KeaML VS Polygres
KeaML VS Polygres对比，KeaML 和 Polygres 有什么区别？
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总结
KeaML 总结
Experience a seamless AI development journey with KeaML, as we empower you through all stages — develop, train, and deploy.
KeaML 着陆页
Polygres 总结
Polygres turns Postgres into working memory for AI agents. Retrieve structured rows, connected relationships, semantic matches, and full-text results through one hybrid API. All without a separate vector store, graph database, or sync pipeline. Build grounded agents over the data you already have, with relational, graph, and vector context joined at the source. Use the managed cloud or self-host with our open source components. Polygres is currently free to use, just sign up!
Polygres 着陆页
比较详细信息
KeaML 详细信息
| 类别 | AI开发者工具, AI模型, AI工作流 |
| KeaML 网站 | https://keaml.com?utm_source=toolify |
| 添加时间 | 2023年11月2日 |
| KeaML 定价 | -- |
Polygres 详细信息
| 类别 | AI开发者工具, AI智能体, AI知识图谱, 人工智能接口, AI搜索引擎 |
| Polygres 网站 | https://polygres.com?utm_source=toolify |
| 添加时间 | 2026年8月26日 |
| Polygres 定价 | -- |
使用情况比较
如何使用 KeaML？
Use KeaML by first developing your AI solutions within its intuitive, pre-configured environments. Then, train your machine learning models using KeaML's optimized resources. Finally, deploy your models seamlessly with KeaML's production-ready environment.
如何使用 Polygres？
Create a Polygres managed instance or install the open-source components on an existing PostgreSQL database. Connect your schema and use the SDK or retrieval API to submit a natural-language query with options such as graph traversal depth, filters, and token limits. Polygres combines relational, graph, vector, and text results into one ranked, token-ready context block that can be passed directly to an AI agent.
比较 KeaML 和 Polygres 的优势
KeaML的核心功能
- Pre-configured development environments
- Optimized resources for model training
- Scalable deployment environment
- Collaborative tools
Polygres的核心功能
- Unified relational, graph, vector, and full-text retrieval
- Native graph traversal over existing PostgreSQL foreign keys
- Fused hybrid search with dense HNSW, sparse, filtered, and semantic retrieval
- Token-ready context assembly for grounded AI agents
- Shortest-path, neighborhood, relationship, and multi-hop graph queries
- Managed cloud hosting and open-source self-hosting
- PostgreSQL-native data retrieval without a separate vector store or sync pipeline
比较使用案例
KeaML的使用案例
- Developing AI solutions with pre-configured environments
- Training machine learning models using optimized resources
- Deploying models in a production-ready environment
Polygres的使用案例
- Build grounded AI agents that retrieve customer records, orders, tickets, and related history
- Investigate failed transactions by combining semantic search with order, payment, and dispute relationships
- Create user-memory systems that retrieve relevant past interactions and connected data
- Query Wikipedia-scale datasets using hybrid indexes and graph relationships
- Provide support agents with ranked, database-backed context
- Develop recommendation and discovery systems using existing PostgreSQL data
KeaML和Polygres的不同计划
KeaML
$0
No credit card required, 2 users, Deploy up to 3 models, Up to 50K requests per month, Up to 2 seconds per request, Up to 2GB memory per model, Email support
$350/month
10 users, Cloud Development Environments, Storage for datasets, Unlimited models, Up to 1M requests per month, Up to 3 seconds per request, Up to 4GB memory per model, Email and Slack priority support
$900/month
Unlimited users, Cloud Development Environments, Storage for datasets, Unlimited models, Up to 1.5M requests per month, Up to 4 seconds per request, Up to 5GB memory per model, Email and Slack priority support
Custom
Contact sales for your company's specific needs
Polygres
$0 / forever
Open-source pgGraph and pgVector, runnable on any PostgreSQL instance, with community support.
$16 / month
Managed cloud database hosting with email and community Discord support. Currently free for now.
$256 / month minimum usage
Automated vertical and horizontal scaling, email and community Discord support, private Discord or Slack channel, and support hours from 8:00 AM to 5:00 PM.
$4,096 / month minimum usage
Coming soon. Includes dedicated database infrastructure, custom compliance, high availability, 24/7 support, and dedicated support and uptime SLAs.
KeaML的流量
KeaML 是月访问量为 0 且平均访问时长为 00:00:00 的工具。 KeaML 的每次访问页数为 0.00，跳出率为 0.00%。
最新流量情况
| 月访问量 | 0 |
| 平均·访问时长 | 00:00:00 |
| 每次访问页数 | 0.00 |
| 跳出率 | 0.00% |
Polygres的流量
Polygres 是月访问量为 11.6K 且平均访问时长为 00:00:12 的工具。 Polygres 的每次访问页数为 1.40，跳出率为 92.09%。
最新流量情况
| 月访问量 | 11.6K |
| 平均·访问时长 | 00:00:12 |
| 每次访问页数 | 1.40 |
| 跳出率 | 92.09% |
地理位置
对不起，没有数据
地理位置
Polygres 的前 1 个国家/地区是：United States 100.00%
Top 1 国家/地区
 | 100.00% |
流量来源
KeaML 的 6 个主要流量来源是：邮件 0, vs_sourcesGenAi 0, 直接访问 0, vs_sourcesAffiliate 0, 外链引荐 0, vs_sourcesDisplayAds 0, vs_sourcesSearchPaid 0, vs_sourcesSocialPaid 0, vs_sourcesSearchOrganic 0, vs_sourcesSocialOrganic 0
邮件 | 0 |
vs_sourcesGenAi | 0 |
直接访问 | 0 |
vs_sourcesAffiliate | 0 |
外链引荐 | 0 |
vs_sourcesDisplayAds | 0 |
vs_sourcesSearchPaid | 0 |
vs_sourcesSocialPaid | 0 |
vs_sourcesSearchOrganic | 0 |
vs_sourcesSocialOrganic | 0 |
流量来源
Polygres 的 6 个主要流量来源是：直接访问 57.14%, vs_sourcesSocialOrganic 21.37%, vs_sourcesSearchOrganic 13.47%, 外链引荐 5.27%, 邮件 2.75%, vs_sourcesGenAi 0.00%, vs_sourcesAffiliate 0.00%, vs_sourcesDisplayAds 0.00%, vs_sourcesSearchPaid 0.00%, vs_sourcesSocialPaid 0.00%
直接访问 | 57.14% |
vs_sourcesSocialOrganic | 21.37% |
vs_sourcesSearchOrganic | 13.47% |
外链引荐 | 5.27% |
邮件 | 2.75% |
vs_sourcesGenAi | 0.00% |
vs_sourcesAffiliate | 0.00% |
vs_sourcesDisplayAds | 0.00% |
vs_sourcesSearchPaid | 0.00% |
vs_sourcesSocialPaid | 0.00% |
KeaML 或 Polygres哪个更好？
Polygres 可能比 KeaML 更受欢迎。如您所见，KeaML 每月有 0 次访问，而 Polygres 每月有 11.6K 次访问。 所以更多的人选择了Polygres。 因此，人们很可能会在社交平台上更多地推荐 Polygres。
KeaML 的平均访问持续时间为 00:00:00，而 Polygres 的平均访问持续时间为 00:00:12。 此外，KeaML 的每次访问页面为 0.00，跳出率为 0.00%。 Polygres 的每次访问页面为 1.40，跳出率为 92.09%。
Polygres 的主要用户是 United States，分布如下：100.00%。
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