How Baidu’s Qianfan Platform Is Accelerating Enterprise AI Adoption

Introduction

On March 21, Baidu Vice President Xie Guangjun delivered a keynote titled “Baidu Intelligent Cloud Qianfan, New Engine for Industry Innovation,” outlining recent large‑model trends and Baidu’s latest thinking and practice.

Key Industry Trends

Continuous technical progress: model architecture improvements and training‑algorithm tuning have markedly boosted efficiency and performance.

Maturing industry applications: large models are being deployed across many sectors, sparking greater interest and investment.

Growing support from governments and investors, providing more resources for R&D, deployment, and use.

Increasing public understanding and demand for intelligent, personalized services.

Enterprise Challenges

In real‑world engagements, Baidu identified four main obstacles for companies adopting large models: exploring viable scenarios, high development thresholds, inference and training costs, and achieving satisfactory application outcomes.

Qianfan Platform’s Response

Qianfan offers a complete suite of tools—including compute resources, model development, and application development—to create a “super‑factory” for large‑model services, helping enterprises lower costs, improve efficiency, and enhance model‑driven results.

Growth Metrics

Since its launch a year ago, Qianfan’s daily average growth has surged by 97% QoQ, surpassing 80,000 customers (an increase of nearly 10,000 in the last month), fine‑tuning over 13,000 models, and supporting 160,000 applications, indicating rapid market adoption.

AppBuilder Overview

AppBuilder is an AI‑native application development platform that combines basic components (large‑model and AI‑ability modules) with advanced components such as Retrieval‑Augmented Generation (RAG), a code interpreter, and generative data analysis (GBI). It enables workflow orchestration and autonomous agents, offering both code‑based and no‑code interfaces and multi‑channel integration.

Core Advantages

Leading application effect: high‑precision knowledge‑question answering and accurate autonomous task planning.

Diverse, customizable components that can be extended.

Open and easy‑to‑use product design.

RAG Use Case

AppBuilder provides an enterprise‑grade full‑link RAG framework with SFT tuning, document parsing, semantic matching, and question decomposition, achieving over 95% accuracy and superior response quality compared with peer products.

Agent Use Case

The platform’s Agent framework delivers precise autonomous task planning with multi‑tool orchestration, reaching more than 90% accuracy. It ships with about 30 built‑in tools covering e‑commerce, entertainment, office, and professional services, while also supporting custom tool integration. The code interpreter’s performance improved by 40%, and data‑analysis acceptance rose to 95%.

Vector Database (VDB)

VDB serves as a core knowledge‑base component, addressing performance, maintenance, and scalability limits of traditional QA systems. VDB 1.0 integrates comprehensive ops control, security, and compatibility with Qianfan and LangChain ecosystems, supporting up to billions of vectors, millisecond‑level retrieval, and up to 10× speed gains over open‑source alternatives, with elastic scaling and robust security.

Model Upgrades: ERNIE 3.5 & 4.0

ERNIE 3.5 now shows notable improvements in instruction compliance, context learning, and logical reasoning, enhancing use cases such as copywriting, information extraction, and tool invocation. ERNIE 4.0 achieves industry‑leading performance in understanding, generation, logic, and memory, ranking first in subject‑ability, safety, and overall scores.

Lightweight Models for Cost‑Performance

ERNIE Speed: optimized for fine‑tuning, supports up to 128K context, and outperforms larger models in novel‑role‑play and English‑speaking scenarios.

ERNIE Lite: an upgraded ERNIE‑Bot‑Turbo delivering ~20% gains in sentiment analysis, multitask learning, and reasoning, while cutting inference cost by 53%.

ERNIE Tiny: the most economical model in the ERNIE family, ideal for high‑concurrency, low‑latency tasks like retrieval, recommendation, and intent recognition, reducing cost by 32% and increasing dialogue turns by 3.5% in search‑recommendation use cases.

Vertical Best‑Practice Models

ERNIE Character is tailored for role‑play scenarios such as game NPCs and customer service, while ERNIE Functions excels in tool‑calling and business‑function invocation, achieving over 92% accuracy in tasks like ticket booking and flight queries.

Model Routing (MoE) Strategy

The router model classifies user intent and dispatches requests to the most suitable model, enabling enterprises to replace heavyweight models with lightweight alternatives. In a mobile‑assistant example, this reduced inference cost by 15% while maintaining performance comparable to ERNIE 3.5.

Model Matrix Summary

ERNIE 3.5/4.0 suit general complex scenarios; Speed and Lite serve vertical fine‑tuning needs; Tiny offers ultra‑low‑cost edge inference; Character targets role‑play; Functions focus on tool‑calling. Enterprises can flexibly select or combine models based on specific business requirements.