Solo.ioa leading cloud networking applications company, announced the release of Gloo AI Gatewaywhich is designed to address the fresh operate case of accelerating innovation in artificial intelligence.
Artificial intelligence continues to gain popularity across industries and is expected to grow at a rate 37.3% between 2023 and 2030. In app development, integrating AI into apps can be complicated and requires a commitment of time and resources to get started. With the Gloo AI Gateway, Solo.io builds on years of excellence from Gloo Gatewhich provides an Envoy-based API gateway and ingress controller that facilitates and secures application traffic at the edge to deliver the same speed, security, and scalability to contemporary AI applications.
Gloo AI Gateway optimizes the AI experience for Solo.io customers by providing:
- Speed of implementation:Eliminates programming friction, boilerplate code, and bugs that could be avoided in applications using LLM APIs.
- Security and control:Protects applications, models, and data from unauthorized access and ensures safe and sound operate of AI with governance controls, auditing capabilities, and consumption transparency.
- ScalabilityLeverages advanced AI integration patterns to augment data volumes and integrate with cloud gateway capabilities to support high-volume AI connectivity with zero downtime.
Key operate cases for Gloo AI Gateway include:
- Multi-LLM Provider Support: Simplifies LLM access for consumers and provides centralized control, transparency, and governance across LLM providers.
- API Key Management: Securely store LLM API keys as secrets, and generate API keys to map to one or more LLM provider secrets.
- Consumption control and transparency: Monitor and track your LLM consumption efficiently with logging, analysis and reporting features that ensure optimal resource utilization and cost efficiency.
- Term Management: Streamlines LLM application integration with rapid templating and rapid enrichment, and leverages term security and data exfiltration controls to reject invalid requests and sanitize LLM responses to ensure consistent governance and control.
- Refined Search Generation (RAG): Ensures that LLM responses are based on correct and relevant information, dynamically retrieved from external sources.