Disruptive Cost Revolution: Chromia Tears Open the AI + Blockchain Commercialization Gap with a 57% Price Reduction Rate

Original authors: Ryan Yoon and Yoon Lee

Reprint: Daisy, Mars Finance

Key Points Summary

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 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 into EVM indexing, AI inference capabilities, and broader support for the developer ecosystem, positioning Chromia as a potential leader in AI innovation in the Web3 space.

1. The Current Status of the Integration of AI and Blockchain

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 that are often viewed as potential solutions 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 technological or functional synergies with Web3. As a result, the valuations of numerous projects have fallen by over 90% from their peak.

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—data remains fragmented and the technology exhibits significant volatility. If data access and utilization could be as straightforward as in traditional systems, the industry might have already achieved clearer outcomes.

This predicament resembles the script of Romeo and Juliet: two powerful technologies from different fields lack a common language or a true point of fusion. It is increasingly evident that the industry needs an infrastructure that can bridge the gap—one that can complement the advantages of both AI and blockchain and serve as a meeting point for the two.

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, the vector database technology that supports most of today's AI innovations 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 known as "vectors." Because they retrieve data based on similarity (rather than precision), vector databases align better with AI's understanding of language and context.

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 due to the system storing information in the form of numerical vectors, capturing relationships based on conceptual similarity rather than exact wording.

Taking a conversation as an example: when asked "How do you feel today?" if the response is "The sky is particularly clear," we can still understand its positive sentiment—even though no explicit emotional vocabulary is used. Vector databases operate in a similar way, enabling systems to interpret underlying meanings rather than relying on direct word matching. This simulates human cognitive patterns, achieving a more natural and intelligent AI interaction.

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 a 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 for 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 the "Chromia extension," which integrates PgVector, an open-source vector similarity search tool widely used within PostgreSQL databases. PgVector supports efficient querying of similar texts or images, providing clear practicality for AI-driven applications.

PgVector has established 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 widespread 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 core 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 with AI is complexity. Creating AI applications on existing blockchains requires connecting complex processes with 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 done on-chain: user queries are transformed 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 a pot, a frying pan, a mixer, and an oven. Chromia simplifies the process by providing a multifunctional food processor 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-chain, ensuring complete transparency. This marks the beginning of the complete integration of blockchain and AI.

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

There is a common prejudice that on-chain services are "inconvenient and expensive." This is especially evident in traditional blockchain models, where each transaction incurs fuel costs, and the structural flaws of skyrocketing costs due to network congestion are significant. The unpredictability of costs has become a major barrier for enterprises adopting blockchain solutions.

Source: Chromia

Chromia solves pain points through efficient architecture and differentiated business model. Unlike the gas fee model of traditional blockchains, Chromia introduces a Server Compute Unit (SCU) rental system – a pricing structure similar to AWS or Google Cloud. This instantiation model is consistent with familiar cloud service pricing, eliminating the cost fluctuations that are common in blockchain networks.

Specifically, users can rent SCUs on a weekly basis 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 incorporates predictable usage pricing from Web2 services while maintaining network decentralization—significantly enhancing cost transparency and efficiency.

Source: Chromia, Tiger Research

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 that adapt PgVector to the on-chain environment, but the larger impact comes from its decentralized resource supply model. Traditional services layer high service premiums on AWS or GCP infrastructure, while Chromia provides computing power and storage directly through node operators, reducing intermediary layers and associated costs.

The distributed architecture also enhances service reliability. The parallel operation of multiple nodes naturally endows the network with high availability— even if individual nodes fail. Therefore, the typical high-cost high availability infrastructure and large support team requirements 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 appeal, with several innovative use cases in development. To accelerate adoption, Chromia actively supports builders by funding the costs associated with using the vector database.

These grants lower the barriers to experimentation, allowing developers to explore new ideas with reduced risk. 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 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 Chromia vector database, achieving significantly accelerated responses. 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 recommendation flows for end users such as investors.

Source: Tiger Research

With the growth of diverse use cases, more data is continuously 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.

This accumulated data becomes the core learning material for AI, driving continuous performance improvement. For example, AI that learns from vast user trading patterns can provide more precise customized financial advice. These advanced AI applications attract more users by enhancing the user experience, and the growth in users will further generate a richer accumulation of data, forming a closed loop of sustainable ecological 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 inference 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 barrier for developers. In response, Chromia has launched an innovative indexing solution centered around developers, aiming to fundamentally simplify 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's dynamic learning data patterns and structures replace rigid predefined query structures, thereby identifying the most efficient information retrieval pathways. Game developers can instantly analyze on-chain item transaction histories, while DeFi projects can quickly track 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 extension on the testnet, with a focus 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 technological 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 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 collaborations to unleash 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 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 generate 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. The Vision of Chromia 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 the vector database—has not been realized in other ecosystems, highlighting a clear technological advantage.

The platform's cloud-based SCU leasing model also introduces an enticing 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 that of Web2 vector database services, significantly enhancing Chromia's market competitiveness.

Nevertheless, Chromia faces key challenges—especially in market awareness and ecosystem 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 ongoing technological development and ecosystem expansion, particularly 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 business application cases will be decisive factors in Chromia's future development.

Chromia has established an early leadership position in the emerging Web3-AI integration field. However, translating technological differences into lasting market value requires continuous progress in infrastructure, ecosystem, and communication levels. The next 12-24 months will be crucial in shaping Chromia's long-term trajectory.