Traditional cloud-based LLMs present significant hurdles for enterprise adoption. These challenges often create barriers to security, scalability, and cost-effectiveness.
Sending sensitive, proprietary data to third-party cloud services creates significant security vulnerabilities and compliance challenges.
Pay-per-token models become prohibitively expensive at scale, especially for applications with high-volume, repetitive tasks.
Round-trips to the cloud introduce delays, making cloud-based models unsuitable for real-time, mission-critical applications.
Small Language Models (SLMs) are orders of magnitude smaller than their large counterparts, enabling them to run efficiently on local hardware without sacrificing core capabilities.
Agentic workflows transform passive models into proactive problem-solvers that can perceive, reason, and act to achieve complex goals.
Ensure sensitive data never leaves your network, meeting the strictest security and compliance standards.
Shift from variable API costs to a predictable, fixed-asset model, drastically reducing TCO at scale.
Achieve near-zero latency by processing data on-premise, enabling real-time decision-making.
This entire workflow executes within the enterprise firewall, ensuring data remains secure and response times are minimal.
A comparison of key performance metrics for an internal support agent handling queries based on proprietary company documents highlights the clear advantages of local deployment.