Depth Analysis of Chromia Vector Database: How AI and Blockchain Integrate?

This report is written by Tiger Research and analyzes Chromia's vector database implementation as a case of the integration of AI and Blockchain technology.

Summary of Key Points

• On-chain Vector Infrastructure: Chromia has launched the first on-chain vector database built on PostgreSQL, marking an important step towards the practical integration of AI and Blockchain.
• Cost Efficiency and Developer Friendliness: By providing a blockchain integrated development environment that costs 57% less 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 such as transparency, reliability, and cost predictability—areas often seen as potential solutions provided by Blockchain.

Despite the AI agent market exploding at the end of 2024, most projects have only achieved surface-level integration of two technologies. Many initiatives rely on the speculative interest in cryptocurrencies to obtain funding and exposure, rather than exploring deep technological or functional synergies with Web3. As a result, the valuations of numerous projects have dropped by more than 90% from their peak.

The root 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 dilemma is similar 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 becoming increasingly clear that the industry needs an infrastructure that can bridge the gap—one that complements the strengths of both AI and Blockchain, and serves as a point of intersection for both.

Addressing 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 underpins 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 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 exactness), vector databases are more aligned with AI's understanding of language and context than traditional databases.

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," and "wolf." This is made possible by 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 - even though explicit emotional vocabulary is not used. Vector databases operate in a similar way, allowing systems to interpret underlying meanings rather than relying on direct vocabulary matching. This simulates human cognitive patterns, achieving more natural and intelligent AI interactions.

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

3. The vision of the Chromia Blockchain vector database

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 Expansion," which integrates PgVector, an open-source vector similarity search tool widely used within PostgreSQL databases. PgVector supports efficient queries for similar text or images, providing clear practicality for AI-driven applications.

PgVector has a solid foundation in the traditional technology ecosystem. Supabase, often regarded 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 to Web3, aligning its infrastructure with the proven standards of traditional tech stacks. This integration plays a key 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 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 connecting complex processes across 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, 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 hacking attacks and reduces overall transparency.

Chromia provides a fundamental solution by directly integrating vector databases into the Blockchain. On Chromia, all processing is completed 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.

Source: Tiger Research

To explain with a simple analogy: In the past, developers had to manage components separately—just like cooking requires buying pots, frying pans, blenders, and ovens. Chromia simplifies the process by providing a multifunctional appliance, integrating 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 complete integration of Blockchain and AI.

3.2 Cost Efficiency: Excellent price competitiveness compared to existing services

There is a common prejudice: on-chain services are "inconvenient and expensive." Especially in traditional Blockchain models, the structural defects of each transaction generating fuel fees and the skyrocketing costs of congested chains are significant. The unpredictability of costs has become a major obstacle for enterprises adopting Blockchain solutions.

Source: Chromia

Chromia addresses pain points through an efficient architecture and differentiated business model. Unlike the fuel fee model of traditional Blockchains, Chromia introduces a Server Computing Unit (SCU) leasing 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 of blockchain networks.

Specifically, users can rent 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 according to demand, achieving flexible and efficient resource allocation. This model integrates predictable usage pricing from Web2 services while maintaining network decentralization—greatly enhancing cost transparency and efficiency.

Source: Chromia, Tiger Research

Chromia vector database further strengthens cost advantage. According to internal benchmarking, 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 the technical optimization of adapting PgVector to the on-chain environment, but the greater impact comes from its decentralized resource supply model. While traditional services are stacked with high service premiums on AWS or GCP infrastructure, Chromia provides computing power and storage directly through node operators, reducing the middle tier and related costs.

The distributed structure also improves service reliability. The parallel operation of multiple nodes makes the network inherently highly available – even if a single node fails. As a result, the high-availability infrastructure and large support teams typical of the Web2 SaaS model are significantly reduced, reducing operational costs and enhancing system resilience.

4. The Beginning of the Integration of Blockchain and AI

Despite being launched just a month ago, the Chromia vector database has already shown early traction, with multiple innovative use cases in 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

An example case is the "AI Web3 Research Hub" developed by Tiger Labs. This system utilizes the Chromia infrastructure to transform research content and on-chain data from Web3 projects into vector embeddings, providing intelligent services through AI agents.

These AI agents can directly query on-chain data through the Chromia vector database, achieving significant response acceleration. Combined with Chromia's EVM indexing capabilities, the system can analyze on-chain activities from Ethereum, BNB Chain, Base, and other chains—supporting a wide range of projects. It is worth noting that user conversation context is stored on-chain, providing complete transparency in the recommendation stream for end users such as investors.

Source: Tiger Research

As the diversity of use cases grows, more data continues to be generated and stored in Chromia, laying the foundation for the "AI flywheel." Text, images, and transaction data from blockchain applications are stored in a structured vector format in the Chromia 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 accurate, customized financial advice. These advanced AI applications attract more users by enhancing the user experience, and the growth in users will generate a richer accumulation of data, forming a closed loop for sustainable ecosystem development.

5. Chromia's roadmap

After the launch of the Mimir mainnet, Chromia will focus on three major areas:

1. Enhance EVM indexing for mainstream chains such as BSC, Ethereum, Base, etc.
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, 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 easier to access.

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

5.2 AI Inference Capability Expansion

The aforementioned data indexing progress lays the foundation for Chromia's expansion of AI reasoning capabilities. The project has successfully launched its first AI reasoning extension 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 and reflects a strategic alignment with the fast-paced innovation of AI models. By supporting the direct operation of increasingly diverse powerful AI models on vendor nodes, Chromia aims to break through the boundaries of distributed AI learning and inference.

5.3 Developer Ecosystem Expansion Strategy

Chromia is actively building 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 company aims at high-impact fields such as AI research agents, 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 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. Chromia's Vision and Market Challenges

Chromia's on-chain vector database makes it a leading competitor in the blockchain-AI integration space. Its innovative approach—direct on-chain integration of vector databases—has not been realized in other ecosystems, highlighting a clear technological advantage.

The platform's cloud-based SCU rental model also introduces an enticing paradigm shift for developers accustomed to fuel fee systems. This predictable and optimized cost structure is particularly suitable for large-scale AI applications, constituting a key differentiator. Notably, the cost of use is approximately 57% lower than Web2 vector database services, significantly enhancing Chromia's market competitiveness.

Nevertheless, Chromia faces critical challenges—especially in market awareness and ecosystem growth. It is essential to communicate its complex innovations, such as its native programming language (Rell) and on-chain AI integration, to developers and enterprises. Maintaining a leading position requires ongoing technological development and ecosystem expansion, especially as other blockchain platforms begin to target similar use cases.

Long-term success depends on validating 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 substantial commercial application cases will be decisive factors for Chromia's future development.

Chromia has established an early leadership position in the emerging Web3-AI integration field. However, transforming technical 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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