它们的增长率如何比较？

Citely 的月度环比增长率为 -6.1%；Teaming 为 +926.1%。

价格如何比较？

Citely: 免费增值。Citely uses a credit-based model with four published tiers: a $9 one-time trial, $19/month billed monthly, $14/month billed yearly, and a $347 one-time three-year Believer plan. Higher tiers raise the credit allowance, extend the character limit for reference verification from 2,000 to 4,000 characters, and add citation export in APA, MLA, and Chicago. Enterprise and larger credit bundles are available on request. Teaming: 免费试用，付费。Free 14-day trial. Published plan is $25 per employee per month billed annually with all features included; per-manager and custom company pricing is quoted on request.

Citely 和 Teaming 的替代方案有哪些？

相关分类中的其他工具包括 Scribbr, Jenni, Scite, Motion, Yoodli AI 演讲教练。

Citely vs Teaming — 流量对比 (6.23万 vs 944)、价格与选型结论

Open AI Tools

Citely vs Teaming — 流量对比 (6.23万 vs 944)、价格与选型结论 | Open AI Tools

并排流量和规格矩阵

维度 / 指标	Citely	Teaming
官方域名	citely.ai	teaming.com
定价模式	免费增值	免费试用，付费
月访问量	6.23万 (62,271)	944 (944)
月度环比流量变化	-4056 (-6.1%)	+852 (+926.1%)
主要市场与份额	🇺🇸 美国 (25.9%)	🇧🇷 巴西 (100%)
直接访问 / 月	9641	358
用户评分	4.2	4.1
支持的平台	网站	网站

Citely

Citely is an academic citation tool that checks whether the references in a paper actually exist and point to the right work. You paste a reference list or a claim, and Citely cross-checks it against large scholarly indexes such as CrossRef, PubMed, arXiv, and OpenAlex, flagging fabricated DOIs, mismatched titles, and the hallucination patterns typical of AI-written references. It also works in reverse as a source finder: give it a claim and it returns real, citable papers with verified metadata in APA, MLA, or Chicago format.

AI Source Finder

Paste a claim or paragraph and Citely returns matching academic papers with verified metadata, ready to cite in APA, MLA, or Chicago style.

Citation & Reference Checker

Submit a reference list and Citely tests each entry against scholarly databases, marking entries that cannot be confirmed or that point to the wrong work.

AI Hallucination Detection

Citely targets the specific errors AI models make when inventing references, including chimera citations, invented DOIs, and citations that drift across years.

Multi-Database Verification

Checks run against CrossRef, PubMed, arXiv, and OpenAlex, so verification reflects real indexed scholarship rather than a single narrow database.