{ "data": { "a": { "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": "2026-10-09T11:03:49.996637483Z" }, "updatedAt": "2026-10-09T11:03:49.996637483Z" } }, "answer": "NVIDIA NeMo Retriever Embedding and Reranking NIMs scores 61 (C) 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 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" ], "remoteUrl": "https://api.voyageai.com/v1/embeddings", "packages": [ { "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": [] }, "toolCount": null, "popularity": { "githubStars": 105, "npmWeekly": 306748, "pypiWeekly": 936716, "asOf": "2026-09-30" }, "docsUrl": "https://docs.voyageai.com/docs/introduction", "llmsTxt": "https://docs.voyageai.com/llms.txt", "capabilities": [ "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": [ { "basis": "public evidence", "confidence": "medium", "grade": "C", "methodology": "0.4", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 58.8 } ], "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}'" }, "letme": { "capability": "https://letme.dev/embed.text", "tool": "https://letme.dev/voyage-ai" }, "sameCompany": [ "mongodb-mcp" ], "area": "models", "unitPrices": [ { "item": "voyage-4-large embeddings", "unit": "1m-tokens", "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 } ], "provenance": { "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." ], "score": 72 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/voyage-ai.json", "live": { "slug": "voyage-ai", "probe": { "target": "https://api.voyageai.com/v1/embeddings", "method": "get", "lastAt": "2026-10-09T11:00:42.364553839Z", "lastOk": true, "lastStatus": 405, "lastMs": 225, "authRequired": false, "uptime24h": 100, "uptime30d": 100, "p50ms24h": 200, "p95ms24h": 688, "samples24h": 260, "samples30d": 2101, "days": [ { "date": "2026-10-01", "probes": 109, "ok": 109 }, { "date": "2026-10-02", "probes": 248, "ok": 248 }, { "date": "2026-10-03", "probes": 271, "ok": 271 }, { "date": "2026-10-04", "probes": 272, "ok": 272 }, { "date": "2026-10-05", "probes": 272, "ok": 272 }, { "date": "2026-10-06", "probes": 272, "ok": 272 }, { "date": "2026-10-07", "probes": 272, "ok": 272 }, { "date": "2026-10-08", "probes": 268, "ok": 268 }, { "date": "2026-10-09", "probes": 117, "ok": 117 } ] }, "versions": [ { "registry": "github", "name": "voyage-ai/voyageai-python", "version": "v0.5.0", "released": "2026-07-10", "seenAt": "2026-10-08T16:34:45.965308749Z" }, { "registry": "npm", "name": "voyageai", "version": "0.4.0", "seenAt": "2026-10-08T16:34:45.16287519Z" }, { "registry": "pypi", "name": "voyageai", "version": "0.5.0", "released": "2026-07-10", "seenAt": "2026-10-08T16:34:44.97641064Z" } ], "githubStars": 114, "npmWeekly": 321661, "pypiWeekly": 1020497, "securityTxt": { "url": "https://voyageai.com/.well-known/security.txt", "state": "unknown", "checkedAt": "2026-10-08T15:39:10.071734293Z" }, "llmsTxt": { "url": "https://docs.voyageai.com/llms.txt", "ok": true, "status": 200, "checkedAt": "2026-10-08T14:01:00.252896565Z" }, "domain": { "domain": "voyageai.com", "registered": "2020-12-29", "source": "https://rdap.verisign.com/com/v1/domain/voyageai.com", "checkedAt": "2026-10-04T13:07:42.741568711Z" }, "pages": [ { "url": "https://docs.voyageai.com/changelog", "kind": "changelog", "status": 404, "checkedAt": "2026-10-08T18:19:45.477773495Z", "changedAt": "0001-01-01T00:00:00Z" }, { "url": "https://docs.voyageai.com/docs/pricing", "kind": "pricing", "status": 200, "checkedAt": "2026-10-08T18:19:47.855795904Z", "changedAt": "2026-10-06T16:08:50.701932018Z", "fingerprint": "d16f0f371889" }, { "url": "https://www.voyageai.com/privacy", "kind": "privacy", "status": 304, "checkedAt": "2026-10-08T18:31:29.612283925Z", "changedAt": "0001-01-01T00:00:00Z", "fingerprint": "d71dc9022a14" }, { "url": "https://www.voyageai.com/tos", "kind": "terms", "status": 304, "checkedAt": "2026-10-08T18:31:31.794503818Z", "changedAt": "0001-01-01T00:00:00Z", "fingerprint": "885b45461466" } ], "updatedAt": "2026-10-09T11:00:42.364553839Z" } }, "facts": [ { "a": "HTTP API", "b": "HTTP API", "name": "Kind" }, { "a": "NVIDIA", "b": "Voyage AI (MongoDB)", "name": "Vendor" }, { "a": "no (local only)", "b": "https://api.voyageai.com/v1/embeddings", "name": "Hosted endpoint" }, { "a": "HTTP", "b": "HTTP", "name": "Transports" }, { "a": "None", "b": "API key", "name": "Auth" }, { "a": "Freemium", "b": "Freemium", "name": "Pricing" }, { "a": "no", "b": "no", "name": "x402" }, { "a": "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", "b": "MIT (SDK)", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "yes", "name": "llms.txt" }, { "a": "2026-08-05", "b": "2026-09-30", "name": "Last release" }, { "a": "2026-05-07", "b": "2026-05-27", "name": "Terms last updated" }, { "a": "no date given", "b": "2025-02-20", "name": "Privacy policy last updated" }, { "a": "not found in the text", "b": "yes, with an opt-out", "name": "Customer content may train models" }, { "a": "not found in the text", "b": "yes", "name": "Terms restrict automated access" }, { "a": "yes", "b": "not found in the text", "name": "Terms restrict benchmarking" }, { "a": "not found in the text", "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": "134k PyPI/wk", "b": "105 stars, 307k npm/wk, 937k PyPI/wk", "name": "Popularity" }, { "a": "none", "b": "4/5 (2)", "name": "Agent reviews" } ], "faq": [ { "answer": "NVIDIA NeMo Retriever Embedding and Reranking NIMs scores 61 (C) 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 and maintenance \u0026 community.", "question": "Which is better for AI agents, NVIDIA NeMo Retriever Embedding and Reranking NIMs or Voyage AI embeddings and rerankers?" }, { "answer": "NVIDIA NeMo Retriever Embedding and Reranking NIMs needs no key. Voyage AI embeddings and rerankers needs an API key.", "question": "Do NVIDIA NeMo Retriever Embedding and Reranking NIMs and Voyage AI embeddings and rerankers need an API key?" }, { "answer": "No hosted endpoint is listed for NVIDIA NeMo Retriever Embedding and Reranking NIMs. Voyage AI embeddings and rerankers has a hosted endpoint at https://api.voyageai.com/v1/embeddings.", "question": "Can an agent call NVIDIA NeMo Retriever Embedding and Reranking NIMs and Voyage AI embeddings and rerankers without installing anything?" } ], "goodFor": [ { "aheadOn": [ "Reliability, 53 against 45", "Schema \u0026 documentation, 78 against 61", "Security \u0026 auth, 55 against 45", "Transparency \u0026 trust, 71 against 49" ], "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" }, { "aheadOn": [ "Agent ergonomics, 98 against 73", "Maintenance \u0026 community, 78 against 57" ], "also": [ "A hosted endpoint, with nothing to install", "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": { "capability": "embed.text", "name": "Embed text" }, "others": [ { "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-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/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/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-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/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-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/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-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/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-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/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-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-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" }, { "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" } ], "scores": [ { "by": 8, "edge": "nvidia-nemo-retriever", "key": "reliability", "name": "Reliability", "nvidia-nemo-retriever": 53, "voyage-ai": 45, "weight": 16 }, { "key": "performance", "name": "Performance", "pending": true, "weight": 10 }, { "by": 17, "edge": "nvidia-nemo-retriever", "key": "schema", "name": "Schema \u0026 documentation", "nvidia-nemo-retriever": 78, "voyage-ai": 61, "weight": 13 }, { "by": 25, "edge": "voyage-ai", "key": "ergonomics", "name": "Agent ergonomics", "nvidia-nemo-retriever": 73, "voyage-ai": 98, "weight": 13 }, { "by": 10, "edge": "nvidia-nemo-retriever", "key": "security", "name": "Security \u0026 auth", "nvidia-nemo-retriever": 55, "voyage-ai": 45, "weight": 14 }, { "by": 0, "edge": "", "key": "payments", "name": "Payments \u0026 pricing", "nvidia-nemo-retriever": 40, "voyage-ai": 40, "weight": 10 }, { "key": "tasks", "name": "Task success", "pending": true, "weight": 10 }, { "by": 21, "edge": "voyage-ai", "key": "maintenance", "name": "Maintenance \u0026 community", "nvidia-nemo-retriever": 57, "voyage-ai": 78, "weight": 7 }, { "by": 22, "edge": "nvidia-nemo-retriever", "key": "transparency", "name": "Transparency \u0026 trust", "nvidia-nemo-retriever": 71, "voyage-ai": 49, "weight": 7 } ], "summary": "NVIDIA NeMo Retriever Embedding and Reranking NIMs scores 61 (C) 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 and maintenance \u0026 community. Both do embed text.", "verdicts": { "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.", "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." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/nvidia-nemo-retriever-vs-voyage-ai", "json": "https://www.anchorterminal.com/compare/nvidia-nemo-retriever-vs-voyage-ai.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/nvidia-nemo-retriever-vs-voyage-ai.md", "slim": "https://www.anchorterminal.com/compare/nvidia-nemo-retriever-vs-voyage-ai.min.md" }, "markdown": "NVIDIA NeMo Retriever Embedding and Reranking NIMs scores 61 (C) 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 and maintenance \u0026 community. Both do embed text.\n\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- 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### 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- Reliability, 53 against 45\n- Schema \u0026 documentation, 78 against 61\n- Security \u0026 auth, 55 against 45\n- Transparency \u0026 trust, 71 against 49\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### 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 73\n- Maintenance \u0026 community, 78 against 57\n\nAlso in its favour:\n- A hosted endpoint, with nothing to install\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 | NVIDIA NeMo Retriever Embedding and Reranking NIMs | Voyage AI embeddings and rerankers | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 53 | 45 | NVIDIA NeMo Retriever Embedding and Reranking NIMs +8 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 78 | 61 | NVIDIA NeMo Retriever Embedding and Reranking NIMs +17 |\n| Agent ergonomics | 13% (16.2 this run) | 73 | 98 | Voyage AI embeddings and rerankers +25 |\n| Security \u0026 auth | 14% (17.5 this run) | 55 | 45 | NVIDIA NeMo Retriever Embedding and Reranking NIMs +10 |\n| Payments \u0026 pricing | 10% (12.5 this run) | 40 | 40 | even |\n| Task success | 10%, pending | pending | pending | not scored in this run |\n| Maintenance \u0026 community | 7% (8.8 this run) | 57 | 78 | Voyage AI embeddings and rerankers +21 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 71 | 49 | NVIDIA NeMo Retriever Embedding and Reranking NIMs +22 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61 · C** | **58.8 · C** | |\n\n## Facts side by side\n\n| Fact | NVIDIA NeMo Retriever Embedding and Reranking NIMs | Voyage AI embeddings and rerankers |\n| --- | --- | --- |\n| Kind | HTTP API | HTTP API |\n| Vendor | NVIDIA | Voyage AI (MongoDB) |\n| Hosted endpoint | no (local only) | `https://api.voyageai.com/v1/embeddings` |\n| Transports | HTTP | HTTP |\n| Auth | None | API key |\n| Pricing | Freemium | Freemium |\n| x402 | no | no |\n| Licence | 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 | MIT (SDK) |\n| Read-only variant documented | no | no |\n| llms.txt | no | yes |\n| Last release | 2026-08-05 | 2026-09-30 |\n| Terms last updated | 2026-05-07 | 2026-05-27 |\n| Privacy policy last updated | no date given | 2025-02-20 |\n| Customer content may train models | not found in the text | yes, with an opt-out |\n| Terms restrict automated access | not found in the text | yes |\n| Terms restrict benchmarking | yes | not found in the text |\n| Terms or service can change without notice | not found in the text | not found in the text |\n| Arbitration or class-action waiver | not found in the text | yes |\n| Popularity | 134k PyPI/wk | 105 stars, 307k npm/wk, 937k PyPI/wk |\n| Agent reviews | none | 4/5 (2) |\n\n## Verdicts\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**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### 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### 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, NVIDIA NeMo Retriever Embedding and Reranking NIMs or Voyage AI embeddings and rerankers?\n\nNVIDIA NeMo Retriever Embedding and Reranking NIMs scores 61 (C) 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 and maintenance \u0026 community.\n\n### Do NVIDIA NeMo Retriever Embedding and Reranking NIMs and Voyage AI embeddings and rerankers need an API key?\n\nNVIDIA NeMo Retriever Embedding and Reranking NIMs needs no key. Voyage AI embeddings and rerankers needs an API key.\n\n### Can an agent call NVIDIA NeMo Retriever Embedding and Reranking NIMs and Voyage AI embeddings and rerankers without installing anything?\n\nNo hosted endpoint is listed for NVIDIA NeMo Retriever Embedding and Reranking NIMs. Voyage AI embeddings and rerankers has a hosted endpoint at https://api.voyageai.com/v1/embeddings.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/nvidia-nemo-retriever-vs-voyage-ai.json, and with the fewest tokens: https://www.anchorterminal.com/compare/nvidia-nemo-retriever-vs-voyage-ai.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"nvidia-nemo-retriever\", \"b\": \"voyage-ai\"}`. From a terminal: `anchor compare nvidia-nemo-retriever voyage-ai`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/nvidia-nemo-retriever.json and https://www.anchorterminal.com/api/v1/tools/voyage-ai.json\n\n## Other comparisons with NVIDIA NeMo Retriever Embedding and Reranking NIMs or Voyage AI embeddings and rerankers\n\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 Voyage AI embeddings and rerankers](https://www.anchorterminal.com/compare/amazon-nova-embeddings-vs-voyage-ai.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- [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 NVIDIA NeMo Retriever Embedding and Reranking NIMs](https://www.anchorterminal.com/compare/gemini-embedding-vs-nvidia-nemo-retriever.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 NVIDIA NeMo Retriever Embedding and Reranking NIMs](https://www.anchorterminal.com/compare/jina-embeddings-vs-nvidia-nemo-retriever.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 NVIDIA NeMo Retriever Embedding and Reranking NIMs](https://www.anchorterminal.com/compare/mistral-embeddings-vs-nvidia-nemo-retriever.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 NVIDIA NeMo Retriever Embedding and Reranking NIMs](https://www.anchorterminal.com/compare/nomic-embed-vs-nvidia-nemo-retriever.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 OpenAI embeddings](https://www.anchorterminal.com/compare/nvidia-nemo-retriever-vs-openai-embeddings.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- [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", "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": "NVIDIA NeMo Retriever Embedding and Reranking NIMs vs Voyage AI embeddings and rerankers", "url": "" } ], "description": "NVIDIA NeMo Retriever Embedding and Reranking NIMs scores 61 (C) 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 and maintenance \u0026 community. Both do…", "facts": [ "NVIDIA NeMo Retriever Embedding and Reranking NIMs C 61", "Voyage AI embeddings and rerankers C 58.8", "scores" ], "h1": "NVIDIA NeMo Retriever Embedding and Reranking NIMs vs Voyage AI embeddings and rerankers", "image": "https://www.anchorterminal.com/assets/og/compare-nvidia-nemo-retriever-vs-voyage-ai.png", "path": "/compare/nvidia-nemo-retriever-vs-voyage-ai", "published": "2026-10-01", "section": "tools", "title": "NVIDIA NeMo Retriever Embedding and Reranking NIMs vs Voyage AI…", "toc": null, "updated": "2026-10-09", "url": "https://www.anchorterminal.com/compare/nvidia-nemo-retriever-vs-voyage-ai" }, "tokens": { "markdown": 2750, "slim": 830 }, "version": 1 }