Deep analysis of Chromia vector database: How to integrate AI and blockchain?

This report was written by Tiger Research and analyzed 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 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 providing a blockchain integrated development environment that is 57% lower 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 reasoning capabilities and broader developer ecosystem support, positioning Chromia as a potential leader in AI innovation in the Web3 field.

1. Current status of the integration of AI and blockchain

 **Source: Kiyotaka**

The intersection of AI and blockchain has attracted industry attention for a long time. Centralized AI systems still face challenges such as transparency, reliability and cost predictability—and these areas are often seen as potential solutions to blockchain.

Although the AI ​​agent market exploded in late 2024, most projects have achieved surface-level integration of only two technologies. Many initiatives rely on cryptocurrencies’ speculative interest in getting money and exposure rather than exploring deep technology or functionality with Web3. As a result, the valuations of many projects have fallen by more than 90% from their peak.

The root cause of the difficulty of AI and blockchain to achieve substantial collaboration lies in multiple structural problems. The most prominent one is the complexity of on-chain data processing - the data is still scattered and the technology is highly volatile. If data access and utilization can be as simple as traditional systems, the industry may have achieved clearer results.

This dilemma is similar to the script by Romeo and Juliet: Two powerful technologies from different fields lack common language or true fusion intersections. It is becoming increasingly obvious that the industry needs an infrastructure that can bridge the gap - which can complement the advantages of AI and blockchain, and serve as the intersection of the two.

Addressing this challenge requires a system that is both cost-effective and high-performance to match the reliability of existing centralized tools. Against this background, vector database technologies that support most of today's AI innovations are becoming key enablers.

2. Necessity of vector database

With the popularity of AI applications, vector databases have emerged because of solving the limitations of traditional database systems. These databases are stored by converting complex data such as text, images, audio, etc. into mathematical representations called "vectors". Because data is retrieved based on similarity (rather than accuracy), vector databases are more in line with AI 's understanding of language and context than traditional databases.

 **Source: weaviate**

Traditional databases are like library directories - only return books containing the word "kitten", while vector databases can present related content such as "cat", "dog", "wolf". This is due to the system storing information in numerical vectors, capturing relationships based on concept similarity (rather than precise wording).

Take the conversation as an example: When asked “How are you feeling today?”, if we answer “The sky is particularly clear”, we can still understand its positive emotions—although there is no clear emotional vocabulary. Vector databases operate in a similar way, allowing the system to interpret potential meanings rather than relying on direct vocabulary matching. This simulates human cognitive model to achieve 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 huge investments. In contrast, Web3 has always been difficult to develop comparable solutions, making the integration of AI and blockchain more at the theoretical level.

3. Vision of vector database on the Chromia chain

 **Source: Tiger Research**

Chromia—Layer1 relational blockchain built on PostgreSQL—stands out with structured data processing capabilities and developer-friendly environment. Relying on its relational database foundation, Chromia has begun to explore the deep integration of blockchain and AI technology.

The recent milestone is the launch of the "Chromia extension" which integrates PgVector, an open source vector similarity search tool widely used in PostgreSQL databases. PgVector supports efficient query of similar text or images, providing clear and practicality for AI-driven applications.

PgVector has a solid foundation in the traditional technology ecosystem. Supabase, often regarded as a replacement for Firebase, a mainstream database service, supports high-performance vector search using PgVector. Its growing popularity on the PostgreSQL platform reflects the industry's broad confidence in the tool.

By integrating PgVector, Chromia introduces vector search capabilities into Web3, aligning its infrastructure with proven standards by the traditional technology stack. This integration plays a central role in the Mimir mainnet upgrade in March 2025 and is seen as a fundamental step towards seamless interoperability of AI-blockchain.

3.1 Integrated Integration Environment: Complete Integration of

Blockchain and AI

The biggest challenge for developers to try to combine blockchain with AI is complexity. Creating an AI application on an existing blockchain requires a complex process of connecting multiple external systems. For example, developers need to store data on the chain, run AI models on external servers, and build an independent vector database.

This fragmented structure leads to inefficient operation. User queries are processed off-chain, and data needs to be continuously migrated 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 increases the risk of hacker attacks and reduces overall transparency.

Chromia provides a fundamental solution by integrating vector databases directly into the blockchain. On Chromia, all processing is completed within the chain: user queries are converted into vectors, and similar data is directly searched for in-chain and the results are returned, realizing the full-process single-environment processing.

 **Source: Tiger Research**

In the past, developers had to manage components separately—just like cooking requires buying pots, pans, mixers and ovens. Chromia simplifies the process by providing a multi-function cooking machine, integrating all functions into a single system.

This integration method greatly simplifies the development process. No external services and complex connection code are required, reducing development time and cost. 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 stereotype: on-chain services are “inconvenient and expensive”. Especially in the traditional blockchain model, the structural defects of fuel fees and surge in congestion on-chain costs are significant. Cost unpredictability has become a major obstacle to enterprises adopting blockchain solutions.

 **Source: Chromia**

Chromia solves pain points through efficient architecture and differentiated business models. Unlike the fuel fee model of traditional blockchain, Chromia introduces a server computing unit (SCU) rental system—a pricing structure similar to AWS or Google Cloud. This instantiation model is consistent with familiar cloud service pricing, eliminating common cost fluctuations in blockchain networks.

Specifically, users can use Chromia native token $CHR to rent SCUs weekly. Each SCU provides 16GB of benchmark storage, and the cost expands linearly with usage. SCU can be adjusted flexibly according to demand to achieve flexible and efficient resource allocation. While maintaining the network decentralization, this model integrates predictable usage metering of Web2 services - greatly improving cost transparency and efficiency.

 **Source: Chromia, Tiger Research**

Chromia vector database further strengthens cost advantages. According to internal benchmarks, the database's monthly operating cost is $727 (based on 2 SCUs with 50GB of storage) — 57% lower than similar Web2 vector database solutions.

This price competitiveness stems from multiple structural efficiency. Chromia benefits from the technical optimization of adapting PgVector to the environment on the chain, but the greater impact comes from its decentralized resource supply model. Traditional services add high service premiums on AWS or GCP infrastructure, while Chromia directly provides computing power and storage through node operators, reducing intermediate layers and related costs.

Distributed structures also improve service reliability. Multi-node parallel operation makes the network naturally highly available—even if individual nodes fail. Therefore, the typical high-availability infrastructure and large-scale support teams in the Web2 SaaS model have significantly reduced demand, both reducing operational costs and enhancing system resilience.

4. The beginning of the integration of blockchain and AI

Despite its launch for just one month, the Chromia vector database has shown early appeal, with multiple innovative use cases under development. To accelerate adoption, Chromia actively supports builders by funding the cost of covering vector database usage.

These grants lower the barriers to experiments, 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.

 **Source: Tiger Research**

Assumption cases, such as the "AI Web3 Research Hub" developed by Tiger Labs. The system uses Chromia infrastructure to convert research content and data on the chain of Web3 projects into vector embeddings for AI agents to provide intelligent services.

These AI agents can directly query on-chain data through the Chromia vector database to achieve significantly accelerated response. Combined with Chromia's EVM indexing capabilities, the system can analyze on-chain activities such as Ethereum, BNB Chain, Base, etc. - supporting a wide range of projects. It is worth noting that the user dialogue context is stored on the chain, providing investors and other end users with a completely transparent recommendation stream.

 **Source: Tiger Research**

As diversified use cases grow, 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 the Chromia database in the form of structured vectors, forming a rich AI-trainable data set.

These accumulated data have become core AI learning materials, driving performance continues to improve. For example, AI learned from massive user trading models can provide more precise and customized financial advice. These advanced AI applications attract more users by enhancing user experience, and user growth will give birth to richer data accumulation, forming a closed loop of sustainable ecological development.

5. Chromia's roadmap

Following the launch of Mimir's main network, Chromia will focus on three major areas:

1. Enhance the EVM index of mainstream chains such as BSC, Ethereum, and Base;

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

3. Expand the developer ecosystem with easier-to-use tools and infrastructure.

5.1 EVM index innovation

The inherent complexity of blockchain has long been a major obstacle for developers. To this end, Chromia has launched an innovative indexing solution with developers as the core, aiming to fundamentally simplify on-chain data query. The goal is clear: by greatly improving query efficiency and flexibility, blockchain data is easier to obtain.

This approach represents a significant change in the way Ethereum NFT transaction tracking is. Chromia dynamically learns data patterns and structures, replacing rigid predefined query structures, thereby identifying the most efficient information retrieval path. Game developers can instantly analyze the on-chain prop transaction history, and DeFi projects can quickly track complex transaction flows.

5.2 Expanding AI reasoning capabilities

The above-mentioned data indexing progress laid the foundation for Chromia to expand its AI reasoning 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 Python clients significantly reduces the difficulty of integrating machine learning models in Chromia environments.

This development goes beyond technological optimization and reflects strategic alignment with the fast-paced innovation of AI model. By enabling increasingly diverse and powerful AI models to be run directly on vendor nodes, Chromia aims to break through the boundaries of distributed AI learning and reasoning.

5.3 Developer Ecological Expansion Strategy

Chromia is actively establishing cooperation to unleash the full potential of vector database technology, focusing on AI-driven application development. These efforts are designed to enhance network utility and demand.

The company targets high-influence fields such as AI research agents, decentralized recommendation systems, context-aware text search and semantic similarity search. This program goes beyond technical support – creating a platform where developers can build real user value applications. Previously enhanced data indexing and AI reasoning capabilities are expected to become the core engines 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 field of blockchain-AI convergence. Its innovative approach—direct on-chain integrated vector database—has not been implemented in other ecosystems, highlighting the clear technical advantages.

The platform's cloud SCU rental model has also introduced an attractive paradigm shift for developers accustomed to fuel expense systems. This predictable and optimized cost structure is especially suitable for large-scale AI applications and forms a key differentiation point. It is worth noting that the cost of use is about 57% lower than that of Web2 vector database services, significantly enhancing the competitiveness of Chromia.

Nevertheless, Chromia faces key challenges – especially market perception and ecological growth. It is crucial to communicate complex innovations such as their native programming language (Rell) and on-chain AI integration to developers and businesses. Maintaining a leading position requires continuous technology development and ecological expansion, especially when other blockchain platforms begin to target similar use cases.

Long-term success depends on validating actual use cases and ensuring the sustainability of the token economic model. The impact of the SCU leasing model on the long-term value of tokens, the effective developer adoption strategies and the creation of substantive commercial application cases will be the decisive factor in Chromia's future development.

Chromia has established early leadership in the emerging Web3-AI convergence field. However, transforming technological differences into lasting market value requires continuous progress at the infrastructure, ecology and communication levels. The next 12-24 months will be crucial to shaping the long-term trajectory of Chromia.

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