CrewAI AMP vs NVIDIA NeMo Evaluator
CrewAI AMP
crewai.comNVIDIA NeMo Evaluator
developer.nvidia.comSide by side
| Figure | CrewAI AMP | NVIDIA NeMo Evaluator |
|---|---|---|
| Price from | Free | Free |
| Free plan | Yes | Yes |
| Free trial | — | — |
| Open source | — | — |
| Web | Yes | — |
| Linux | — | Yes |
| API | Yes | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 2 | 1 |
| Spec score · Ai Development Tools | 8.1 (#6) | 7.6 (#12) |
| Deployment | Enterprise 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. |
| Purpose | CrewAI 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. |
| Support | The 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
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.