PostgresML vs 豆包：怎么选？

关于 PostgresML

Hear from our communityThis is why I’m bullish on@postgresml- devs will always prefer to do things in data stores they already use in productionJames yu@jamesyuGreat article by PostgresML, running@huggingfacemodels INSIDE@PostgreSQLnice tidbit on scalability: "Our example data is based on 5 million DVD reviews from Amazon ... that's more data than fits in a Pinecone Pod at the time of writing"Paul Copplestone@kiwicoppleLove the fact that@postgresmlcan run various algorithms to find the optimum one for model creationRebataurAI@rebataurYou can look at PostgresML. Its based on Postgres, not specifically a vector database but they've got a pleasantly full featured eco-system for the whole training process, fetching datasets, huggingface integration, training etc. of course they also have vector related functionsDushyant (e/acc)@DevDminGodIf you want to seamlessly integrate machine learning models into your#PostgreSQLdatabase, use PostgresML.Khuyen Tran@KhuyenTran16? there's also PostgresML if you wanna get a little more full featured - supports embedding in-database as well as CUBE / pgvectorMartin McFly@martinmarkTons of capability in that Postgres extension. It's an important part of the ML Stack atcloud.tembo.ioas well.Adam Hendel@adamhendelA game-changer indeed! By integrating ML and AI directly at the database level with@postgresml, we're not just streamlining processes but revolutionizing data handling and insights generation in one fell swoop.Pranay Suyash@pranaysuyashThis is why I’m bullish on@postgresml- devs will always prefer to do things in data stores they already use in productionJames yu@jamesyuGreat article by PostgresML, running@huggingfacemodels INSIDE@PostgreSQLnice tidbit on scalability: "Our example data is based on 5 million DVD reviews from Amazon ... that's more data than fits in a Pinecone Pod at the time of writing"Paul Copplestone@kiwicoppleLove the fact that@postgresmlcan run various algorithms to find the optimum one for model creationRebataurAI@rebataurYou can look at PostgresML. Its based on Postgres, not specifically a vector database but they've got a pleasantly full featured eco-system for the whole training process, fetching datasets, huggingface integration, training etc. of course they also have vector related functionsDushyant (e/acc)@DevDminGodIf you want to seamlessly integrate machine learning models into your#PostgreSQLdatabase, use PostgresML.Khuyen Tran@KhuyenTran16? there's also PostgresML if you wanna get a little more full featured - supports embedding in-database as well as CUBE / pgvectorMartin McFly@martinmarkTons of capability in that Postgres extension. It's an important part of the ML Stack atcloud.tembo.ioas well.Adam Hendel@adamhendelA game-changer indeed! By integrating ML and AI directly at the database level with@postgresml, we're not just streamlining processes but revolutionizing data handling and insights generation in one fell swoop.Pranay Suyash@pranaysuyash

What makes PostgresMLso powerfulIndex, filter and re-rank vector embeddings10x faster vector operationsPerform fast KNN and ANN searchIndex embeddings with HNSW or IVFFlatLearn Morearrow_forwardGenerate embeddingsChoose from state-of-the-art modelsBuilt-in data preprocessors for splitting and chunkingConvert text to vector embeddingsLearn Morearrow_forwardColocate data and computeEmbed, serve and store all in one processTerabytes of data on a single machineBuilt-in data privacy & securityTrain, tune and deployRegression, classification and clusteringFine-tune LLMs on your own dataMonitor model deployments over timeLearn Morearrow_forwardGet the most of LLMsUse open-source models (Mistral, LLama, etc.)Perform a range of NLP tasksServe with the same infrastructureLearn Morearrow_forwardComprehensive platformMultiple deployment optionsPerform several AI & machine learning tasksUse SQL or SDKs in JS and PythonIndex, filter and re-rank vector embeddings10x faster vector operationsPerform fast KNN and ANN searchIndex embeddings with HNSW or IVFFlatLearn Morearrow_forwardGenerate embeddingsChoose from state-of-the-art modelsBuilt-in data preprocessors for splitting and chunkingConvert text to vector embeddingsLearn Morearrow_forwardColocate data and computeEmbed, serve and store all in one processTerabytes of data on a single machineBuilt-in data privacy & securityTrain, tune and deployRegression, classification and clusteringFine-tune LLMs on your own dataMonitor model deployments over timeLearn Morearrow_forwardGet the most of LLMsUse open-source models (Mistral, LLama, etc.)Perform a range of NLP tasksServe with the same infrastructureLearn Morearrow_forwardComprehensive platformMultiple deployment optionsPerform several AI & machine learning tasksUse SQL or SDKs in JS and Python

This is why I’m bullish on@postgresml- devs will always prefer to do things in data stores they already use in productionJames yu@jamesyuGreat article by PostgresML, running@huggingfacemodels INSIDE@PostgreSQLnice tidbit on scalability: "Our example data is based on 5 million DVD reviews from Amazon ... that's more data than fits in a Pinecone Pod at the time of writing"Paul Copplestone@kiwicoppleLove the fact that@postgresmlcan run various algorithms to find the optimum one for model creationRebataurAI@rebataurYou can look at PostgresML. Its based on Postgres, not specifically a vector database but they've got a pleasantly full featured eco-system for the whole training process, fetching datasets, huggingface integration, training etc. of course they also have vector related functionsDushyant (e/acc)@DevDminGodIf you want to seamlessly integrate machine learning models into your#PostgreSQLdatabase, use PostgresML.Khuyen Tran@KhuyenTran16? there's also PostgresML if you wanna get a little more full featured - supports embedding in-database as well as CUBE / pgvectorMartin McFly@martinmarkTons of capability in that Postgres extension. It's an important part of the ML Stack atcloud.tembo.ioas well.Adam Hendel@adamhendelA game-changer indeed! By integrating ML and AI directly at the database level with@postgresml, we're not just streamlining processes but revolutionizing data handling and insights generation in one fell swoop.Pranay Suyash@pranaysuyash

核心功能

• filter_dramaPostgresML Cloud（自动优化版）
• vpn_keyVPC（自动优化版）
• descriptionLLMs（自动优化版）
• subtitlesEmbeddings（自动优化版）
• open_withVector Database（自动优化版）
• model_trainingSupervised Learning（自动优化版）
• manage_searchRAG（自动优化版）
• feature_searchSearch（自动优化版）

关于 豆包 豆包是字节跳动推出的AI智能助手，目前已从基础的问答对话工具发展为具备复杂任务执行能力的生产力平台。截至2026年6月，豆包大模型日均Token调用量已突破180万亿，月活跃用户约3.45亿，稳居国内消费级大模型用户规模榜首。

一、核心功能

豆包的功能体系可划分为三大板块：

1. 基础AI能力（免费版核心）

免费版用户可正常使用上述全部功能，且可在一定额度内体验办公任务模式（搭载豆包2.1 Turbo模型）。

2. 办公任务模式（专业版核心突破）

2026年6月24日，豆包正式推出豆包专业版，上线基于Agent驱动的“办公任务模式”。与传统对话模式不同，该模式能够：

1. 理解复杂工作目标，自主拆解为多步任务
2. 调用各类工具完成全流程执行（本地电脑、浏览器、Office套件、飞书等）
3. 后台运行长任务，用户可脱身处理其他工作
4. 定时执行重复任务，如每日日报、定期报告汇总

关键能力实测表现：

3. 智能体与Skills（技能生态）

豆包支持智能体（Agent）创建与分享，用户可根据学习、工作、创作、生活等场景定制专属智能体。

“Skills（技能）”是另一重要能力，首批包括文档、表格、PPT、创意设计、操作浏览器、可视化讲解等，以及金融行业专业技能。用户还可以创建、安装自己的专属技能。