As large language model applications evolve from chain-of-thought reasoning toward multi-agent collaboration, enterprise AI applications are moving from capability exploration into a stage of large-scale operations. Facing the growing demand for AI applications, Token is gradually becoming a new type of AI resource that enterprises need to focus on and manage, following storage and computing power.
To help enterprises build secure, efficient, and operable large language model service systems, Futong Technology officially launched the Futong TokenWise Token Operations Platform. Centered around Token operations and management, the platform connects model resources, AI service operations, and business applications, helping enterprises achieve traceable Token consumption, analyzable usage costs, and optimized resource allocation. By building a closed-loop AI resource management and cost operations system, the platform makes AI investments more transparent and efficient, driving enterprise AI capabilities from “accessible” to “efficiently operated”.
Focusing on Large-Scale Enterprise LLM Adoption and Addressing New Challenges in AI Cost Management
As large language models are gradually integrated into business scenarios, more and more enterprises are incorporating LLM capabilities into R&D, marketing, customer service, operations, knowledge management, and other business processes. At the same time, a new management challenge is emerging:
When Token becomes a fundamental resource for enterprises to utilize AI capabilities, how can organizations effectively manage Token consumption and AI usage costs in the same way they manage cloud resources and computing resources?
In practical applications, enterprises commonly face the following challenges:
1. Invisible Token Costs
Business teams can quickly apply for and use large language model services, but management teams often find it difficult to gain a comprehensive understanding of Token consumption.
How many Tokens are consumed by different departments? Which applications generate the majority of usage costs? Are investments in different business scenarios reasonable? Enterprises often lack a unified perspective for analysis, causing AI costs to gradually become an “invisible expense”.
2. Difficulty in Controlling Token Resources
As the number of LLM applications increases, the demand for Token resources from different teams and business units continues to grow. Enterprises often lack unified mechanisms for Token requests, allocation, quota management, and usage standards, which can lead to disorganized resource utilization and continuously increasing AI service costs, making it difficult to achieve refined AI resource management.
3. Difficulty in Measuring Token Value
Different models and tasks consume different amounts of Tokens and generate different levels of business value. Enterprises need to understand the business contribution behind each Token consumption event, evaluate whether AI investments are truly improving efficiency and creating value, rather than simply pursuing higher usage volumes.
4. Difficulty in Sustaining Token Operations
AI applications are not a one-time deployment, but a continuous operational process. Enterprises need to establish a complete operational closed loop covering Token requests, usage monitoring, cost analysis, and resource optimization, transforming Token resources from something that is merely “consumed” into enterprise AI assets that are “manageable, optimizable, and continuously operated”.
The Futong TokenWise Token Operations Platform is designed around the needs of enterprises adopting AI at scale. Through unified management, intelligent optimization, and continuous operations, the platform helps enterprises establish a Token resource operations and cost management system, enabling visible Token usage, controllable resource allocation, and measurable investment value.
Building a “Supply–Operations–Consumption” Closed Loop to Enable Unified LLM Capability Operations
Futong TokenWise builds a three-in-one Token “supply–operations–consumption” platform system around the entire lifecycle of enterprise AI applications: “Supply” provides model resources, “Operations” enables unified management and operations, and “Consumption” delivers AI capabilities to business applications. Together, these capabilities form a complete closed loop covering model resource integration, capability operations, and business utilization.
Supply Side: Unified Integration of Multi-Source Model Capabilities
Futong TokenWise is compatible with mainstream large language models and enterprise-developed models. Through unified interface protocols, the platform abstracts technical differences between different models. Enterprises only need to complete one integration to access diverse AI capabilities on demand, without repeated development and adaptation when adding new models in the future.
Core capabilities include:
Multi-source model unified integration:
Supports standardized integration and unified management of mainstream industry large language models and enterprise-owned models.
Model status monitoring:
Monitors the availability and response status of each model service in real time, ensuring stable supply from the model resource pool.
Model performance validation:
Supports effectiveness validation and comparative evaluation before new models are deployed, providing decision-making support for model onboarding from the operations perspective.
Unified protocol encapsulation:
Encapsulates differences between various models into unified calling capabilities through standardized interface protocols, reducing adaptation costs for business applications.
Token pooling:
Integrates Token resources from different channels into a unified internal resource pool, which can then be allocated according to enterprise-specific requirements, ensuring continuity for critical business applications.
Through these capabilities, Futong TokenWise helps enterprises avoid repeated interface adaptation, duplicated calling logic development, and rebuilding of monitoring pipelines every time a new model is introduced, significantly reducing the engineering costs and maintenance burden associated with AI capability adoption.
Operations Side: Enabling Granular Token Resource Management
Futong TokenWise takes Token as the core resource for LLM service consumption and builds unified operational management capabilities across the entire lifecycle of model services, enabling every AI service request to be measured, controlled, and optimized.
Core capabilities include:
Model service management:
Unified management of deployed model services, providing clear visibility into the status and usage of each model to support operational decision-making.
Tenant and permission management:
Configures access permissions based on organizational structures, ensuring secure and controllable model usage.
Token allocation and measurement:
Precisely allocates quotas by department and individual users, records Token consumption, and provides multi-dimensional statistics based on models, business scenarios, user teams, and other factors.
Service package management:
Provides flexible configuration and subscription management for model service packages to accommodate different business scales and usage frequencies.
Usage analytics and operational monitoring:
Monitors usage activities in real time, identifies anomalies promptly, and supports operational decision-making.
Through these capabilities, enterprises can achieve granular Token usage management — from quota configuration and permission control to consumption monitoring — establishing an operational closed loop with capabilities for planning before usage, intervention during usage, and traceability after usage.
Consumption Side: Delivering AI Capabilities to Business with Transparent and Traceable Costs
Futong TokenWise provides standardized AI capability access and transparent cost attribution mechanisms for enterprise internal business systems and authorized users. Business systems can integrate once and access multiple model capabilities on demand without developing separate integration code for each model. Authorized users can obtain access credentials independently within their permission scope and quickly integrate AI capabilities into daily workflows.
Core capabilities include:
Standard client support:
The API keys issued by the platform support mainstream AI agent clients and programming tools available in the market.
Self-service API Key management:
Authorized users can independently request and manage access credentials without going through complex application and approval processes.
Model authorization and policy execution:
Based on business requirements, the platform assigns suitable models and invocation policies to different teams, ensuring alignment between capability supply and business scenarios while maintaining business continuity.
Usage experience and validation environment:
Provides a visual model experience and debugging environment, helping business teams validate performance before formal integration.
Financial center:
Enables users to view personal and team usage records and cost details, ensuring that costs can be accurately attributed to specific usage scenarios.
Through these capabilities, business teams can independently access AI capabilities within their authorized scope, while every expense is automatically attributed to the corresponding team and scenario. This transforms AI costs from an unclear expense into a measurable, allocatable, and optimizable business investment.
From Connection to Operations: Building Enterprise-Level AI Resource Optimization Capabilities
Unlike gateway-based products that primarily focus on model invocation connectivity, Futong TokenWise focuses more on the long-term operation of enterprise AI capabilities. The platform builds differentiated capabilities across three dimensions, making model invocation more controllable and cost-efficient, while enabling continuous accumulation of operational value over time.
Enhancement and Governance
After enterprises scale their AI adoption, the key challenge is no longer simply “whether models can be accessed”, but how to control usage costs and improve resource efficiency while ensuring model performance.
Futong TokenWise builds unified enhancement and governance capabilities between models and business applications. Through semantic caching, content security, intelligent routing, and other capabilities, the platform optimizes the entire model invocation process.
For high-frequency scenarios such as customer service and knowledge management, the platform reduces repeated model requests through semantic caching, lowering Token consumption by 20%–30%. For enterprise-sensitive data, it supports data desensitization before invocation and content security controls to meet Multi-Level Protection Scheme (MLPS) and generative AI compliance requirements. Meanwhile, the platform supports multi-level invocation strategy configuration, helping enterprises flexibly balance model performance and resource costs across different business scenarios, continuously improving AI service efficiency and optimizing operational costs.
Data Flywheel
As the scale of AI applications continues to expand, enterprises need to continuously accumulate usage data to support model selection optimization, cost analysis, and AI application value assessment.
Futong TokenWise transforms every model invocation into enterprise-specific data assets, continuously accumulating operational data such as business data, model evaluation results, invocation strategies, and Token consumption. All data is retained within the enterprise’s own domain, building a data flywheel that covers model evaluation, cost analysis, strategy optimization, and operational decision-making.
Based on continuously accumulated data assets, Futong TokenWise helps enterprises gain insights into resource consumption and usage effectiveness across different models and business scenarios. The platform optimizes model selection, routing strategies, and resource allocation, improving the alignment between models and business scenarios while enhancing Token utilization efficiency.
By reducing unnecessary invocation costs, Futong TokenWise enables enterprises to transform AI investment from “consumption management” toward “value-driven operations”.
Delivery and Operations
Enterprise AI capability development does not end with platform deployment, but relies on continuous optimization throughout long-term operation. Futong TokenWise provides delivery assurance and operational optimization capabilities throughout the entire platform lifecycle, helping enterprises reduce the complexity of AI deployment and operations while ensuring stable performance and continuous value creation.
During the deployment phase, Futong Technology provides capabilities including trusted innovation ecosystem adaptation and Multi-Level Protection Scheme (MLPS) compliance support, helping enterprises accelerate AI platform implementation while reducing infrastructure construction and compliance adaptation costs.
During the operation phase, the platform ensures business continuity through mechanisms such as dual-domain deployment, traffic isolation, and multi-model disaster recovery. The overall service availability reaches 99.95%, reducing business impact caused by fluctuations in model services.
During continuous operations, the platform generates optimization recommendations based on model invocation data, resource consumption patterns, and business usage effectiveness. This helps enterprises continuously optimize model configurations, invocation strategies, and resource investments, improving AI resource utilization efficiency and optimizing operational costs.
Futong TokenWise Token Operations Platform v1.0 is Now Officially Available
By making Token usage transparent, cost investments measurable, and resource allocation optimizable, Futong TokenWise helps enterprises move from passively bearing AI usage costs toward proactively managing the value of AI investments, enabling sustainable long-term AI capability development.
Futong Technology welcomes industry customers and partners to engage in discussions and collaboration to explore new approaches for large-scale enterprise adoption of large language models.
