Gate.Ai: Unified access to 200+ large models, redefining the way the crypto market obtains information

The crypto market has never lacked information; what’s lacking is the efficiency of obtaining valid information. On-chain data, market fluctuations, project documentation, market sentiment—these elements are scattered across different platforms and protocols. Any participant attempting to fully understand the market state bears high search costs. In May 2026, Gate officially launched its AI infrastructure product Gate.Ai to the market, aiming to address this long-neglected foundational issue.

The positioning of Gate.Ai is noteworthy: it is not a trading signal tool aimed at retail investors, nor does it directly provide investment advice. Instead, it is a foundational infrastructure layer connecting large models with users. The so-called “call layer” refers to a unified interface where users do not need to connect separately to over 200 large models via APIs like GPT, Gemini, Claude, DeepSeek, etc., but can access, switch, and settle costs through Gate.Ai’s unified interface. This design is closer to an API gateway in the cloud computing era rather than a traditional trading assistant.

As of June 1, 2026, according to Gate market data, Bitcoin’s price is reported at $73,678.0, with a 24-hour trading range between $73,393.9 and $74,276.9. Ethereum’s price is reported at $2,007.35, and market sentiment remains neutral. These market features indicate that the short-term direction is unclear, and market participants have a stronger demand for structured data and multi-dimensional cross-analysis. Against this backdrop, Gate.Ai has been brought to the forefront.

The Long-term Pain Point of the Crypto Market Information Structure

To understand the value of Gate.Ai, one must first understand the peculiarities of the crypto market information structure. Traditional financial markets have centralized data providers and standardized information terminals, whereas data in the crypto market is dispersed across exchanges, on-chain explorers, governance forums, developer communities, and social platforms. Information exists but is fragmented into isolated islands.

This fragmentation leads to two consequences. First, ordinary users have very low efficiency in obtaining information, needing to switch repeatedly between multiple platforms. Second, even professional institutions find it difficult to establish unified data processing pipelines, where cross-model calls, cost attribution, and permission management become hidden internal governance costs. Gate.Ai’s entry point precisely addresses the intersection of these two needs—using a unified interface layer to connect dispersed models and data sources into a single call entry point.

Intelligent Routing Solves Not a Technical Problem, but a Stability Issue

Gate.Ai’s built-in intelligent routing and automatic fallback mechanisms may seem like technical features on the surface, but their real significance lies in ensuring usability. When a model’s response latency increases or the service is interrupted, the system automatically switches requests to backup models, transparently to the caller.

For products that require continuous access to market data or embed AI capabilities, this stability is not a luxury but a necessity. Any developer relying on external APIs for service construction knows that fluctuations in model provider availability directly affect user experience. Gate.Ai solves this uncertainty at the routing layer, freeing users from writing complex retry logic and degradation strategies on the backend.

Cost Management Is Becoming a Key Variable for Enterprise AI Adoption

Another less-discussed issue in the market is the controllability of AI invocation costs. As enterprises integrate large models into their workflows, the cost fluctuations caused by increasing call volumes have become a focus for management. Gate.Ai’s unified billing, budget limits, and cross-model usage analysis turn cost management from “post-billing” to “real-time control.”

Settling at original manufacturer prices and paying per use avoids distortions caused by middlemen’s markups. Enterprises can clearly see which model each call consumes and which team generated it. This transparency is crucial for internal management and continuous efficiency optimization. At this stage, cost governance may have a more direct impact on enterprise AI adoption decisions than the model’s performance itself.

Zero Data Retention Is Not Just a Slogan, but a Trust Anchor for Infrastructure

Data privacy has always been a sensitive topic in the crypto industry. Gate.Ai defaults to a zero data retention policy, not storing user input content or using user data for model training or product improvement. For enterprise users, this means they can confidently send internal data via API calls without worrying about data leaks or indirect inclusion in third-party training datasets.

Coupled with team-level API key management, role-based permissions, and full-chain call tracking, Gate.Ai effectively builds an organizational AI usage governance framework. The core idea is to unify “who can call, what was called, and how much was spent” into a single control panel. Enterprises gain not just an API gateway but an auditable AI usage management system.

From Tool Attribute to Infrastructure Attribute: A Cross-Over

When the market discusses the integration of AI and crypto, most attention remains on the application layer—trading signals, intelligent investment advisors, automation strategies, etc. But Gate.Ai has chosen a different path: it focuses on the connection layer rather than the application layer. Connecting models with users, data with decisions, costs with benefits—these seemingly foundational tasks form the underlying track for further AI integration in the industry.

From a longer-term perspective, the information infrastructure of the crypto market has been slowly evolving. From early forum discussions to data aggregation platforms, and now to AI invocation layers, each evolution reduces friction in information access. The significance of Gate.Ai is not just that it offers a new feature but that it elevates AI invocation from “each team building their own” to “a unified access and centralized governance” infrastructure model. Once this model is widely adopted, the speed of AI integration in the crypto industry will accelerate significantly.

FAQ

What is Gate.Ai

Gate.Ai is an AI infrastructure product launched by Gate, providing unified access to over 200 large models, intelligent routing, cost management, and data privacy protection, aimed at developers and enterprise users.

How is Gate.Ai different from general trading assistants

Gate.Ai is positioned at the model invocation layer rather than the application layer, not providing trading signals or investment advice, but solving issues of unified model access, availability assurance, and cost management.

Which large models does Gate.Ai support

Supports over 200 mainstream models including GPT, Gemini, Claude, DeepSeek, Qwen, GLM, Grok, Nemotron, MiniMax, Kimi, and others.

How does Gate.Ai protect data privacy

Defaults to a zero data retention policy, not storing user input content or using user data for any model training or product improvement plans.

How are Gate.Ai’s costs calculated

Settled at original manufacturer prices, pay-as-you-go, with unified billing and budget control features, allowing enterprises to set spending limits and trace each call’s attribution.

What is the practical use of Gate.Ai’s intelligent routing

When a model’s latency increases or it becomes unavailable, the system automatically switches requests to backup models, ensuring continuous service without requiring users to handle retries themselves.