Simulating realistic Customer support chatbot parameters (3,500 in / 350 out with 70% cache reuse). GLM-4.5 (Z.ai) delivers a 39% cost reduction over Databricks DBRX Instruct.
| Traffic Volume Tier | GLM-4.5 (Z.ai) Monthly | Databricks DBRX Instruct Monthly | Monthly Savings by picking GLM-4.5 (Z.ai) |
|---|---|---|---|
| 1,000 reqs/mo (Dev/Testing) | $1.67 | $2.73 | Save $1.06 / mo |
| 10,000 reqs/mo (Small App) | $16.695 | $27.30 | Save $10.605 / mo |
| 100,000 reqs/mo (Growth Production) | $166.95 | $273.00 | Save $106.05 / mo |
| 1,000,000 reqs/mo (Scale SaaS) | $1,669.50 | $2,730.00 | Save $1,060.50 / mo |
GLM-4.5 (Z.ai) is 39% cheaper for Customer support chatbot workloads. At standard Customer support chatbot parameter ratios (3,500 input tokens, 350 output tokens, 70% cache hit), GLM-4.5 (Z.ai) costs $0.00167 per request compared to $0.00273 on Databricks DBRX Instruct.
GLM-4.5 (Z.ai) offers a context window of 128,000 tokens (max output: 98,304), while Databricks DBRX Instruct offers 32,768 tokens (max output: 4,096).
At 100,000 requests per month, using GLM-4.5 (Z.ai) saves $106.05 every month (or $1,272.60 annually) compared to Databricks DBRX Instruct.
Cache the system prompt and static documentation chunks — cached input is often 4–10× cheaper. Route simple FAQ turns to a nano-tier model and escalate only complex tickets. Cap max_output per reply; support answers rarely need more than a few hundred tokens.