This report, written by Tiger Research, analyzes Chromia's vector database implementation as a case of the integration of AI and Blockchain technology.
Key Points Summary
On-chain Vector Infrastructure: Chromia has launched the first on-chain vector database built on PostgreSQL, marking an important step in 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 entry barrier for AI-Web3 application development.
Future Outlook: The platform plans to expand to 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 Status of AI and Blockchain Integration
**Source: Kiyotaka**
The intersection of AI and Blockchain has long attracted industry attention. Centralized AI systems still face challenges in transparency, reliability, and cost predictability—areas often seen as potential solutions offered by Blockchain.
Despite the AI agent market exploding at the end of 2024, 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 fallen by more than 90% from their peak.
The root cause of the difficulty in achieving substantial synergy between AI and Blockchain lies in multiple structural challenges. Among these, the most prominent is the complexity of on-chain data processing—data remains fragmented, and technological volatility is strong. If data access and utilization were as straightforward as in traditional systems, the industry might have already achieved clearer results.
This dilemma is similar to the script of Romeo and Juliet: Two powerful technologies from different fields lack a common language or a true point of integration. It is becoming increasingly clear that the industry needs an infrastructure that can bridge the gap—one that complements the advantages of AI and Blockchain, while also serving as an intersection for both.
Addressing this challenge requires a system that is both cost-effective and high-performance, to match the reliability of existing centralized tools. In this context, vector database technology, which supports most of today's AI innovations, is becoming a key enabler.
The Necessity of Vector Databases
With the popularity of AI applications, vector databases have emerged due to their ability to address 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." Because they retrieve data based on similarity (rather than accuracy), vector databases align better with AI's understanding logic of language and context than traditional databases.
! [Deep Dive into the Chromia Vector Database: How Does AI and Blockchain Converge?] ](https://img.gateio.im/social/moments-c9b08aef85cdcd7c73e3116a6043deb9)
**Source: weaviate**
Traditional databases are like library catalogs – they only return books that contain the word "kitten", while vector databases can present related content such as "cat", "dog", "wolf", etc. 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 are you feeling today?", if the response is "The sky is exceptionally clear", we can still understand its positive emotion—even though explicit emotional vocabulary is not used. Vector databases operate in a similar way, allowing systems to interpret underlying meanings instead of relying on direct word matching. This simulates 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 received substantial investments. In contrast, Web3 has always struggled to develop comparable solutions, resulting in the integration of AI and Blockchain remaining largely on a theoretical level.
3. The vision of the vector database on the Chromia Blockchain
**Source: Tiger Research**
Chromia - a Layer 1 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 to explore the deep integration of Blockchain and AI technologies.
The recent milestone is the launch of "Chromia Extension," which integrates PgVector (an open-source vector similarity search tool widely used in PostgreSQL databases). PgVector supports efficient queries for similar texts or images, providing clear practicality for AI-driven applications.
PgVector has established a solid foundation in the traditional technology ecosystem. Supabase, often seen as an alternative to the mainstream database service Firebase, utilizes PgVector to support high-performance vector searches. Its growing popularity on the PostgreSQL platform reflects the industry's broad confidence in this tool.
By integrating PgVector, Chromia introduces vector search capabilities into Web3, aligning its infrastructure with the proven standards of traditional tech stacks. This integration plays a central role in the Mimir mainnet upgrade in March 2025 and is seen as a foundational step towards seamless interoperability between AI and Blockchain.
3.1 Integrated Environment: Complete Integration of Blockchain and AI
The biggest challenge for developers trying to combine Blockchain and AI is complexity. Creating AI applications on existing Blockchains requires connecting complex processes across multiple external systems. For example, developers need to store data on the 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, requiring data to 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 hacker attacks and reduces overall transparency.
Chromia provides a fundamental solution by directly integrating vector databases into the Blockchain. On Chromia, all processing is done on-chain: user queries are transformed into vectors, searching for similar data directly on-chain and returning results, achieving end-to-end processing in a single environment.
**Source: Tiger Research**
To explain with a simple analogy: in the past, developers needed to manage components separately—just like cooking requires buying pots, frying pans, blenders, and ovens. Chromia simplifies the process by providing a multifunctional kitchen appliance that integrates all functions into a single system.
This integrated approach greatly simplifies the development process. There is no need for external services and complex connection code, reducing development time and costs. In addition, all data and processing are recorded on the chain, ensuring complete transparency. This marks the beginning of the full integration of Blockchain and AI.
3.2 Cost Efficiency: Superior price competitiveness compared to existing services.
There is a pervasive stereotype that on-chain services are "inconvenient and expensive." **Especially in the traditional blockchain model, the structural defects of gas fees per transaction and congestion chain costs surge are significant. Cost unpredictability is a major barrier for businesses to adopt blockchain solutions.
**Source: Chromia**
Chromia addresses pain points through efficient architecture and differentiated business models. Unlike the fuel fee model of traditional blockchains, Chromia introduces a Server Computing Unit (SCU) rental system - similar to the pricing structure of AWS or Google Cloud. This instantiation model is consistent with familiar cloud service pricing, eliminating the common cost fluctuations associated with blockchain networks.
Specifically, users can lease SCUs weekly using the native Chromia token $CHR. Each SCU provides 16GB of baseline storage, with costs scaling linearly with usage. SCUs can be elastically adjusted based on demand, enabling flexible and efficient resource allocation. This model integrates predictable usage pricing from Web2 services while maintaining network decentralization—significantly improving cost transparency and efficiency.
**Source: Chromia, Tiger Research**
Chromia vector database further strengthens cost advantages. According to internal benchmark tests, the monthly operating cost of this database 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 technological optimizations that adapt PgVector to the on-chain environment, but the greater impact comes from its decentralized resource supply model. Traditional services impose high service premiums on AWS or GCP infrastructure, while Chromia directly provides computing power and storage through node operators, reducing intermediary layers and associated costs.
The distributed architecture also enhances service reliability. Multi-node parallel operation gives the network inherent high availability—even if individual nodes fail. As a result, the typical high costs associated with high availability infrastructure and large support teams in the Web2 SaaS model are significantly reduced, lowering operational costs while enhancing system resilience.
4. The Beginning of the Integration of Blockchain and AI
Despite being launched only a month ago, the Chromia vector database has already shown early traction, with multiple innovative use cases under development. To accelerate adoption, Chromia is actively supporting builders by funding the costs associated with using the vector database.
These grants lower the experimental threshold, allowing developers to explore new ideas with lower risks. Potential applications include AI-integrated DeFi services, transparent content recommendation systems, user-owned data sharing platforms, and community-driven knowledge management tools.
**Source: Tiger Research**
A hypothetical case is the "AI Web3 Research Hub" developed by Tiger Labs. This system uses the Chromia infrastructure to convert research content and on-chain data from Web3 projects into vector embeddings, which are provided for intelligent services by AI agents.
These AI agents can directly query on-chain data through the Chromia vector database, significantly accelerating response times. Combined with Chromia's EVM indexing capabilities, the system can analyze on-chain activities across Ethereum, BNB Chain, Base, and more—supporting a wide range of projects. Notably, user conversation context is stored on-chain, providing complete transparency of the recommendation flow for end users such as investors.
**Source: Tiger Research**
With the growth of diverse use cases, more data continues to be generated and stored on Chromia, laying the foundation for the "AI flywheel." Text, images, and transaction data from blockchain applications are stored in structured vector form in the Chromia database, creating 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 accurate customized financial advice. These advanced AI applications attract more users by enhancing the user experience, and the growth in users will in turn generate richer data accumulation, forming a closed loop of sustainable ecosystem development.
5. Chromia's Roadmap
After the launch of the Mimir mainnet, Chromia will focus on three major areas:
Enhance EVM indexing for mainstream chains such as BSC, Ethereum, Base, etc.
Expand AI reasoning capabilities to support a wider range of models and use cases;
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, Chromia has launched an innovative indexing solution centered around developers, aimed at fundamentally simplifying on-chain data queries. The goal is clear: to significantly enhance query efficiency and flexibility, making blockchain data more accessible.
This method represents a significant shift in the way Ethereum NFT transactions are tracked. Chromia dynamically learns data patterns and structures, replacing rigid pre-defined query structures, thereby identifying the most efficient information retrieval paths. Game developers can instantly analyze on-chain item transaction history, and DeFi projects can quickly trace complex transaction flows.
5.2 Expansion of AI Inference Capabilities
The aforementioned data index progress lays the foundation for Chromia's expanded AI reasoning capabilities. The project has successfully launched its first AI reasoning expansion on the testnet, focusing on supporting open-source AI models. It is worth noting that the introduction of the Python client significantly reduces the difficulty of integrating machine learning models in the Chromia environment.
This development goes beyond technical 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, Chromia aims to break through the boundaries of distributed AI learning and reasoning.
5.3 Developer Ecosystem Expansion Strategy
Chromia is actively establishing partnerships to unlock the full potential of vector database technology, with a focus on the development of AI-driven applications. These efforts aim to enhance network utility and demand.
The company targets high-impact areas such as AI research agency, decentralized recommendation systems, context-aware text search, and semantic similarity search. This plan goes beyond technical support - creating a platform for developers to build applications that provide 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. Chromia's Vision and Market Challenges
Chromia's on-chain vector database makes it a leading competitor in the blockchain-AI integration field. Its innovative approach—direct on-chain integration of vector databases—has not yet been realized in other ecosystems, highlighting a clear technological advantage.
The platform's cloud-based SCU leasing model also brings an attractive paradigm shift for developers accustomed to the fuel fee system. This predictable and optimized cost structure is particularly suitable for large-scale AI applications, constituting a key differentiator. It is worth noting that the usage cost is approximately 57% lower than Web2 vector database services, significantly enhancing Chromia's market competitiveness.
Nevertheless, Chromia faces critical challenges—especially in market recognition and ecological growth. It is crucial to communicate its complex innovations, such as the native programming language (Rell) and on-chain AI integration, to developers and enterprises. Maintaining a leading position requires continuous technological development and ecological expansion, especially as other blockchain platforms begin to target similar use cases.
Long-term success depends on verifying real use cases and ensuring the sustainability of the token economic model. The impact of the SCU leasing model on the long-term value of the token, effective developer adoption strategies, and the creation of substantive business application cases will be the decisive factors for Chromia's future development.
Chromia has established an early leadership position in the emerging Web3-AI convergence field. However, transforming technological differences into lasting market value requires continuous progress at the infrastructure, ecosystem, and communication levels. The next 12-24 months will be crucial in shaping Chromia's long-term trajectory.
The content is for reference only, not a solicitation or offer. No investment, tax, or legal advice provided. See Disclaimer for more risks disclosure.
In-depth analysis of Chromia vector database: How AI and blockchain converge?
This report, written by Tiger Research, analyzes Chromia's vector database implementation as a case of the integration of AI and Blockchain technology.
Key Points Summary
1. The Current Status of AI and Blockchain Integration
The intersection of AI and Blockchain has long attracted industry attention. Centralized AI systems still face challenges in transparency, reliability, and cost predictability—areas often seen as potential solutions offered by Blockchain.
Despite the AI agent market exploding at the end of 2024, 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 fallen by more than 90% from their peak.
The root cause of the difficulty in achieving substantial synergy between AI and Blockchain lies in multiple structural challenges. Among these, the most prominent is the complexity of on-chain data processing—data remains fragmented, and technological volatility is strong. If data access and utilization were as straightforward as in traditional systems, the industry might have already achieved clearer results.
This dilemma is similar to the script of Romeo and Juliet: Two powerful technologies from different fields lack a common language or a true point of integration. It is becoming increasingly clear that the industry needs an infrastructure that can bridge the gap—one that complements the advantages of AI and Blockchain, while also serving as an intersection for both.
Addressing this challenge requires a system that is both cost-effective and high-performance, to match the reliability of existing centralized tools. In this context, vector database technology, which supports most of today's AI innovations, is becoming a key enabler.
The Necessity of Vector Databases
With the popularity of AI applications, vector databases have emerged due to their ability to address 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." Because they retrieve data based on similarity (rather than accuracy), vector databases align better with AI's understanding logic of language and context than traditional databases.
! [Deep Dive into the Chromia Vector Database: How Does AI and Blockchain Converge?] ](https://img.gateio.im/social/moments-c9b08aef85cdcd7c73e3116a6043deb9)
Traditional databases are like library catalogs – they only return books that contain the word "kitten", while vector databases can present related content such as "cat", "dog", "wolf", etc. 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 are you feeling today?", if the response is "The sky is exceptionally clear", we can still understand its positive emotion—even though explicit emotional vocabulary is not used. Vector databases operate in a similar way, allowing systems to interpret underlying meanings instead of relying on direct word matching. This simulates 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 received substantial investments. In contrast, Web3 has always struggled to develop comparable solutions, resulting in the integration of AI and Blockchain remaining largely on a theoretical level.
3. The vision of the vector database on the Chromia Blockchain
Chromia - a Layer 1 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 to explore the deep integration of Blockchain and AI technologies.
The recent milestone is the launch of "Chromia Extension," which integrates PgVector (an open-source vector similarity search tool widely used in PostgreSQL databases). PgVector supports efficient queries for similar texts or images, providing clear practicality for AI-driven applications.
PgVector has established a solid foundation in the traditional technology ecosystem. Supabase, often seen as an alternative to the mainstream database service Firebase, utilizes PgVector to support high-performance vector searches. Its growing popularity on the PostgreSQL platform reflects the industry's broad confidence in this tool.
By integrating PgVector, Chromia introduces vector search capabilities into Web3, aligning its infrastructure with the proven standards of traditional tech stacks. This integration plays a central role in the Mimir mainnet upgrade in March 2025 and is seen as a foundational step towards seamless interoperability between AI and Blockchain.
3.1 Integrated Environment: Complete Integration of Blockchain and AI
The biggest challenge for developers trying to combine Blockchain and AI is complexity. Creating AI applications on existing Blockchains requires connecting complex processes across multiple external systems. For example, developers need to store data on the 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, requiring data to 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 hacker attacks and reduces overall transparency.
Chromia provides a fundamental solution by directly integrating vector databases into the Blockchain. On Chromia, all processing is done on-chain: user queries are transformed into vectors, searching for similar data directly on-chain and returning results, achieving end-to-end processing in a single environment.
To explain with a simple analogy: in the past, developers needed to manage components separately—just like cooking requires buying pots, frying pans, blenders, and ovens. Chromia simplifies the process by providing a multifunctional kitchen appliance that integrates all functions into a single system.
This integrated approach greatly simplifies the development process. There is no need for external services and complex connection code, reducing development time and costs. In addition, all data and processing are recorded on the chain, ensuring complete transparency. This marks the beginning of the full integration of Blockchain and AI.
3.2 Cost Efficiency: Superior price competitiveness compared to existing services.
There is a pervasive stereotype that on-chain services are "inconvenient and expensive." **Especially in the traditional blockchain model, the structural defects of gas fees per transaction and congestion chain costs surge are significant. Cost unpredictability is a major barrier for businesses to adopt blockchain solutions.
Chromia addresses pain points through efficient architecture and differentiated business models. Unlike the fuel fee model of traditional blockchains, Chromia introduces a Server Computing Unit (SCU) rental system - similar to the pricing structure of AWS or Google Cloud. This instantiation model is consistent with familiar cloud service pricing, eliminating the common cost fluctuations associated with blockchain networks.
Specifically, users can lease SCUs weekly using the native Chromia token $CHR. Each SCU provides 16GB of baseline storage, with costs scaling linearly with usage. SCUs can be elastically adjusted based on demand, enabling flexible and efficient resource allocation. This model integrates predictable usage pricing from Web2 services while maintaining network decentralization—significantly improving cost transparency and efficiency.
Chromia vector database further strengthens cost advantages. According to internal benchmark tests, the monthly operating cost of this database 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 technological optimizations that adapt PgVector to the on-chain environment, but the greater impact comes from its decentralized resource supply model. Traditional services impose high service premiums on AWS or GCP infrastructure, while Chromia directly provides computing power and storage through node operators, reducing intermediary layers and associated costs.
The distributed architecture also enhances service reliability. Multi-node parallel operation gives the network inherent high availability—even if individual nodes fail. As a result, the typical high costs associated with high availability infrastructure and large support teams in the Web2 SaaS model are significantly reduced, lowering operational costs while enhancing system resilience.
4. The Beginning of the Integration of Blockchain and AI
Despite being launched only a month ago, the Chromia vector database has already shown early traction, with multiple innovative use cases under development. To accelerate adoption, Chromia is actively supporting builders by funding the costs associated with using the vector database.
These grants lower the experimental threshold, allowing developers to explore new ideas with lower risks. Potential applications include AI-integrated DeFi services, transparent content recommendation systems, user-owned data sharing platforms, and community-driven knowledge management tools.
A hypothetical case is the "AI Web3 Research Hub" developed by Tiger Labs. This system uses the Chromia infrastructure to convert research content and on-chain data from Web3 projects into vector embeddings, which are provided for intelligent services by AI agents.
These AI agents can directly query on-chain data through the Chromia vector database, significantly accelerating response times. Combined with Chromia's EVM indexing capabilities, the system can analyze on-chain activities across Ethereum, BNB Chain, Base, and more—supporting a wide range of projects. Notably, user conversation context is stored on-chain, providing complete transparency of the recommendation flow for end users such as investors.
With the growth of diverse use cases, more data continues to be generated and stored on Chromia, laying the foundation for the "AI flywheel." Text, images, and transaction data from blockchain applications are stored in structured vector form in the Chromia database, creating 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 accurate customized financial advice. These advanced AI applications attract more users by enhancing the user experience, and the growth in users will in turn generate richer data accumulation, forming a closed loop of sustainable ecosystem development.
5. Chromia's Roadmap
After the launch of the Mimir mainnet, Chromia will focus on three major areas:
5.1 EVM Index Innovation
The inherent complexity of blockchain has long been a major obstacle for developers. To address this, Chromia has launched an innovative indexing solution centered around developers, aimed at fundamentally simplifying on-chain data queries. The goal is clear: to significantly enhance query efficiency and flexibility, making blockchain data more accessible.
This method represents a significant shift in the way Ethereum NFT transactions are tracked. Chromia dynamically learns data patterns and structures, replacing rigid pre-defined query structures, thereby identifying the most efficient information retrieval paths. Game developers can instantly analyze on-chain item transaction history, and DeFi projects can quickly trace complex transaction flows.
5.2 Expansion of AI Inference Capabilities
The aforementioned data index progress lays the foundation for Chromia's expanded AI reasoning capabilities. The project has successfully launched its first AI reasoning expansion on the testnet, focusing on supporting open-source AI models. It is worth noting that the introduction of the Python client significantly reduces the difficulty of integrating machine learning models in the Chromia environment.
This development goes beyond technical 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, Chromia aims to break through the boundaries of distributed AI learning and reasoning.
5.3 Developer Ecosystem Expansion Strategy
Chromia is actively establishing partnerships to unlock the full potential of vector database technology, with a focus on the development of AI-driven applications. These efforts aim to enhance network utility and demand.
The company targets high-impact areas such as AI research agency, decentralized recommendation systems, context-aware text search, and semantic similarity search. This plan goes beyond technical support - creating a platform for developers to build applications that provide 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. Chromia's Vision and Market Challenges
Chromia's on-chain vector database makes it a leading competitor in the blockchain-AI integration field. Its innovative approach—direct on-chain integration of vector databases—has not yet been realized in other ecosystems, highlighting a clear technological advantage.
The platform's cloud-based SCU leasing model also brings an attractive paradigm shift for developers accustomed to the fuel fee system. This predictable and optimized cost structure is particularly suitable for large-scale AI applications, constituting a key differentiator. It is worth noting that the usage cost is approximately 57% lower than Web2 vector database services, significantly enhancing Chromia's market competitiveness.
Nevertheless, Chromia faces critical challenges—especially in market recognition and ecological growth. It is crucial to communicate its complex innovations, such as the native programming language (Rell) and on-chain AI integration, to developers and enterprises. Maintaining a leading position requires continuous technological development and ecological expansion, especially as other blockchain platforms begin to target similar use cases.
Long-term success depends on verifying real use cases and ensuring the sustainability of the token economic model. The impact of the SCU leasing model on the long-term value of the token, effective developer adoption strategies, and the creation of substantive business application cases will be the decisive factors for Chromia's future development.
Chromia has established an early leadership position in the emerging Web3-AI convergence field. However, transforming technological differences into lasting market value requires continuous progress at the infrastructure, ecosystem, and communication levels. The next 12-24 months will be crucial in shaping Chromia's long-term trajectory.
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