Blockchain meets AI, much like Romeo meets Juliet.

This report, authored by Tiger Research, examines Chromia’s vector database implementation as a case study of the convergence of AI and blockchain technologies.

Key Takeaways

On-Chain Vector Infrastructure: Chromia has launched the first on-chain vector database built on PostgreSQL, marking a significant step towards the practical integration of AI and blockchain.

Cost Efficiency and Developer-Friendliness: By offering a blockchain-integrated development environment that is 57% cheaper than traditional industry vector solutions, Chromia lowers the barrier to entry for AI-Web3 application development.

Future Outlook: The platform plans to expand into EVM indexing, AI inference capabilities, and broader developer ecosystem support, positioning Chromia as a potential leader in AI innovation within the Web3 space.

1. The Current State of AI and Blockchain Convergence

The intersection of AI and blockchain has long captivated industry attention. Centralized AI systems still face challenges related to transparency, reliability, and cost predictability—areas often seen as potential applications for blockchain solutions.

Despite the surge in the AI agent market at the end of 2024, most projects have only achieved superficial integration of the two technologies. Many initiatives rely on speculative interest in cryptocurrencies for funding and exposure rather than exploring deep technical or functional synergies with Web3. As a result, the valuations of many projects have dropped by over 90% from their peaks.

The root of the difficulty in achieving substantive synergy between AI and blockchain lies in several structural challenges. The most prominent is the complexity of on-chain data processing—data remains fragmented and technologically volatile. If data access and utilization were as simple as in traditional systems, the industry might have achieved clearer results by now.

This predicament is akin to the script of Romeo and Juliet: two powerful technologies from different domains lack a common language or a true point of convergence. It is increasingly evident that the industry needs an infrastructure that can bridge the gap—complementing the strengths of both AI and blockchain and serving as a point of intersection.

Addressing this challenge requires a system that is both cost-effective and high-performing, matching the reliability of existing centralized tools. Against this backdrop, vector database technology, which underpins much of today’s AI innovation, is emerging as a key enabler.

2. The Necessity of Vector Databases

As AI applications become more widespread, vector databases have come to the fore by addressing the limitations of traditional database systems. These databases store complex data such as text, images, and audio in the form of mathematical representations known as “vectors.” Because they retrieve data based on similarity rather than exactness, vector databases align more closely with AI’s understanding of language and context.

Traditional databases are like library catalogs—returning only books that contain the exact term “kitten,” while vector databases can present related content such as “cat,” “dog,” and “wolf.” This is because the system stores information in numerical vector form, capturing relationships based on conceptual similarity rather than exact wording.

Take conversation as an example: when asked, “How are you feeling today?” a response like “The sky is exceptionally clear” still conveys a positive emotion, even without using explicit emotional terms. Vector databases operate in a similar way, allowing systems to interpret underlying meanings rather than relying on direct word matches. This mimics human cognitive patterns, enabling more natural and intelligent AI interactions.

In Web2, the value of vector databases has been widely recognized. Platforms such as Pinecone ($100 million), Weaviate ($50 million), Milvus ($60 million), and Chroma ($18 million) have garnered substantial investments. In contrast, Web3 has struggled to develop comparable solutions, leaving the integration of AI and blockchain largely theoretical.

3. Chromia’s Vision for On-Chain Vector Databases

Chromia—a Layer1 relational blockchain built on PostgreSQL—stands out with its structured data processing capabilities and developer-friendly environment. Leveraging its relational database foundation, Chromia has begun exploring deep integration of blockchain and AI technologies.

A recent milestone is the launch of “Chromia Extensions,” which 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 utility for AI-driven applications.

PgVector has solid roots in the traditional technology ecosystem. Supabase, often seen as an alternative to mainstream database service Firebase, uses PgVector to support high-performance vector searches. Its growing popularity on the PostgreSQL platform reflects the industry’s widespread confidence in this tool.

By integrating PgVector, Chromia brings vector search capabilities to Web3, aligning its infrastructure with the proven standards of traditional technology stacks. This integration played a central role in the Mimir mainnet upgrade in March 2025 and is seen as a foundational step towards seamless AI-blockchain interoperability.

3.1 Integrated Environment: Full Convergence of Blockchain and AI

The biggest challenge for developers trying to combine blockchain and AI is complexity. Creating AI applications on existing blockchains involves a complex process of connecting multiple external systems. For example, developers need to store data on-chain, run AI models on external servers, and build separate vector databases.

This fragmented structure leads to inefficient operations. User queries are processed off-chain, with data constantly migrating between on-chain and off-chain environments. This not only increases development time and infrastructure costs but also creates significant security vulnerabilities—data transfer between systems increases the risk of hacker attacks and reduces overall transparency.

Chromia provides a fundamental solution by integrating the vector database directly into the blockchain. On Chromia, all processing is done on-chain: user queries are converted into vectors, which directly search for similar data within the chain and return results, achieving full single-environment processing.

To illustrate with a simple analogy: in the past, developers had to manage components separately—like having to purchase pots, pans, blenders, and ovens for cooking. Chromia simplifies the process by providing a multifunctional food processor, integrating all functions into a single system.

This integrated approach greatly simplifies the development process. With no need for external services and complex connection code, development time and costs are reduced. Moreover, all data and processing are recorded on-chain, ensuring complete transparency. This marks the beginning of full convergence between blockchain and AI.

3.2 Cost Efficiency: Exceptional Price Competitiveness Compared to Existing Services

There is a common misconception that on-chain services are “inconvenient and expensive.” Particularly in traditional blockchain models, the structural flaw of rising on-chain costs due to fuel fees and congestion in every transaction is significant. The unpredictability of costs has become a major barrier for enterprises adopting blockchain solutions.

Chromia addresses this pain point through an efficient architecture and differentiated business model. Unlike the fuel fee model of traditional blockchains, Chromia introduces a Server Compute Unit (SCU) leasing system—similar to the pricing structure of AWS or Google Cloud. This instance-based model aligns with the familiar pricing of cloud services, eliminating the cost volatility common in blockchain networks.

Specifically, users can lease SCUs using Chromia’s native token $CHR on a weekly basis. Each SCU provides 16GB of baseline storage, with costs scaling linearly with usage. SCUs can be adjusted elastically according to demand, enabling flexible and efficient resource allocation. This model maintains the decentralization of the network while incorporating the predictable usage-based pricing of Web2 services—significantly enhancing cost transparency and efficiency.

Chromia’s vector database further strengthens its cost advantage. According to internal benchmarking, the database’s monthly operating cost is $727 (based on 2 SCUs and 50GB of storage)—57% lower than comparable Web2 vector database solutions.

This price competitiveness stems from multiple structural efficiencies. Chromia benefits from technical optimizations in adapting PgVector to the on-chain environment, but the larger impact comes from its decentralized resource supply model. Traditional services add a high service premium on top of AWS or GCP infrastructure, while Chromia provides compute and storage directly through node operators, reducing intermediate layers and associated costs.

The distributed structure also enhances service reliability. Parallel operation of multiple nodes makes the network inherently highly available—even if individual nodes fail. As a result, the need for expensive high-availability infrastructure and large support teams typical in Web2 SaaS models is significantly reduced, lowering operational costs and enhancing system resilience.

4. The Dawn of Blockchain and AI Convergence

Despite being launched only a month ago, Chromia’s vector database has already shown early signs of attraction, with several innovative use cases under development. To accelerate adoption, Chromia actively supports builders by subsidizing the costs of using the vector database.

These subsidies lower the barrier to experimentation, allowing developers to explore new ideas at lower risk. Potential applications cover AI-integrated DeFi services, transparent content recommendation systems, user-owned data-sharing platforms, and community-driven knowledge management tools.

Consider the hypothetical case of the “AI Web3 Research Hub” developed by Tiger Labs. This system leverages Chromia’s infrastructure to transform research content and on-chain data of Web3 projects into vector embeddings for AI agents to provide intelligent services.

These AI agents can directly query on-chain data through Chromia’s vector database, achieving significantly accelerated responses. Combined with Chromia’s EVM indexing capabilities, the system can analyze activities on chains such as Ethereum, BNB Chain, and Base—supporting a wide range of projects. Notably, user conversation contexts are stored on-chain, providing completely transparent recommendation flows for end users such as investors.

Blockchain meets AI, much like Romeo meets Juliet.

This report, authored by Tiger Research, examines Chromia’s vector database implementation as a case study of the convergence of AI and blockchain technologies.

Key Takeaways

On-Chain Vector Infrastructure: Chromia has launched the first on-chain vector database built on PostgreSQL, marking a significant step towards the practical integration of AI and blockchain.

Cost Efficiency and Developer-Friendliness: By offering a blockchain-integrated development environment that is 57% cheaper than traditional industry vector solutions, Chromia lowers the barrier to entry for AI-Web3 application development.

Future Outlook: The platform plans to expand into EVM indexing, AI inference capabilities, and broader developer ecosystem support, positioning Chromia as a potential leader in AI innovation within the Web3 space.

1. The Current State of AI and Blockchain Convergence

The intersection of AI and blockchain has long captivated industry attention. Centralized AI systems still face challenges related to transparency, reliability, and cost predictability—areas often seen as potential applications for blockchain solutions.

Despite the surge in the AI agent market at the end of 2024, most projects have only achieved superficial integration of the two technologies. Many initiatives rely on speculative interest in cryptocurrencies for funding and exposure rather than exploring deep technical or functional synergies with Web3. As a result, the valuations of many projects have dropped by over 90% from their peaks.

The root of the difficulty in achieving substantive synergy between AI and blockchain lies in several structural challenges. The most prominent is the complexity of on-chain data processing—data remains fragmented and technologically volatile. If data access and utilization were as simple as in traditional systems, the industry might have achieved clearer results by now.

This predicament is akin to the script of Romeo and Juliet: two powerful technologies from different domains lack a common language or a true point of convergence. It is increasingly evident that the industry needs an infrastructure that can bridge the gap—complementing the strengths of both AI and blockchain and serving as a point of intersection.

Addressing this challenge requires a system that is both cost-effective and high-performing, matching the reliability of existing centralized tools. Against this backdrop, vector database technology, which underpins much of today’s AI innovation, is emerging as a key enabler.

2. The Necessity of Vector Databases

As AI applications become more widespread, vector databases have come to the fore by addressing the limitations of traditional database systems. These databases store complex data such as text, images, and audio in the form of mathematical representations known as “vectors.” Because they retrieve data based on similarity rather than exactness, vector databases align more closely with AI’s understanding of language and context.

Traditional databases are like library catalogs—returning only books that contain the exact term “kitten,” while vector databases can present related content such as “cat,” “dog,” and “wolf.” This is because the system stores information in numerical vector form, capturing relationships based on conceptual similarity rather than exact wording.

Take conversation as an example: when asked, “How are you feeling today?” a response like “The sky is exceptionally clear” still conveys a positive emotion, even without using explicit emotional terms. Vector databases operate in a similar way, allowing systems to interpret underlying meanings rather than relying on direct word matches. This mimics human cognitive patterns, enabling more natural and intelligent AI interactions.

In Web2, the value of vector databases has been widely recognized. Platforms such as Pinecone ($100 million), Weaviate ($50 million), Milvus ($60 million), and Chroma ($18 million) have garnered substantial investments. In contrast, Web3 has struggled to develop comparable solutions, leaving the integration of AI and blockchain largely theoretical.

3. Chromia’s Vision for On-Chain Vector Databases

Chromia—a Layer1 relational blockchain built on PostgreSQL—stands out with its structured data processing capabilities and developer-friendly environment. Leveraging its relational database foundation, Chromia has begun exploring deep integration of blockchain and AI technologies.

A recent milestone is the launch of “Chromia Extensions,” which 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 utility for AI-driven applications.

PgVector has solid roots in the traditional technology ecosystem. Supabase, often seen as an alternative to mainstream database service Firebase, uses PgVector to support high-performance vector searches. Its growing popularity on the PostgreSQL platform reflects the industry’s widespread confidence in this tool.

By integrating PgVector, Chromia brings vector search capabilities to Web3, aligning its infrastructure with the proven standards of traditional technology stacks. This integration played a central role in the Mimir mainnet upgrade in March 2025 and is seen as a foundational step towards seamless AI-blockchain interoperability.

3.1 Integrated Environment: Full Convergence of Blockchain and AI

The biggest challenge for developers trying to combine blockchain and AI is complexity. Creating AI applications on existing blockchains involves a complex process of connecting multiple external systems. For example, developers need to store data on-chain, run AI models on external servers, and build separate vector databases.

This fragmented structure leads to inefficient operations. User queries are processed off-chain, with data constantly migrating between on-chain and off-chain environments. This not only increases development time and infrastructure costs but also creates significant security vulnerabilities—data transfer between systems increases the risk of hacker attacks and reduces overall transparency.

Chromia provides a fundamental solution by integrating the vector database directly into the blockchain. On Chromia, all processing is done on-chain: user queries are converted into vectors, which directly search for similar data within the chain and return results, achieving full single-environment processing.

To illustrate with a simple analogy: in the past, developers had to manage components separately—like having to purchase pots, pans, blenders, and ovens for cooking. Chromia simplifies the process by providing a multifunctional food processor, integrating all functions into a single system.

This integrated approach greatly simplifies the development process. With no need for external services and complex connection code, development time and costs are reduced. Moreover, all data and processing are recorded on-chain, ensuring complete transparency. This marks the beginning of full convergence between blockchain and AI.

3.2 Cost Efficiency: Exceptional Price Competitiveness Compared to Existing Services

There is a common misconception that on-chain services are “inconvenient and expensive.” Particularly in traditional blockchain models, the structural flaw of rising on-chain costs due to fuel fees and congestion in every transaction is significant. The unpredictability of costs has become a major barrier for enterprises adopting blockchain solutions.

Chromia addresses this pain point through an efficient architecture and differentiated business model. Unlike the fuel fee model of traditional blockchains, Chromia introduces a Server Compute Unit (SCU) leasing system—similar to the pricing structure of AWS or Google Cloud. This instance-based model aligns with the familiar pricing of cloud services, eliminating the cost volatility common in blockchain networks.

Specifically, users can lease SCUs using Chromia’s native token $CHR on a weekly basis. Each SCU provides 16GB of baseline storage, with costs scaling linearly with usage. SCUs can be adjusted elastically according to demand, enabling flexible and efficient resource allocation. This model maintains the decentralization of the network while incorporating the predictable usage-based pricing of Web2 services—significantly enhancing cost transparency and efficiency.

Chromia’s vector database further strengthens its cost advantage. According to internal benchmarking, the database’s monthly operating cost is $727 (based on 2 SCUs and 50GB of storage)—57% lower than comparable Web2 vector database solutions.

This price competitiveness stems from multiple structural efficiencies. Chromia benefits from technical optimizations in adapting PgVector to the on-chain environment, but the larger impact comes from its decentralized resource supply model. Traditional services add a high service premium on top of AWS or GCP infrastructure, while Chromia provides compute and storage directly through node operators, reducing intermediate layers and associated costs.

The distributed structure also enhances service reliability. Parallel operation of multiple nodes makes the network inherently highly available—even if individual nodes fail. As a result, the need for expensive high-availability infrastructure and large support teams typical in Web2 SaaS models is significantly reduced, lowering operational costs and enhancing system resilience.

4. The Dawn of Blockchain and AI Convergence

Despite being launched only a month ago, Chromia’s vector database has already shown early signs of attraction, with several innovative use cases under development. To accelerate adoption, Chromia actively supports builders by subsidizing the costs of using the vector database.

These subsidies lower the barrier to experimentation, allowing developers to explore new ideas at lower risk. Potential applications cover AI-integrated DeFi services, transparent content recommendation systems, user-owned data-sharing platforms, and community-driven knowledge management tools.

Consider the hypothetical case of the “AI Web3 Research Hub” developed by Tiger Labs. This system leverages Chromia’s infrastructure to transform research content and on-chain data of Web3 projects into vector embeddings for AI agents to provide intelligent services.

These AI agents can directly query on-chain data through Chromia’s vector database, achieving significantly accelerated responses. Combined with Chromia’s EVM indexing capabilities, the system can analyze activities on chains such as Ethereum, BNB Chain, and Base—supporting a wide range of projects. Notably, user conversation contexts are stored on-chain, providing completely transparent recommendation flows for end users such as investors.