Kuldeep Paul

Google's Gemini CLI has rapidly become a go-to terminal-based coding agent, offering advanced reasoning capabilities and tight integration with the Google ecosystem. But production engineering teams rarely operate within a single provider's walled garden. Codebases demand different models for different tasks: a reasoning-heavy model for architectural

Claude Code is one of the most capable agentic coding tools available today, bringing AI-powered development directly into the terminal. However, it is locked to Anthropic's model family by default. This means teams that want to experiment with GPT-5, Gemini 2.5 Pro, Groq, or models hosted on

LLM API costs are one of the fastest-growing line items in enterprise technology budgets. A customer support agent handling 10,000 daily conversations can generate over $7,500 per month in API costs alone. Multiply that across multiple teams, products, and providers, and costs quickly become unpredictable and unmanageable. The

Enterprise AI adoption is accelerating. Organizations are no longer running a single LLM in isolation. They are operating across OpenAI, Anthropic, Google Gemini, AWS Bedrock, and Azure simultaneously, often across multiple teams, products, and environments. Without a unified control layer, this quickly becomes a tangle of fragmented API integrations, untracked