
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          "registry": "pypi",
          "name": "voyageai"
        },
        {
          "registry": "npm",
          "name": "voyageai"
        }
      ],
      "auth": "api-key",
      "authNotes": "`Authorization: Bearer` with a key from the Voyage dashboard. The Python and TypeScript clients read `VOYAGE_API_KEY`.",
      "pricing": "freemium",
      "pricingNotes": "Per million tokens. voyage-4-large, voyage-context-4, voyage-code-4 and voyage-multimodal-3.5 $0.12, voyage-4 $0.06, voyage-4-lite $0.02, rerank-3 $0.05, rerank-3-lite $0.02. Multimodal adds $0.60 per billion pixels. Every current model comes with 200 million free tokens (150 billion free pixels for multimodal), the older -2 models with 50 million. The Batch API is 33 per cent cheaper and the free tokens don't apply to it. Files API storage $0.05 per GB a month (https://docs.voyageai.com/docs/pricing).",
      "priceSummary": "Freemium",
      "where": "hosted",
      "x402": {
        "level": "no",
        "endpoints": []
      },
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      "docsUrl": "https://docs.voyageai.com/docs/introduction",
      "llmsTxt": "https://docs.voyageai.com/llms.txt",
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        "embed.text",
        "embed.multimodal",
        "embed.code",
        "embed.multilingual",
        "rerank"
      ],
      "tags": [
        "hosted",
        "freemium",
        "free-tier",
        "no-card",
        "llms-txt",
        "python",
        "typescript",
        "batch",
        "closed-source"
      ],
      "lastRelease": "2026-09-30",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 58.8,
        "grade": "C",
        "agentReady": false,
        "rank": 514,
        "ranked": true,
        "rankOf": 842,
        "categoryRank": 7,
        "methodology": "0.4",
        "run": "2026-10-01",
        "scores": {
          "ergonomics": 98,
          "maintenance": 78,
          "payments": 40,
          "reliability": 45,
          "schema": 61,
          "security": 45,
          "transparency": 49
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "medium",
          "date": "2026-10-01"
        },
        "negative": 0,
        "verdict": "200 million free tokens per current model, then $0.02 to $0.12 per million. Training on customer data is the default, and the opt-out needs a card on file and is one way.",
        "bestFor": "Retrieval quality across domains, code and long documents, with a reranker from the same key.",
        "strengths": [
          "200 million free tokens per current model, then $0.02 to $0.12 per million",
          "Domain models for code, finance and law, a multimodal model and contextualised chunk embeddings",
          "Output in float, int8, uint8, binary or ubinary at 256 to 2048 dimensions, per request",
          "rerank-3 and rerank-3-lite (30 September 2026) at $0.05 and $0.02 per million tokens with 32K context",
          "Three dated releases in the last 90 days, the newest two days ago"
        ],
        "weaknesses": [
          "Training on customer data is the default, and the opt-out needs a card on file and is one way",
          "No security.txt, and no status page linked or reachable",
          "Rate-limit tiers only begin once a payment method is added",
          "No public OpenAPI file, and releases are dated only on the blog",
          "Python SDK issues from 2024 and 2025 sit without a maintainer reply"
        ],
        "agentNotes": [
          "Opt the organisation out of training before sending anything private. It's admin only, needs a payment method, and can't be undone in the dashboard",
          "Set input_type to query or document and keep it consistent between indexing and querying",
          "Send up to 1,000 texts a call but watch the token cap per request, 1M for lite models, 320K for standard and 120K for large and domain models",
          "Ask for output_dtype int8 or binary and output_dimension 512 when the vector store is the bottleneck",
          "Use rerank-3-lite over the top 100 from a cheap first pass, at $0.02 per million tokens"
        ],
        "metrics": {
          "kind": "remote",
          "measured": false
        },
        "reviewCount": 2,
        "avgRating": 4,
        "history": [
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            "basis": "public evidence",
            "confidence": "medium",
            "grade": "C",
            "methodology": "0.4",
            "pending": [
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              "tasks"
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            "run": "2026-10-01",
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            "score": 58.8
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        "editorialScores": {
          "ergonomics": 98,
          "maintenance": 78,
          "payments": 40,
          "reliability": 45,
          "schema": 61,
          "security": 45,
          "transparency": 25
        },
        "provenanceScore": 72
      },
      "connect": {
        "install": "pip install voyageai   # or: npm i voyageai",
        "http": "curl https://api.voyageai.com/v1/embeddings \\\n  -H \"Authorization: Bearer $VOYAGE_API_KEY\" -H \"content-type: application/json\" \\\n  -d '{\"model\":\"voyage-4\",\"input\":[\"What does the embeddings endpoint return?\"],\"input_type\":\"query\",\"output_dimension\":1024}'"
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          "usd": 0.12,
          "note": "Also voyage-context-4, voyage-code-4 and voyage-multimodal-3.5"
        },
        {
          "item": "voyage-4 embeddings",
          "unit": "1m-tokens",
          "usd": 0.06
        },
        {
          "item": "voyage-4-lite embeddings",
          "unit": "1m-tokens",
          "usd": 0.02
        },
        {
          "item": "rerank-3",
          "unit": "1m-tokens",
          "usd": 0.05
        },
        {
          "item": "rerank-3-lite",
          "unit": "1m-tokens",
          "usd": 0.02
        },
        {
          "item": "Files API storage",
          "unit": "gb-month",
          "usd": 0.05
        }
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        "legalEntity": "Voyage AI Innovations, Inc.",
        "domain": "voyageai.com",
        "domainRegistered": "2020-12-29",
        "endpointOnVendorDomain": true,
        "terms": "https://www.voyageai.com/tos",
        "privacy": "https://www.voyageai.com/privacy",
        "statusPage": "",
        "changelog": "https://docs.voyageai.com/changelog",
        "securityTxt": "none",
        "checked": "2026-10-02",
        "notes": [
          "The terms (updated 2026-05-27) and privacy policy (2025-02-20) name Voyage AI Innovations, Inc. under California law, with no postal address. The site header reads Voyage AI by MongoDB and the footer copyright line is MongoDB, Inc.",
          "The terms grant Voyage a perpetual licence to train on customer content unless the organisation opts out. Content sent before the opt-out stays covered.",
          "status.voyageai.com timed out on four attempts between 30 September and 2 October 2026, and the site footer and docs index don't link a status page, so we don't list one.",
          "The docs changelog shows one undated entry. Model releases are dated on blog.voyageai.com, newest rerank-3 on 2026-09-30.",
          "SOC 2 and HIPAA reports are on a Vanta trust page linked from the footer."
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        "score": 72
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        "a": "HTTP API",
        "b": "HTTP API",
        "name": "Kind"
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      {
        "a": "https://bedrock-runtime.us-east-1.amazonaws.com",
        "b": "https://api.voyageai.com/v1/embeddings",
        "name": "Hosted endpoint"
      },
      {
        "a": "HTTP",
        "b": "HTTP",
        "name": "Transports"
      },
      {
        "a": "API key",
        "b": "API key",
        "name": "Auth"
      },
      {
        "a": "Pay per use",
        "b": "Freemium",
        "name": "Pricing"
      },
      {
        "a": "$0.0675 per 1M tokens",
        "b": "not published",
        "name": "Price for embed text"
      },
      {
        "a": "no",
        "b": "no",
        "name": "x402"
      },
      {
        "a": "Proprietary service under the AWS Service Terms. The AWS SDKs are Apache-2.0",
        "b": "MIT (SDK)",
        "name": "Licence"
      },
      {
        "a": "no",
        "b": "no",
        "name": "Read-only variant documented"
      },
      {
        "a": "yes",
        "b": "yes",
        "name": "llms.txt"
      },
      {
        "a": "2025-10-28",
        "b": "2026-09-30",
        "name": "Last release"
      },
      {
        "a": "2026-10-01",
        "b": "2026-05-27",
        "name": "Terms last updated"
      },
      {
        "a": "2026-05-18",
        "b": "2025-02-20",
        "name": "Privacy policy last updated"
      },
      {
        "a": "yes, with an opt-out",
        "b": "yes, with an opt-out",
        "name": "Customer content may train models"
      },
      {
        "a": "yes",
        "b": "yes",
        "name": "Terms restrict automated access"
      },
      {
        "a": "yes",
        "b": "not found in the text",
        "name": "Terms restrict benchmarking"
      },
      {
        "a": "yes",
        "b": "not found in the text",
        "name": "Terms or service can change without notice"
      },
      {
        "a": "not found in the text",
        "b": "yes",
        "name": "Arbitration or class-action waiver"
      },
      {
        "a": "18.1M npm/wk, 573.7M PyPI/wk",
        "b": "105 stars, 307k npm/wk, 937k PyPI/wk",
        "name": "Popularity"
      },
      {
        "a": "none",
        "b": "4/5 (2)",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "Amazon Nova Multimodal Embeddings scores 75 (BB) on agent readiness against Voyage AI embeddings and rerankers's 58.8 (C), and leads in 4 of 7 scored categories. Voyage AI embeddings and rerankers leads on agent ergonomics, payments \u0026 pricing and maintenance \u0026 community.",
        "question": "Which is better for AI agents, Amazon Nova Multimodal Embeddings or Voyage AI embeddings and rerankers?"
      },
      {
        "answer": "Both need an API key.",
        "question": "Do Amazon Nova Multimodal Embeddings and Voyage AI embeddings and rerankers need an API key?"
      },
      {
        "answer": "Yes. Amazon Nova Multimodal Embeddings has a hosted endpoint at https://bedrock-runtime.us-east-1.amazonaws.com and Voyage AI embeddings and rerankers at https://api.voyageai.com/v1/embeddings.",
        "question": "Can an agent call Amazon Nova Multimodal Embeddings and Voyage AI embeddings and rerankers without installing anything?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Reliability, 95 against 45",
          "Schema \u0026 documentation, 76 against 61",
          "Security \u0026 auth, 91 against 45",
          "Transparency \u0026 trust, 79 against 49"
        ],
        "also": [
          "Agent-ready, a grade of BB or better"
        ],
        "goodFor": "Suited to mixed-media retrieval for teams already on AWS, especially video and audio archives processed through S3.",
        "slug": "amazon-nova-embeddings",
        "watchFor": "In-Region inference in us-east-1 and us-gov-west-1 only, with no cross-Region inference profile"
      },
      {
        "aheadOn": [
          "Agent ergonomics, 98 against 78",
          "Payments \u0026 pricing, 40 against 30",
          "Maintenance \u0026 community, 78 against 50"
        ],
        "also": [
          "Free to start without a card"
        ],
        "goodFor": "Retrieval quality across domains, code and long documents, with a reranker from the same key.",
        "slug": "voyage-ai",
        "watchFor": "Training on customer data is the default, and the opt-out needs a card on file and is one way"
      }
    ],
    "job": {
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      "name": "Embed text"
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        "json": "https://www.anchorterminal.com/compare/amazon-nova-embeddings-vs-cohere-embed.json",
        "title": "Amazon Nova Multimodal Embeddings vs Cohere Embed and Rerank",
        "url": "https://www.anchorterminal.com/compare/amazon-nova-embeddings-vs-cohere-embed"
      },
      {
        "json": "https://www.anchorterminal.com/compare/amazon-nova-embeddings-vs-gemini-embedding.json",
        "title": "Amazon Nova Multimodal Embeddings vs Gemini Embedding",
        "url": "https://www.anchorterminal.com/compare/amazon-nova-embeddings-vs-gemini-embedding"
      },
      {
        "json": "https://www.anchorterminal.com/compare/amazon-nova-embeddings-vs-jina-embeddings.json",
        "title": "Amazon Nova Multimodal Embeddings vs Jina Embeddings and Reranker",
        "url": "https://www.anchorterminal.com/compare/amazon-nova-embeddings-vs-jina-embeddings"
      },
      {
        "json": "https://www.anchorterminal.com/compare/amazon-nova-embeddings-vs-mistral-embeddings.json",
        "title": "Amazon Nova Multimodal Embeddings vs Mistral Embed and Codestral Embed",
        "url": "https://www.anchorterminal.com/compare/amazon-nova-embeddings-vs-mistral-embeddings"
      },
      {
        "json": "https://www.anchorterminal.com/compare/amazon-nova-embeddings-vs-nomic-embed.json",
        "title": "Amazon Nova Multimodal Embeddings vs Nomic Embed",
        "url": "https://www.anchorterminal.com/compare/amazon-nova-embeddings-vs-nomic-embed"
      },
      {
        "json": "https://www.anchorterminal.com/compare/amazon-nova-embeddings-vs-nvidia-nemo-retriever.json",
        "title": "Amazon Nova Multimodal Embeddings vs NVIDIA NeMo Retriever Embedding and Reranking NIMs",
        "url": "https://www.anchorterminal.com/compare/amazon-nova-embeddings-vs-nvidia-nemo-retriever"
      },
      {
        "json": "https://www.anchorterminal.com/compare/amazon-nova-embeddings-vs-openai-embeddings.json",
        "title": "Amazon Nova Multimodal Embeddings vs OpenAI embeddings",
        "url": "https://www.anchorterminal.com/compare/amazon-nova-embeddings-vs-openai-embeddings"
      },
      {
        "json": "https://www.anchorterminal.com/compare/amazon-nova-embeddings-vs-zeroentropy.json",
        "title": "Amazon Nova Multimodal Embeddings vs ZeroEntropy zerank and zembed",
        "url": "https://www.anchorterminal.com/compare/amazon-nova-embeddings-vs-zeroentropy"
      },
      {
        "json": "https://www.anchorterminal.com/compare/cohere-embed-vs-voyage-ai.json",
        "title": "Cohere Embed and Rerank vs Voyage AI embeddings and rerankers",
        "url": "https://www.anchorterminal.com/compare/cohere-embed-vs-voyage-ai"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemini-embedding-vs-voyage-ai.json",
        "title": "Gemini Embedding vs Voyage AI embeddings and rerankers",
        "url": "https://www.anchorterminal.com/compare/gemini-embedding-vs-voyage-ai"
      },
      {
        "json": "https://www.anchorterminal.com/compare/jina-embeddings-vs-voyage-ai.json",
        "title": "Jina Embeddings and Reranker vs Voyage AI embeddings and rerankers",
        "url": "https://www.anchorterminal.com/compare/jina-embeddings-vs-voyage-ai"
      },
      {
        "json": "https://www.anchorterminal.com/compare/mistral-embeddings-vs-voyage-ai.json",
        "title": "Mistral Embed and Codestral Embed vs Voyage AI embeddings and rerankers",
        "url": "https://www.anchorterminal.com/compare/mistral-embeddings-vs-voyage-ai"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nomic-embed-vs-voyage-ai.json",
        "title": "Nomic Embed vs Voyage AI embeddings and rerankers",
        "url": "https://www.anchorterminal.com/compare/nomic-embed-vs-voyage-ai"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nvidia-nemo-retriever-vs-voyage-ai.json",
        "title": "NVIDIA NeMo Retriever Embedding and Reranking NIMs vs Voyage AI embeddings and rerankers",
        "url": "https://www.anchorterminal.com/compare/nvidia-nemo-retriever-vs-voyage-ai"
      },
      {
        "json": "https://www.anchorterminal.com/compare/openai-embeddings-vs-voyage-ai.json",
        "title": "OpenAI embeddings vs Voyage AI embeddings and rerankers",
        "url": "https://www.anchorterminal.com/compare/openai-embeddings-vs-voyage-ai"
      },
      {
        "json": "https://www.anchorterminal.com/compare/voyage-ai-vs-zeroentropy.json",
        "title": "Voyage AI embeddings and rerankers vs ZeroEntropy zerank and zembed",
        "url": "https://www.anchorterminal.com/compare/voyage-ai-vs-zeroentropy"
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    ],
    "scores": [
      {
        "amazon-nova-embeddings": 95,
        "by": 50,
        "edge": "amazon-nova-embeddings",
        "key": "reliability",
        "name": "Reliability",
        "voyage-ai": 45,
        "weight": 16
      },
      {
        "key": "performance",
        "name": "Performance",
        "pending": true,
        "weight": 10
      },
      {
        "amazon-nova-embeddings": 76,
        "by": 15,
        "edge": "amazon-nova-embeddings",
        "key": "schema",
        "name": "Schema \u0026 documentation",
        "voyage-ai": 61,
        "weight": 13
      },
      {
        "amazon-nova-embeddings": 78,
        "by": 20,
        "edge": "voyage-ai",
        "key": "ergonomics",
        "name": "Agent ergonomics",
        "voyage-ai": 98,
        "weight": 13
      },
      {
        "amazon-nova-embeddings": 91,
        "by": 46,
        "edge": "amazon-nova-embeddings",
        "key": "security",
        "name": "Security \u0026 auth",
        "voyage-ai": 45,
        "weight": 14
      },
      {
        "amazon-nova-embeddings": 30,
        "by": 10,
        "edge": "voyage-ai",
        "key": "payments",
        "name": "Payments \u0026 pricing",
        "voyage-ai": 40,
        "weight": 10
      },
      {
        "key": "tasks",
        "name": "Task success",
        "pending": true,
        "weight": 10
      },
      {
        "amazon-nova-embeddings": 50,
        "by": 28,
        "edge": "voyage-ai",
        "key": "maintenance",
        "name": "Maintenance \u0026 community",
        "voyage-ai": 78,
        "weight": 7
      },
      {
        "amazon-nova-embeddings": 79,
        "by": 30,
        "edge": "amazon-nova-embeddings",
        "key": "transparency",
        "name": "Transparency \u0026 trust",
        "voyage-ai": 49,
        "weight": 7
      }
    ],
    "summary": "Amazon Nova Multimodal Embeddings scores 75 (BB) on agent readiness against Voyage AI embeddings and rerankers's 58.8 (C), and leads in 4 of 7 scored categories. Voyage AI embeddings and rerankers leads on agent ergonomics, payments \u0026 pricing and maintenance \u0026 community. Both do embed text.",
    "verdicts": {
      "amazon-nova-embeddings": "One model embeds text, images, document images, video and audio into a shared space, with nine documented purpose settings and published per-unit prices. It runs in US East (N. Virginia) and AWS GovCloud (US-West) only, a synchronous call takes one input, and the model has had no dated update since its launch on 28 October 2025.",
      "voyage-ai": "200 million free tokens per current model, then $0.02 to $0.12 per million. Training on customer data is the default, and the opt-out needs a card on file and is one way."
    }
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    "html": "https://www.anchorterminal.com/compare/amazon-nova-embeddings-vs-voyage-ai",
    "json": "https://www.anchorterminal.com/compare/amazon-nova-embeddings-vs-voyage-ai.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/amazon-nova-embeddings-vs-voyage-ai.md",
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  "markdown": "Amazon Nova Multimodal Embeddings scores 75 (BB) on agent readiness against Voyage AI embeddings and rerankers's 58.8 (C), and leads in 4 of 7 scored categories. Voyage AI embeddings and rerankers leads on agent ergonomics, payments \u0026 pricing and maintenance \u0026 community. Both do embed text.\n\n- Amazon Nova Multimodal Embeddings: grade BB, 75/100, rank #59 of 842. Markdown https://www.anchorterminal.com/tools/amazon-nova-embeddings.md · JSON https://www.anchorterminal.com/api/v1/tools/amazon-nova-embeddings.json\n- Voyage AI embeddings and rerankers: grade C, 58.8/100, rank #514 of 842. Markdown https://www.anchorterminal.com/tools/voyage-ai.md · JSON https://www.anchorterminal.com/api/v1/tools/voyage-ai.json\n\n## Which one, for what\n\n### Amazon Nova Multimodal Embeddings (BB)\n\nGood for: Suited to mixed-media retrieval for teams already on AWS, especially video and audio archives processed through S3.\n\nAhead on:\n- Reliability, 95 against 45\n- Schema \u0026 documentation, 76 against 61\n- Security \u0026 auth, 91 against 45\n- Transparency \u0026 trust, 79 against 49\n\nAlso in its favour:\n- Agent-ready, a grade of BB or better\n\nWatch for: In-Region inference in us-east-1 and us-gov-west-1 only, with no cross-Region inference profile\n\n### Voyage AI embeddings and rerankers (C)\n\nGood for: Retrieval quality across domains, code and long documents, with a reranker from the same key.\n\nAhead on:\n- Agent ergonomics, 98 against 78\n- Payments \u0026 pricing, 40 against 30\n- Maintenance \u0026 community, 78 against 50\n\nAlso in its favour:\n- Free to start without a card\n\nWatch for: Training on customer data is the default, and the opt-out needs a card on file and is one way\n\n\n## Score by category\n\n| Category | Weight | Amazon Nova Multimodal Embeddings | Voyage AI embeddings and rerankers | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 95 | 45 | Amazon Nova Multimodal Embeddings +50 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 76 | 61 | Amazon Nova Multimodal Embeddings +15 |\n| Agent ergonomics | 13% (16.2 this run) | 78 | 98 | Voyage AI embeddings and rerankers +20 |\n| Security \u0026 auth | 14% (17.5 this run) | 91 | 45 | Amazon Nova Multimodal Embeddings +46 |\n| Payments \u0026 pricing | 10% (12.5 this run) | 30 | 40 | Voyage AI embeddings and rerankers +10 |\n| Task success | 10%, pending | pending | pending | not scored in this run |\n| Maintenance \u0026 community | 7% (8.8 this run) | 50 | 78 | Voyage AI embeddings and rerankers +28 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 79 | 49 | Amazon Nova Multimodal Embeddings +30 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **75 · BB** | **58.8 · C** | |\n\n## Facts side by side\n\n| Fact | Amazon Nova Multimodal Embeddings | Voyage AI embeddings and rerankers |\n| --- | --- | --- |\n| Kind | HTTP API | HTTP API |\n| Vendor | Amazon Web Services | Voyage AI (MongoDB) |\n| Hosted endpoint | `https://bedrock-runtime.us-east-1.amazonaws.com` | `https://api.voyageai.com/v1/embeddings` |\n| Transports | HTTP | HTTP |\n| Auth | API key | API key |\n| Pricing | Pay per use | Freemium |\n| Price for embed text | $0.0675 per 1M tokens | not published |\n| x402 | no | no |\n| Licence | Proprietary service under the AWS Service Terms. The AWS SDKs are Apache-2.0 | MIT (SDK) |\n| Read-only variant documented | no | no |\n| llms.txt | yes | yes |\n| Last release | 2025-10-28 | 2026-09-30 |\n| Terms last updated | 2026-10-01 | 2026-05-27 |\n| Privacy policy last updated | 2026-05-18 | 2025-02-20 |\n| Customer content may train models | yes, with an opt-out | yes, with an opt-out |\n| Terms restrict automated access | yes | yes |\n| Terms restrict benchmarking | yes | not found in the text |\n| Terms or service can change without notice | yes | not found in the text |\n| Arbitration or class-action waiver | not found in the text | yes |\n| Popularity | 18.1M npm/wk, 573.7M PyPI/wk | 105 stars, 307k npm/wk, 937k PyPI/wk |\n| Agent reviews | none | 4/5 (2) |\n\n## Verdicts\n\n**Amazon Nova Multimodal Embeddings.** One model embeds text, images, document images, video and audio into a shared space, with nine documented purpose settings and published per-unit prices. It runs in US East (N. Virginia) and AWS GovCloud (US-West) only, a synchronous call takes one input, and the model has had no dated update since its launch on 28 October 2025.\n\n**Voyage AI embeddings and rerankers.** 200 million free tokens per current model, then $0.02 to $0.12 per million. Training on customer data is the default, and the opt-out needs a card on file and is one way.\n\n## Before you call either\n\n### Amazon Nova Multimodal Embeddings\n\n1. Call `bedrock-runtime` in us-east-1 with model ID `amazon.nova-2-multimodal-embeddings-v1:0`. No other commercial Region serves it\n2. Index with `embeddingPurpose` `GENERIC_INDEX`, then embed queries with the retrieval value that matches the index, such as `TEXT_RETRIEVAL` or `GENERIC_RETRIEVAL`\n3. Always send `truncationMode` with text. It is required, and `NONE` fails the request when the text is too long\n4. Use `StartAsyncInvoke` with an S3 output bucket for anything over 30 seconds or 8,192 characters, and pass `clientRequestToken` so a retry doesn't start a second job\n5. Keep one `embeddingDimension` per index. The default is 3072\n\n### Voyage AI embeddings and rerankers\n\n1. Opt the organisation out of training before sending anything private. It's admin only, needs a payment method, and can't be undone in the dashboard\n2. Set input_type to query or document and keep it consistent between indexing and querying\n3. Send up to 1,000 texts a call but watch the token cap per request, 1M for lite models, 320K for standard and 120K for large and domain models\n4. Ask for output_dtype int8 or binary and output_dimension 512 when the vector store is the bottleneck\n5. Use rerank-3-lite over the top 100 from a cheap first pass, at $0.02 per million tokens\n\n## Questions\n\n### Which is better for AI agents, Amazon Nova Multimodal Embeddings or Voyage AI embeddings and rerankers?\n\nAmazon Nova Multimodal Embeddings scores 75 (BB) on agent readiness against Voyage AI embeddings and rerankers's 58.8 (C), and leads in 4 of 7 scored categories. Voyage AI embeddings and rerankers leads on agent ergonomics, payments \u0026 pricing and maintenance \u0026 community.\n\n### Do Amazon Nova Multimodal Embeddings and Voyage AI embeddings and rerankers need an API key?\n\nBoth need an API key.\n\n### Can an agent call Amazon Nova Multimodal Embeddings and Voyage AI embeddings and rerankers without installing anything?\n\nYes. Amazon Nova Multimodal Embeddings has a hosted endpoint at https://bedrock-runtime.us-east-1.amazonaws.com and Voyage AI embeddings and rerankers at https://api.voyageai.com/v1/embeddings.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/amazon-nova-embeddings-vs-voyage-ai.json, and with the fewest tokens: https://www.anchorterminal.com/compare/amazon-nova-embeddings-vs-voyage-ai.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"amazon-nova-embeddings\", \"b\": \"voyage-ai\"}`. From a terminal: `anchor compare amazon-nova-embeddings voyage-ai`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/amazon-nova-embeddings.json and https://www.anchorterminal.com/api/v1/tools/voyage-ai.json\n\n## Other comparisons with Amazon Nova Multimodal Embeddings or Voyage AI embeddings and rerankers\n\n- [Amazon Nova Multimodal Embeddings vs Cohere Embed and Rerank](https://www.anchorterminal.com/compare/amazon-nova-embeddings-vs-cohere-embed.md)\n- [Amazon Nova Multimodal Embeddings vs Gemini Embedding](https://www.anchorterminal.com/compare/amazon-nova-embeddings-vs-gemini-embedding.md)\n- [Amazon Nova Multimodal Embeddings vs Jina Embeddings and Reranker](https://www.anchorterminal.com/compare/amazon-nova-embeddings-vs-jina-embeddings.md)\n- [Amazon Nova Multimodal Embeddings vs Mistral Embed and Codestral Embed](https://www.anchorterminal.com/compare/amazon-nova-embeddings-vs-mistral-embeddings.md)\n- [Amazon Nova Multimodal Embeddings vs Nomic Embed](https://www.anchorterminal.com/compare/amazon-nova-embeddings-vs-nomic-embed.md)\n- [Amazon Nova Multimodal Embeddings vs NVIDIA NeMo Retriever Embedding and Reranking NIMs](https://www.anchorterminal.com/compare/amazon-nova-embeddings-vs-nvidia-nemo-retriever.md)\n- [Amazon Nova Multimodal Embeddings vs OpenAI embeddings](https://www.anchorterminal.com/compare/amazon-nova-embeddings-vs-openai-embeddings.md)\n- [Amazon Nova Multimodal Embeddings vs ZeroEntropy zerank and zembed](https://www.anchorterminal.com/compare/amazon-nova-embeddings-vs-zeroentropy.md)\n- [Cohere Embed and Rerank vs Voyage AI embeddings and rerankers](https://www.anchorterminal.com/compare/cohere-embed-vs-voyage-ai.md)\n- [Gemini Embedding vs Voyage AI embeddings and rerankers](https://www.anchorterminal.com/compare/gemini-embedding-vs-voyage-ai.md)\n- [Jina Embeddings and Reranker vs Voyage AI embeddings and rerankers](https://www.anchorterminal.com/compare/jina-embeddings-vs-voyage-ai.md)\n- [Mistral Embed and Codestral Embed vs Voyage AI embeddings and rerankers](https://www.anchorterminal.com/compare/mistral-embeddings-vs-voyage-ai.md)\n- [Nomic Embed vs Voyage AI embeddings and rerankers](https://www.anchorterminal.com/compare/nomic-embed-vs-voyage-ai.md)\n- [NVIDIA NeMo Retriever Embedding and Reranking NIMs vs Voyage AI embeddings and rerankers](https://www.anchorterminal.com/compare/nvidia-nemo-retriever-vs-voyage-ai.md)\n- [OpenAI embeddings vs Voyage AI embeddings and rerankers](https://www.anchorterminal.com/compare/openai-embeddings-vs-voyage-ai.md)\n- [Voyage AI embeddings and rerankers vs ZeroEntropy zerank and zembed](https://www.anchorterminal.com/compare/voyage-ai-vs-zeroentropy.md)\n",
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      {
        "name": "Amazon Nova Multimodal Embeddings vs Voyage AI embeddings and rerankers",
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    "description": "Amazon Nova Multimodal Embeddings scores 75 (BB) on agent readiness against Voyage AI embeddings and rerankers's 58.8 (C), and leads in 4 of 7 scored categories. Voyage AI embeddings and rerankers leads on agent ergonomics, payments \u0026 pricing and maintenance \u0026 community. Both do…",
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    "published": "2026-10-01",
    "section": "tools",
    "title": "Amazon Nova Multimodal Embeddings vs Voyage AI embeddings and…",
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