{ "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": "2026-10-09T10:42:35.138522771Z", "lastOk": true, "lastStatus": 404, "lastMs": 261, "authRequired": false, "uptime24h": 100, "uptime30d": 100, "p50ms24h": 258, "p95ms24h": 279, "samples24h": 33, "samples30d": 33, "days": [ { "date": "2026-10-09", "probes": 33, "ok": 33 } ] }, "updatedAt": "2026-10-09T10:42:35.138522771Z" } }, "answer": "Amazon Nova Multimodal Embeddings scores 75 (BB) on agent readiness against NVIDIA NeMo Retriever Embedding and Reranking NIMs's 61 (C), and leads in 4 of 7 scored categories. NVIDIA NeMo Retriever Embedding and Reranking NIMs leads on payments \u0026 pricing and maintenance \u0026 community.", "b": { "slug": "nvidia-nemo-retriever", "name": "NVIDIA NeMo Retriever Embedding and Reranking NIMs", "vendor": "NVIDIA", "vendorUrl": "https://www.nvidia.com", "kind": "http-api", "category": "embeddings", "summary": "NVIDIA's NeMo Retriever Embedding and Reranking NIMs are GPU containers that run text and image embedding models and rerankers behind a local REST API, with `/v1/embeddings` in the OpenAI shape and `/v1/ranking`.", "url": "https://www.anchorterminal.com/tools/nvidia-nemo-retriever", "markdownUrl": "https://www.anchorterminal.com/tools/nvidia-nemo-retriever.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/nvidia-nemo-retriever.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/nvidia-nemo-retriever.json", "license": "Proprietary containers under the NVIDIA Software Licence Agreement and Product-Specific Terms for AI Products. Models carry their own licences, such as OpenMDW 1.1 for `nvidia/nemotron-3-embed-1b` and the NVIDIA Open Model Licence for the Llama Nemotron models", "transports": [ "http" ], "packages": [ { "registry": "pypi", "name": "langchain-nvidia-ai-endpoints" } ], "auth": "none", "authNotes": "The NIM's own API takes no credential. NVIDIA's security page says the deployer must secure the endpoints and suggests a proxy with HTTPS. Pulling images from `nvcr.io` needs a personal NGC API key, created by a person at org.ngc.nvidia.com and sent as the password for the user `$oauthtoken`. Model weights download from Hugging Face by default with `HF_TOKEN`, or from NGC with `NGC_API_KEY`. Some models need their licence terms accepted on the NGC catalogue page first. TLS is built in through `NIM_SERVER_TLS_CERT_PATH` and `NIM_SERVER_TLS_KEY_PATH`.", "pricing": "freemium", "pricingNotes": "Free for research, development and testing on up to 16 GPUs through the NVIDIA developer programme, with no card. Production use needs NVIDIA AI Enterprise, listed at $4,500 a GPU a year through partners or $1 a GPU-hour on AWS, Azure, Google Cloud and Oracle marketplaces, plus the instance. A 90-day AI Enterprise trial licence is available on request. The hosted trial endpoints on build.nvidia.com are free for prototyping (https://docs.nvidia.com/ai-enterprise/planning-resource/licensing-guide/latest/pricing.html, checked 2026-10-08).", "priceSummary": "Freemium", "where": "local", "x402": { "level": "no", "evidence": "No x402, MPP or L402 in the NIM docs, the OpenAPI files or the AI Enterprise pricing page (checked 2026-10-08).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": 133630, "asOf": "2026-10-08" }, "docsUrl": "https://docs.nvidia.com/nim/nemo-retriever/embedding/latest/overview.html", "openapi": "https://docs.nvidia.com/nim/nemo-retriever/embedding/latest/_downloads/9957cfb9472fcf23c93820c1922c4343/openai-api.openapi.yaml", "capabilities": [ "embed.text", "embed.multimodal", "embed.multilingual", "rerank" ], "tags": [ "self-hosted", "docker", "kubernetes", "gpu", "openai-compatible", "openapi", "proprietary", "enterprise", "free-for-development", "multimodal" ], "lastRelease": "2026-08-05", "graded": true, "anchor": { "graded": true, "score": 61, "grade": "C", "agentReady": false, "rank": 439, "ranked": true, "rankOf": 842, "categoryRank": 6, "methodology": "0.4", "run": "2026-10-01", "scores": { "ergonomics": 73, "maintenance": 57, "payments": 40, "reliability": 53, "schema": 78, "security": 55, "transparency": 71 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-10-08" }, "negative": 0, "verdict": "Self-hosted containers with OpenAPI 3.1 files, typed request fields, five embedding output types and a dated end-of-life list. The API has no authentication or rate limiting of its own, production use needs an NVIDIA AI Enterprise licence at $4,500 a GPU a year, and the release notes carry no dates.", "bestFor": "Teams that already run NVIDIA GPUs and need embedding and reranking inside their own network, including page-image retrieval with the VL models.", "strengths": [ "OpenAPI 3.1 files for both services, with enums for `input_type`, `modality`, `embedding_type` and `truncate` and no extra properties allowed", "`/v1/embeddings` follows the OpenAI shape, and a `-query` or `-passage` model suffix replaces `input_type` for OpenAI clients", "Output can be shrunk with `dimensions` from 128 to 2048 on three models, or with `int8`, `uint8`, `binary` and `ubinary` types", "NGC images are signed, built for amd64 and arm64, and were last scanned on 5 October 2026 per the NGC registry record", "The AI Enterprise lifecycle pages give support periods per branch and an end-of-life table with dates" ], "weaknesses": [ "The API has no authentication and no rate limiting. The security page leaves both to a proxy the deployer runs", "Production use needs an NVIDIA AI Enterprise licence, $4,500 a GPU a year or $1 a GPU-hour on cloud marketplaces, plus the GPU", "Release notes carry no dates, and environment variable names changed between 2.0 and 2.3 without a note in the 2.2 or 2.3 notes we read", "The NVIDIA Software Licence Agreement forbids disclosing benchmark results without written permission, apart from a published exception", "Closed-source runtime with no public issue tracker or test suite, and no llms.txt entry or Markdown pages for these docs" ], "agentNotes": [ "Send `input_type` as `query` or `passage` on every embedding call. Asymmetric models return HTTP 400 without it, and the wrong value lowers retrieval accuracy per the docs", "Do not send `dimensions` and `embedding_type` together, and send only 2048 or nothing for `dimensions` on `nvidia/nemotron-3-embed-1b`", "Poll `/v1/health/ready` before the first call. The Docker health check can report unhealthy while the NIM is ready, per the 2.3 known issues", "Check the image tag on NGC before pulling. The guide uses `nemotron-3-embed-1b:2.3`, and NGC's record for that image listed tags up to 2.2.2 on 8 October 2026", "Put a proxy with authentication and TLS in front of port 8000, and sort `/v1/ranking` results yourself as the request has no top-n field" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "C", "methodology": "0.4", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 61 } ], "editorialScores": { "ergonomics": 73, "maintenance": 57, "payments": 40, "reliability": 53, "schema": 78, "security": 55, "transparency": 59 }, "provenanceScore": 83 }, "connect": { "install": "echo \"$NGC_API_KEY\" | docker login nvcr.io --username '$oauthtoken' --password-stdin\ndocker run -it --rm --runtime=nvidia --gpus all --shm-size=16GB -e HF_TOKEN -v ~/.cache/nim/cache:/opt/cache -v ~/.cache/nim/weights:/model -u $(id -u) -p 8000:8000 nvcr.io/nim/nvidia/nemotron-3-embed-1b:2.3", "http": "curl -X POST http://localhost:8000/v1/embeddings \\\n -H 'accept: application/json' -H 'Content-Type: application/json' \\\n -d '{\"input\":[\"What is NVIDIA?\"],\"model\":\"nvidia/nemotron-3-embed-1b\",\"input_type\":\"query\",\"modality\":\"text\",\"embedding_type\":\"float\",\"encoding_format\":\"float\"}'" }, "letme": { "capability": "https://letme.dev/embed.text", "tool": "https://letme.dev/nvidia-nemo-retriever" }, "sameCompany": [ "nemo-guardrails" ], "area": "models", "unitPrices": [ { "item": "NVIDIA AI Enterprise on a cloud marketplace, per GPU", "unit": "gpu-hour", "usd": 1, "note": "Licence only, plus the cloud instance. Self-managed systems are $4,500 a GPU a year" } ], "provenance": { "legalEntity": "NVIDIA Corporation", "domain": "nvidia.com", "domainRegistered": "1993-04-20", "domainNote": "Self-hosted software. The API answers on the deployer's own host, port 8000 by default. Images come from nvcr.io and the docs are on docs.nvidia.com.", "endpointOnVendorDomain": null, "terms": "https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-software-license-agreement/", "privacy": "https://www.nvidia.com/en-us/about-nvidia/privacy-policy/", "statusPage": "https://status.ngc.nvidia.com", "changelog": "https://docs.nvidia.com/nim/nemo-retriever/embedding/latest/release-notes.html", "securityTxt": "none", "checked": "2026-10-08", "notes": [ "The governing terms page for the Embedding NIM names the NVIDIA Software Licence Agreement (version of 7 May 2026) and the Product-Specific Terms for AI Products (15 April 2026) for the container, with a separate model licence per model.", "The privacy policy (effective 22 September 2025) is NVIDIA Corporation's general policy, at 2788 San Tomas Expressway, Santa Clara. The model cards name it as the applicable privacy policy. No product-specific privacy document was found.", "www.nvidia.com/.well-known/security.txt answers 403 with an access-denied body and www.nvidia.com/security.txt answers 404. Vulnerability reports go to NVIDIA PSIRT.", "status.ngc.nvidia.com covers NGC, the registry the images are pulled from, and NVIDIA Build. It does not cover a self-hosted NIM.", "RDAP for nvidia.com gives a registration date of 1993-04-20.", "The hosted trial endpoints are on integrate.api.nvidia.com and ai.api.nvidia.com, under the NVIDIA API Trial Terms of Service, a PDF on assets.ngc.nvidia.com that we did not read." ], "score": 83 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/nvidia-nemo-retriever.json", "live": { "slug": "nvidia-nemo-retriever", "vendorStatus": { "page": "https://status.ngc.nvidia.com", "indicator": "none", "summary": "All Systems Operational", "checkedAt": "2026-10-09T10:41:50.255876868Z" }, "updatedAt": "2026-10-09T10:41:50.255876868Z" } }, "facts": [ { "a": "HTTP API", "b": "HTTP API", "name": "Kind" }, { "a": "Amazon Web Services", "b": "NVIDIA", "name": "Vendor" }, { "a": "https://bedrock-runtime.us-east-1.amazonaws.com", "b": "no (local only)", "name": "Hosted endpoint" }, { "a": "HTTP", "b": "HTTP", "name": "Transports" }, { "a": "API key", "b": "None", "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": "Proprietary containers under the NVIDIA Software Licence Agreement and Product-Specific Terms for AI Products. Models carry their own licences, such as OpenMDW 1.1 for `nvidia/nemotron-3-embed-1b` and the NVIDIA Open Model Licence for the Llama Nemotron models", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "yes", "b": "no", "name": "llms.txt" }, { "a": "2025-10-28", "b": "2026-08-05", "name": "Last release" }, { "a": "2026-10-01", "b": "2026-05-07", "name": "Terms last updated" }, { "a": "2026-05-18", "b": "no date given", "name": "Privacy policy last updated" }, { "a": "yes, with an opt-out", "b": "not found in the text", "name": "Customer content may train models" }, { "a": "yes", "b": "not found in the text", "name": "Terms restrict automated access" }, { "a": "yes", "b": "yes", "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": "not found in the text", "name": "Arbitration or class-action waiver" }, { "a": "18.1M npm/wk, 573.7M PyPI/wk", "b": "134k PyPI/wk", "name": "Popularity" } ], "faq": [ { "answer": "Amazon Nova Multimodal Embeddings scores 75 (BB) on agent readiness against NVIDIA NeMo Retriever Embedding and Reranking NIMs's 61 (C), and leads in 4 of 7 scored categories. NVIDIA NeMo Retriever Embedding and Reranking NIMs leads on payments \u0026 pricing and maintenance \u0026 community.", "question": "Which is better for AI agents, Amazon Nova Multimodal Embeddings or NVIDIA NeMo Retriever Embedding and Reranking NIMs?" }, { "answer": "Amazon Nova Multimodal Embeddings needs an API key. NVIDIA NeMo Retriever Embedding and Reranking NIMs needs no key.", "question": "Do Amazon Nova Multimodal Embeddings and NVIDIA NeMo Retriever Embedding and Reranking NIMs need an API key?" }, { "answer": "Amazon Nova Multimodal Embeddings has a hosted endpoint at https://bedrock-runtime.us-east-1.amazonaws.com. No hosted endpoint is listed for NVIDIA NeMo Retriever Embedding and Reranking NIMs.", "question": "Can an agent call Amazon Nova Multimodal Embeddings and NVIDIA NeMo Retriever Embedding and Reranking NIMs without installing anything?" } ], "goodFor": [ { "aheadOn": [ "Reliability, 95 against 53", "Agent ergonomics, 78 against 73", "Security \u0026 auth, 91 against 55", "Transparency \u0026 trust, 79 against 71" ], "also": [ "Agent-ready, a grade of BB or better", "A hosted endpoint, with nothing to install" ], "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": [ "Payments \u0026 pricing, 40 against 30", "Maintenance \u0026 community, 57 against 50" ], "also": [ "No key needed to call it" ], "goodFor": "Teams that already run NVIDIA GPUs and need embedding and reranking inside their own network, including page-image retrieval with the VL models.", "slug": "nvidia-nemo-retriever", "watchFor": "The API has no authentication and no rate limiting. The security page leaves both to a proxy the deployer runs" } ], "job": { "capability": "embed.text", "name": "Embed text" }, "others": [ { "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-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-voyage-ai.json", "title": "Amazon Nova Multimodal Embeddings vs Voyage AI embeddings and rerankers", "url": "https://www.anchorterminal.com/compare/amazon-nova-embeddings-vs-voyage-ai" }, { "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-nvidia-nemo-retriever.json", "title": "Cohere Embed and Rerank vs NVIDIA NeMo Retriever Embedding and Reranking NIMs", "url": "https://www.anchorterminal.com/compare/cohere-embed-vs-nvidia-nemo-retriever" }, { "json": "https://www.anchorterminal.com/compare/gemini-embedding-vs-nvidia-nemo-retriever.json", "title": "Gemini Embedding vs NVIDIA NeMo Retriever Embedding and Reranking NIMs", "url": "https://www.anchorterminal.com/compare/gemini-embedding-vs-nvidia-nemo-retriever" }, { "json": "https://www.anchorterminal.com/compare/jina-embeddings-vs-nvidia-nemo-retriever.json", "title": "Jina Embeddings and Reranker vs NVIDIA NeMo Retriever Embedding and Reranking NIMs", "url": "https://www.anchorterminal.com/compare/jina-embeddings-vs-nvidia-nemo-retriever" }, { "json": "https://www.anchorterminal.com/compare/mistral-embeddings-vs-nvidia-nemo-retriever.json", "title": "Mistral Embed and Codestral Embed vs NVIDIA NeMo Retriever Embedding and Reranking NIMs", "url": "https://www.anchorterminal.com/compare/mistral-embeddings-vs-nvidia-nemo-retriever" }, { "json": "https://www.anchorterminal.com/compare/nomic-embed-vs-nvidia-nemo-retriever.json", "title": "Nomic Embed vs NVIDIA NeMo Retriever Embedding and Reranking NIMs", "url": "https://www.anchorterminal.com/compare/nomic-embed-vs-nvidia-nemo-retriever" }, { "json": "https://www.anchorterminal.com/compare/nvidia-nemo-retriever-vs-openai-embeddings.json", "title": "NVIDIA NeMo Retriever Embedding and Reranking NIMs vs OpenAI embeddings", "url": "https://www.anchorterminal.com/compare/nvidia-nemo-retriever-vs-openai-embeddings" }, { "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/nvidia-nemo-retriever-vs-zeroentropy.json", "title": "NVIDIA NeMo Retriever Embedding and Reranking NIMs vs ZeroEntropy zerank and zembed", "url": "https://www.anchorterminal.com/compare/nvidia-nemo-retriever-vs-zeroentropy" } ], "scores": [ { "amazon-nova-embeddings": 95, "by": 42, "edge": "amazon-nova-embeddings", "key": "reliability", "name": "Reliability", "nvidia-nemo-retriever": 53, "weight": 16 }, { "key": "performance", "name": "Performance", "pending": true, "weight": 10 }, { "amazon-nova-embeddings": 76, "by": 2, "edge": "nvidia-nemo-retriever", "key": "schema", "name": "Schema \u0026 documentation", "nvidia-nemo-retriever": 78, "weight": 13 }, { "amazon-nova-embeddings": 78, "by": 5, "edge": "amazon-nova-embeddings", "key": "ergonomics", "name": "Agent ergonomics", "nvidia-nemo-retriever": 73, "weight": 13 }, { "amazon-nova-embeddings": 91, "by": 36, "edge": "amazon-nova-embeddings", "key": "security", "name": "Security \u0026 auth", "nvidia-nemo-retriever": 55, "weight": 14 }, { "amazon-nova-embeddings": 30, "by": 10, "edge": "nvidia-nemo-retriever", "key": "payments", "name": "Payments \u0026 pricing", "nvidia-nemo-retriever": 40, "weight": 10 }, { "key": "tasks", "name": "Task success", "pending": true, "weight": 10 }, { "amazon-nova-embeddings": 50, "by": 7, "edge": "nvidia-nemo-retriever", "key": "maintenance", "name": "Maintenance \u0026 community", "nvidia-nemo-retriever": 57, "weight": 7 }, { "amazon-nova-embeddings": 79, "by": 8, "edge": "amazon-nova-embeddings", "key": "transparency", "name": "Transparency \u0026 trust", "nvidia-nemo-retriever": 71, "weight": 7 } ], "summary": "Amazon Nova Multimodal Embeddings scores 75 (BB) on agent readiness against NVIDIA NeMo Retriever Embedding and Reranking NIMs's 61 (C), and leads in 4 of 7 scored categories. NVIDIA NeMo Retriever Embedding and Reranking NIMs leads on 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.", "nvidia-nemo-retriever": "Self-hosted containers with OpenAPI 3.1 files, typed request fields, five embedding output types and a dated end-of-life list. The API has no authentication or rate limiting of its own, production use needs an NVIDIA AI Enterprise licence at $4,500 a GPU a year, and the release notes carry no dates." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/amazon-nova-embeddings-vs-nvidia-nemo-retriever", "json": "https://www.anchorterminal.com/compare/amazon-nova-embeddings-vs-nvidia-nemo-retriever.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/amazon-nova-embeddings-vs-nvidia-nemo-retriever.md", "slim": "https://www.anchorterminal.com/compare/amazon-nova-embeddings-vs-nvidia-nemo-retriever.min.md" }, "markdown": "Amazon Nova Multimodal Embeddings scores 75 (BB) on agent readiness against NVIDIA NeMo Retriever Embedding and Reranking NIMs's 61 (C), and leads in 4 of 7 scored categories. NVIDIA NeMo Retriever Embedding and Reranking NIMs leads on 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- NVIDIA NeMo Retriever Embedding and Reranking NIMs: grade C, 61/100, rank #439 of 842. Markdown https://www.anchorterminal.com/tools/nvidia-nemo-retriever.md · JSON https://www.anchorterminal.com/api/v1/tools/nvidia-nemo-retriever.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 53\n- Agent ergonomics, 78 against 73\n- Security \u0026 auth, 91 against 55\n- Transparency \u0026 trust, 79 against 71\n\nAlso in its favour:\n- Agent-ready, a grade of BB or better\n- A hosted endpoint, with nothing to install\n\nWatch for: In-Region inference in us-east-1 and us-gov-west-1 only, with no cross-Region inference profile\n\n### NVIDIA NeMo Retriever Embedding and Reranking NIMs (C)\n\nGood for: Teams that already run NVIDIA GPUs and need embedding and reranking inside their own network, including page-image retrieval with the VL models.\n\nAhead on:\n- Payments \u0026 pricing, 40 against 30\n- Maintenance \u0026 community, 57 against 50\n\nAlso in its favour:\n- No key needed to call it\n\nWatch for: The API has no authentication and no rate limiting. The security page leaves both to a proxy the deployer runs\n\n\n## Score by category\n\n| Category | Weight | Amazon Nova Multimodal Embeddings | NVIDIA NeMo Retriever Embedding and Reranking NIMs | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 95 | 53 | Amazon Nova Multimodal Embeddings +42 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 76 | 78 | NVIDIA NeMo Retriever Embedding and Reranking NIMs +2 |\n| Agent ergonomics | 13% (16.2 this run) | 78 | 73 | Amazon Nova Multimodal Embeddings +5 |\n| Security \u0026 auth | 14% (17.5 this run) | 91 | 55 | Amazon Nova Multimodal Embeddings +36 |\n| Payments \u0026 pricing | 10% (12.5 this run) | 30 | 40 | NVIDIA NeMo Retriever Embedding and Reranking NIMs +10 |\n| Task success | 10%, pending | pending | pending | not scored in this run |\n| Maintenance \u0026 community | 7% (8.8 this run) | 50 | 57 | NVIDIA NeMo Retriever Embedding and Reranking NIMs +7 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 79 | 71 | Amazon Nova Multimodal Embeddings +8 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **75 · BB** | **61 · C** | |\n\n## Facts side by side\n\n| Fact | Amazon Nova Multimodal Embeddings | NVIDIA NeMo Retriever Embedding and Reranking NIMs |\n| --- | --- | --- |\n| Kind | HTTP API | HTTP API |\n| Vendor | Amazon Web Services | NVIDIA |\n| Hosted endpoint | `https://bedrock-runtime.us-east-1.amazonaws.com` | no (local only) |\n| Transports | HTTP | HTTP |\n| Auth | API key | None |\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 | Proprietary containers under the NVIDIA Software Licence Agreement and Product-Specific Terms for AI Products. Models carry their own licences, such as OpenMDW 1.1 for `nvidia/nemotron-3-embed-1b` and the NVIDIA Open Model Licence for the Llama Nemotron models |\n| Read-only variant documented | no | no |\n| llms.txt | yes | no |\n| Last release | 2025-10-28 | 2026-08-05 |\n| Terms last updated | 2026-10-01 | 2026-05-07 |\n| Privacy policy last updated | 2026-05-18 | no date given |\n| Customer content may train models | yes, with an opt-out | not found in the text |\n| Terms restrict automated access | yes | not found in the text |\n| Terms restrict benchmarking | yes | yes |\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 | not found in the text |\n| Popularity | 18.1M npm/wk, 573.7M PyPI/wk | 134k PyPI/wk |\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**NVIDIA NeMo Retriever Embedding and Reranking NIMs.** Self-hosted containers with OpenAPI 3.1 files, typed request fields, five embedding output types and a dated end-of-life list. The API has no authentication or rate limiting of its own, production use needs an NVIDIA AI Enterprise licence at $4,500 a GPU a year, and the release notes carry no dates.\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### NVIDIA NeMo Retriever Embedding and Reranking NIMs\n\n1. Send `input_type` as `query` or `passage` on every embedding call. Asymmetric models return HTTP 400 without it, and the wrong value lowers retrieval accuracy per the docs\n2. Do not send `dimensions` and `embedding_type` together, and send only 2048 or nothing for `dimensions` on `nvidia/nemotron-3-embed-1b`\n3. Poll `/v1/health/ready` before the first call. The Docker health check can report unhealthy while the NIM is ready, per the 2.3 known issues\n4. Check the image tag on NGC before pulling. The guide uses `nemotron-3-embed-1b:2.3`, and NGC's record for that image listed tags up to 2.2.2 on 8 October 2026\n5. Put a proxy with authentication and TLS in front of port 8000, and sort `/v1/ranking` results yourself as the request has no top-n field\n\n## Questions\n\n### Which is better for AI agents, Amazon Nova Multimodal Embeddings or NVIDIA NeMo Retriever Embedding and Reranking NIMs?\n\nAmazon Nova Multimodal Embeddings scores 75 (BB) on agent readiness against NVIDIA NeMo Retriever Embedding and Reranking NIMs's 61 (C), and leads in 4 of 7 scored categories. NVIDIA NeMo Retriever Embedding and Reranking NIMs leads on payments \u0026 pricing and maintenance \u0026 community.\n\n### Do Amazon Nova Multimodal Embeddings and NVIDIA NeMo Retriever Embedding and Reranking NIMs need an API key?\n\nAmazon Nova Multimodal Embeddings needs an API key. NVIDIA NeMo Retriever Embedding and Reranking NIMs needs no key.\n\n### Can an agent call Amazon Nova Multimodal Embeddings and NVIDIA NeMo Retriever Embedding and Reranking NIMs without installing anything?\n\nAmazon Nova Multimodal Embeddings has a hosted endpoint at https://bedrock-runtime.us-east-1.amazonaws.com. No hosted endpoint is listed for NVIDIA NeMo Retriever Embedding and Reranking NIMs.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/amazon-nova-embeddings-vs-nvidia-nemo-retriever.json, and with the fewest tokens: https://www.anchorterminal.com/compare/amazon-nova-embeddings-vs-nvidia-nemo-retriever.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"amazon-nova-embeddings\", \"b\": \"nvidia-nemo-retriever\"}`. From a terminal: `anchor compare amazon-nova-embeddings nvidia-nemo-retriever`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/amazon-nova-embeddings.json and https://www.anchorterminal.com/api/v1/tools/nvidia-nemo-retriever.json\n\n## Other comparisons with Amazon Nova Multimodal Embeddings or NVIDIA NeMo Retriever Embedding and Reranking NIMs\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 OpenAI embeddings](https://www.anchorterminal.com/compare/amazon-nova-embeddings-vs-openai-embeddings.md)\n- [Amazon Nova Multimodal Embeddings vs Voyage AI embeddings and rerankers](https://www.anchorterminal.com/compare/amazon-nova-embeddings-vs-voyage-ai.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 NVIDIA NeMo Retriever Embedding and Reranking NIMs](https://www.anchorterminal.com/compare/cohere-embed-vs-nvidia-nemo-retriever.md)\n- [Gemini Embedding vs NVIDIA NeMo Retriever Embedding and Reranking NIMs](https://www.anchorterminal.com/compare/gemini-embedding-vs-nvidia-nemo-retriever.md)\n- [Jina Embeddings and Reranker vs NVIDIA NeMo Retriever Embedding and Reranking NIMs](https://www.anchorterminal.com/compare/jina-embeddings-vs-nvidia-nemo-retriever.md)\n- [Mistral Embed and Codestral Embed vs NVIDIA NeMo Retriever Embedding and Reranking NIMs](https://www.anchorterminal.com/compare/mistral-embeddings-vs-nvidia-nemo-retriever.md)\n- [Nomic Embed vs NVIDIA NeMo Retriever Embedding and Reranking NIMs](https://www.anchorterminal.com/compare/nomic-embed-vs-nvidia-nemo-retriever.md)\n- [NVIDIA NeMo Retriever Embedding and Reranking NIMs vs OpenAI embeddings](https://www.anchorterminal.com/compare/nvidia-nemo-retriever-vs-openai-embeddings.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- [NVIDIA NeMo Retriever Embedding and Reranking NIMs vs ZeroEntropy zerank and zembed](https://www.anchorterminal.com/compare/nvidia-nemo-retriever-vs-zeroentropy.md)\n", "meta": { "attribution": "Anchor Terminal (https://www.anchorterminal.com)", "docs": "https://www.anchorterminal.com/docs/", "generatedAt": "2026-10-09", "license": "CC-BY-4.0", "method": "https://www.anchorterminal.com/benchmark/", "methodology": "0.4", "openapi": "https://www.anchorterminal.com/openapi.json", "preview": false, "run": "2026-10-01", "runLabel": "October 2026 research run" }, "page": { "breadcrumbs": [ { "name": "Home", "url": "https://www.anchorterminal.com/" }, { "name": "Compare", "url": "https://www.anchorterminal.com/compare/" }, { "name": "Amazon Nova Multimodal Embeddings vs NVIDIA NeMo Retriever Embedding and Reranking NIMs", "url": "" } ], "description": "Amazon Nova Multimodal Embeddings scores 75 (BB) on agent readiness against NVIDIA NeMo Retriever Embedding and Reranking NIMs's 61 (C), and leads in 4 of 7 scored categories. NVIDIA NeMo Retriever Embedding and Reranking NIMs leads on payments \u0026 pricing and maintenance \u0026…", "facts": [ "Amazon Nova Multimodal Embeddings BB 75", "NVIDIA NeMo Retriever Embedding and Reranking NIMs C 61", "scores" ], "h1": "Amazon Nova Multimodal Embeddings vs NVIDIA NeMo Retriever Embedding and Reranking NIMs", "image": "https://www.anchorterminal.com/assets/og/compare-amazon-nova-embeddings-vs-nvidia-nemo-retriever.png", "path": "/compare/amazon-nova-embeddings-vs-nvidia-nemo-retriever", "published": "2026-10-01", "section": "tools", "title": "Amazon Nova Multimodal Embeddings vs NVIDIA NeMo Retriever Embedding…", "toc": null, "updated": "2026-10-09", "url": "https://www.anchorterminal.com/compare/amazon-nova-embeddings-vs-nvidia-nemo-retriever" }, "tokens": { "markdown": 2900, "slim": 830 }, "version": 1 }