A New Chapter in the Fusion of AI and Blockchain: Analyzing the Breakthrough Progress of On-chain Vector Databases

Key Points Overview

• On-chain vector infrastructure: The first on-chain vector database based on PostgreSQL has been launched, marking an important step towards the practical integration of AI and Blockchain.

• Cost-effectiveness and developer friendliness: By providing a blockchain integration environment that is more cost-effective than traditional industry solutions, it lowers the barriers to AI-Web3 application development.

• Future Outlook: Plans to expand to EVM indexing, AI inference capabilities, and broader developer ecosystem support, with the potential to become a pioneer in AI innovation within the Web3 space.

1. The Current State of AI and Blockchain Integration

The intersection of AI and Blockchain has long attracted industry attention. The challenges faced by centralized AI systems, such as transparency, reliability, and cost predictability, are precisely the areas where blockchain technology may provide solutions.

Despite the recent explosion in the AI agent market, most projects have only achieved superficial integration of two technologies. Many initiatives rely on the speculative interest in cryptocurrencies to gain funding and exposure, rather than exploring deep technical or functional synergies with Web3. As a result, the valuations of numerous projects have significantly declined.

The root cause of the difficulty in achieving substantial synergy between AI and Blockchain lies in several structural challenges. The most prominent of these is the complexity of on-chain data processing — the data remains fragmented and the technology is highly volatile. If data access and utilization could be as simple as in traditional systems, the industry might have already achieved clearer results.

This predicament is akin to two powerful technologies from different fields lacking a common language or a true intersection point for integration. It is becoming increasingly apparent that the industry needs an infrastructure that can bridge the gap—one that complements the advantages of AI and Blockchain, while also serving as a convergence point for both.

Meeting this challenge requires a cost-effective and high-performance system to match the reliability of existing centralized tools. In this context, the vector database technology that supports most AI innovations today is becoming a key enabler.

2. The Necessity of Vector Databases

With the widespread application of AI, vector databases have emerged due to their ability to overcome the limitations of traditional database systems. These databases store complex data such as text, images, and audio by converting them into mathematical representations called "vectors." By retrieving data based on similarity (rather than precision), vector databases align more closely with AI's understanding of language and context than traditional databases.

Traditional databases are like library catalogs — they only return books that contain specific words, while vector databases can present relevant content. This is due to the system storing information in the form of numerical vectors, capturing relationships based on conceptual similarity rather than exact wording.

For example, in a conversation: when asked "How do you feel today?", if the response is "The sky is particularly clear", we can still understand the positive emotion—despite not using explicit emotional vocabulary. Vector databases operate in a similar way, allowing systems to interpret underlying meanings rather than relying on direct word matching. This simulates human cognitive patterns, enabling more natural and intelligent AI interactions.

In the traditional internet field, the value of vector databases has been widely recognized, and several platforms have received substantial investments. In contrast, Web3 has always struggled to develop comparable solutions, resulting in the integration of AI and Blockchain remaining more at the theoretical level.

3. Vision of On-chain Vector Database

A certain blockchain platform stands out due to its structured data processing capabilities and developer-friendly environment. Leveraging its relational database foundation, the platform has begun to explore the deep integration of blockchain and AI technologies.

The recent milestone is the launch of the functionality extension that integrates PgVector, an open-source vector similarity search tool widely used within PostgreSQL databases. PgVector supports efficient querying of similar text or images, providing clear practicality for AI-driven applications.

PgVector has established a solid foundation in the traditional technology ecosystem. By integrating PgVector, the platform introduces vector search capabilities into Web3, aligning its infrastructure with the proven standards of the traditional tech stack. This integration will play a key role in future mainnet upgrades and is seen as a foundational step towards seamless interoperability between AI and Blockchain.

3.1 Integrated Environment: Complete Fusion of Blockchain and AI

The biggest challenge for developers trying to combine Blockchain and AI is complexity. Creating AI applications on existing blockchains requires complex processes that connect multiple external systems. For example, developers need to store data on-chain, run AI models on external servers, and build independent vector databases.

This fragmented structure leads to inefficient operations. User queries are processed off-chain, and data must continuously migrate between on-chain and off-chain environments. This not only increases development time and infrastructure costs but also creates serious security vulnerabilities—data transmission between systems exacerbates the risk of hacking and reduces overall transparency.

A certain platform provides a fundamental solution by directly integrating a vector database into the Blockchain. On this platform, all processing is done on-chain: user queries are converted into vectors, similar data is searched directly on-chain, and results are returned, achieving end-to-end processing in a single environment.

To explain with a simple analogy: In the past, developers had to manage components separately—just like cooking requires purchasing a pot, frying pan, blender, and oven. This platform simplifies the process by providing a multifunctional cooking machine that integrates all functions into a single system.

This integrated approach greatly simplifies the development process. There is no need for external services or complex connection code, reducing development time and costs. In addition, all data and processing are recorded on-chain, ensuring complete transparency. This marks the beginning of the complete integration of Blockchain and AI.

3.2 Cost Efficiency: Outstanding price competitiveness compared to existing services.

There is a common prejudice that "on-chain services are inconvenient and expensive." Especially in traditional Blockchain models, the structural defects of each transaction generating fuel fees and the surge in costs due to network congestion are significant. The unpredictability of costs has become a major barrier for enterprises adopting Blockchain solutions.

A certain platform addresses pain points through an efficient architecture and differentiated business model. Unlike the traditional fuel fee model of Blockchain, this platform introduces a Server Computing Unit (SCU) leasing system - similar to the pricing structure of mainstream cloud services. This instantiation model is consistent with familiar cloud service pricing, eliminating the common cost fluctuations found in on-chain networks.

Specifically, users can rent SCUs on a weekly basis using the platform's native token. Each SCU provides a certain baseline storage, with costs expanding linearly with usage. SCUs can be elastically adjusted according to demand, achieving flexible and efficient resource allocation. This model incorporates predictable usage pricing while maintaining the decentralization of the network—significantly enhancing cost transparency and efficiency.

The platform's vector database further strengthens its cost advantages. According to internal benchmark tests, the monthly operating cost of this database is 57% lower than that of similar traditional vector database solutions.

This price competitiveness stems from multiple structural efficiencies. The platform benefits from technical optimizations that adapt PgVector to on-chain environments, but the greater impact comes from its decentralized resource supply model. Traditional services impose high service premiums on cloud infrastructure, while this platform directly provides computing power and storage through node operators, reducing intermediaries and related costs.

The distributed architecture also enhances service reliability. The parallel operation of multiple nodes naturally provides high availability for the network—even if individual nodes fail. Therefore, the typical high costs associated with high availability infrastructure and large support teams in traditional SaaS models are significantly reduced, lowering operational costs while enhancing system resilience.

4. The Beginning of the Fusion of Blockchain and AI

Despite being launched only a month ago, the platform's vector database has already shown early traction, with multiple innovative use cases being developed. To accelerate adoption, the platform actively supports builders by funding the costs associated with the use of the vector database.

These grants lower the experimental threshold, allowing developers to explore new ideas with lower risks. Potential applications cover AI-integrated DeFi services, transparent content recommendation systems, user-owned data sharing platforms, and community-driven knowledge management tools.

Assuming the case of the "AI Web3 Research Hub" developed by a research team. This system utilizes platform infrastructure to convert research content and on-chain data from Web3 projects into vector embeddings for AI agents to provide intelligent services.

These AI agents can directly query on-chain data through the platform's vector database, achieving significant acceleration in response. Combined with the platform's EVM indexing capability, the system can analyze activities across multiple mainstream blockchains. It is worth noting that user dialogue context is stored on-chain, providing end-users like investors with a fully transparent recommendation stream.

As the diverse use cases grow, more data continues to be generated and stored on the platform—laying the foundation for the "AI flywheel". Text, images, and transaction data from blockchain applications are stored in structured vector form in the database, forming a rich AI trainable dataset.

These accumulated data become the core learning materials for AI, driving continuous performance improvement. For example, AI that learns from massive user trading patterns can provide more precise customized financial advice. These advanced AI applications attract more users by enhancing user experience, and user growth will further generate richer data accumulation, forming a closed loop of sustainable ecological development.

5. Future Roadmap

After the mainnet upgrade, the platform will focus on three major areas:

1. Enhance the EVM index of mainstream Blockchains;

2. Expand AI inference capabilities to support a wider range of models and use cases;

3. Expand the developer ecosystem through more user-friendly tools and infrastructure.

5.1 EVM Index Innovation

The inherent complexity of Blockchain has long been a major obstacle for developers. To address this, the platform has launched an innovative indexing solution centered around developers, aimed at fundamentally simplifying on-chain data queries. The goal is clear: to make Blockchain data more accessible by significantly enhancing query efficiency and flexibility.

This method represents a significant shift in the way Ethereum NFT transactions are tracked. The platform dynamically learns data patterns and structures, replacing rigid predefined query structures to identify the most efficient information retrieval paths. Game developers can instantly analyze on-chain item transaction history, and DeFi projects can quickly track complex transaction flows.

5.2 AI reasoning capability expansion

The aforementioned data indexing progress lays the foundation for the platform to expand its AI inference capabilities. The project has successfully launched the first AI inference extension on the test network, focusing on supporting open-source AI models. It is worth noting that the introduction of the Python client has significantly reduced the difficulty of integrating machine learning models in the platform environment.

This development goes beyond technological optimization, reflecting a strategic alignment with the fast-paced innovation of AI models. By supporting the direct operation of increasingly diverse powerful AI models at vendor nodes, the platform aims to break through the boundaries of distributed AI learning and reasoning.

5.3 Developer Ecosystem Expansion Strategy

The platform is actively establishing partnerships to unlock the full potential of vector database technology, with a focus on AI-driven application development. These efforts aim to enhance network utility and demand.

The team aims at high-impact areas such as AI research agents, decentralized recommendation systems, context-aware text search, and semantic similarity search. The plan goes beyond technical support - creating a platform where developers can build applications that deliver real user value. The enhanced data indexing and AI reasoning capabilities are expected to become the core engine for the development of these applications.

6. Vision and Market Challenges

The platform's on-chain vector database makes it a leading competitor in the blockchain-AI integration field. Its innovative approach—direct on-chain integration of the vector database—has not yet been realized in other ecosystems, highlighting