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CrewAI AMP vs NVIDIA NeMo Evaluator

CrewAI AMP

crewai.com

NVIDIA NeMo Evaluator

developer.nvidia.com

Side by side

FigureCrewAI AMPNVIDIA NeMo Evaluator
Price fromFreeFree
Free planYesYes
Free trial——
Open source——
WebYes—
Linux—Yes
APIYesYes
Self-hostedYesYes
Plans published21
Spec score · Ai Development Tools8.1 (#6)7.6 (#12)
DeploymentEnterprise customers can deploy on CrewAI cloud, their own VPC, or their own infrastructure.Its cloud-native architecture supports deployment on premises, in private clouds, or with public cloud providers.
PurposeCrewAI AMP is an agent management platform for building, deploying, governing, and scaling AI agent workflows across enterprise applications, with or without code.NeMo Evaluator evaluates generative AI applications, including LLMs, RAG pipelines, and AI agents.
SupportThe pricing comparison lists community support for both plans and dedicated, Slack/Teams, and on-site support for Enterprise.NVIDIA provides product support and customer-care chat through its support page.

Plans

CrewAI AMP

  • BasicFree
  • EnterprisePriced on request

CrewAI AMP pricing →

NVIDIA NeMo Evaluator

  • NeMo Evaluator microserviceFree

NVIDIA NeMo Evaluator pricing →

In their own words

CrewAI AMP

CrewAI AMP has no write-up on Specifiction yet; its figures are above.

Visit crewai.com

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.

Visit developer.nvidia.com