
시장 조사용 대규모 소비자 패널이지만, Prolific처럼 학술 연구와 AI 튜닝에 특화되지는 않았습니다.
nOps는 월간 방문 97,959, 전월 대비 성장률 +13.8%, Usage-based 가격 모델을 기록합니다. Prolific는 월간 방문 18,697,671, 성장률 -1.8%, 유료 가격 모델을 기록합니다.
nOps is an automated cloud cost optimization platform that combines AI-powered cost visibility with autonomous commitment management. It continuously purchases and rebalances AWS, Azure, and GCP savings commitments to raise your effective savings rate, while giving FinOps, engineering, and finance teams hourly visibility into multicloud, Kubernetes, SaaS, and AI costs.
| 항목 / 지표 | ||
|---|---|---|
| 공식 도메인 | nops.io | prolific.com |
| 가격 모델 | Usage-based | 유료 |
| 월간 방문 수 | 9.8만 (97,959) | 1869.77만 (18,697,671) |
| 전월 대비 트래픽 변화량 | +1.19만 (+13.8%) | -35.2만 (-1.8%) |
| 주요 시장 및 점유율 | 🇺🇸 미국 (52.2%) | 🇺🇸 미국 (48.8%) |
| 월 직접 방문 수 | 3.2만 | 1297.97만 |
| 사용자 평점 | 4.4 | 4.6 |
| 지원 플랫폼 | 웹사이트 | 웹사이트 |
nOps is an automated cloud cost optimization platform that combines AI-powered cost visibility with autonomous commitment management. It continuously purchases and rebalances AWS, Azure, and GCP savings commitments to raise your effective savings rate, while giving FinOps, engineering, and finance teams hourly visibility into multicloud, Kubernetes, SaaS, and AI costs.
An autonomous engine maps your usage patterns hourly, buys the optimal mix of Reserved Instances, Savings Plans, and committed use discounts, and rebalances continuously to keep discounts high without locking you into long commitments.
See multicloud, Kubernetes, SaaS, and AI spend at hourly granularity, with business context, forecasting, anomaly detection, and unit economics that show what actually drives cost.
Allocate hourly AI spend to the model, account, team, customer, and feature behind it, covering providers such as Amazon Bedrock, OpenAI, Claude, and Cursor, with recommendations for token efficiency, model choice, and caching.
Track Effective Savings Rate, commitment coverage, and realized savings in real time, and route alerts and tasks into tools like Slack, Jira, Datadog, and Snowflake.
AI 학습·평가·연구를 위한 고품질 인간 데이터
세부 인구통계·직업·행동 조건으로 참여자를 필터링해 일반적인 군중이 아닌 좁고 대표성 있는 표본에 접근합니다.
파인튜닝용 인간 선호 데이터를 수집하고 안전성 평가와 레드팀 테스트를 수행하며, 피드백 API로 자동 파이프라인에 인간을 연결합니다.
주의력 검사, 성실성 스크리닝, 승인 워크플로로 분석 전에 부주의하거나 부정한 응답을 걸러냅니다.
연구를 게시하면 곧바로 응답 수집을 시작할 수 있고, 스크리닝·할당량·종단 설계를 지원하는 도구를 제공합니다.

시장 조사용 대규모 소비자 패널이지만, Prolific처럼 학술 연구와 AI 튜닝에 특화되지는 않았습니다.


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