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NVIDIA NeMo Evaluator vs Together AI Dedicated Inference

NVIDIA NeMo Evaluator

developer.nvidia.com

Together AI Dedicated Inference

together.ai

Side by side

FigureNVIDIA NeMo EvaluatorTogether AI Dedicated Inference
Price fromFree—
Free planYes—
Free trial——
Open source——
Web—Yes
LinuxYes—
APIYesYes
Self-hostedYes—
Plans published13
Spec score · Ai Development Tools7.6 (#12)6.7 (#16)
DeploymentIts cloud-native architecture supports deployment on premises, in private clouds, or with public cloud providers.Customers select a target model and hardware configuration to deploy a production-ready endpoint, and Together says it can be live in minutes without deep infrastructure expertise.
SupportNVIDIA provides product support and customer-care chat through its support page.The product FAQ directs users to contact Together's team for help, and the product page offers a Talk to Sales option.

Plans

NVIDIA NeMo Evaluator

  • NeMo Evaluator microserviceFree

NVIDIA NeMo Evaluator pricing →

Together AI Dedicated Inference

  • Dedicated Inference — NVIDIA HGX H100 (on-demand)Priced on request
  • Dedicated Inference — NVIDIA HGX B200 (on-demand)Priced on request
  • Dedicated Inference — reserved capacityPriced on request

Together AI Dedicated Inference pricing →

In their own words

NVIDIA NeMo Evaluator

NVIDIA NeMo Evaluator is a free, open-source service for evaluating generative AI applications, including large language models, retrieval-augmented generation pipelines, and AI agents. Its API accepts an evaluation dataset, model name, and evaluation type, then returns a downloadable archive. Methods include built-in benchmarks such as MMLU, HellaSwag, and GSM8K, exact and fuzzy matching, multiple-choice and numeric checks, code sandbox evaluation, JSON-schema validation, regression comparison, and quality gates. Agent metrics can check whether the expected function and parameters were used. RAG evaluations cover generator, embedding, and reranking models, including offline checks from supplied queries and responses. LLM-as-a-judge supports open-ended assessment, model comparison, RAG, and agent evaluation. It runs on Linux with Docker 23.0.1 or later and Kubernetes 1.26.0 or later, and can deploy on premises or in private or public clouds. The evaluator itself does not require a GPU.

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Together AI Dedicated Inference

Together AI Dedicated Inference has no write-up on Specifiction yet; its figures are above.

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