Experimenting with GLM-4.7 for internal tools and workflows led to deploying it behind a Claude-compatible API, offering a cost-effective alternative for tasks like agent experiments and code-related activities. While official APIs are stable, their high costs for continuous testing prompted the exploration of self-hosting, which proved cumbersome due to GPU management demands. The current setup with GLM-4.7 provides strong performance for code and reasoning tasks, with significant cost savings and easy integration due to the Claude-style request/response format. However, stability relies heavily on GPU scheduling, and this approach isn't a complete replacement for Claude, especially where output consistency and safety are critical. This matters because it highlights a viable, cost-effective solution for those needing flexibility and scalability in AI model deployment without the high costs of official APIs.
