u/Background_Salt6475

Has anyone else found Cortex Code to be expensive?

I set CORTEX_CODE_desktop_DAILY_EST_CREDIT_LIMIT_PER_USER to 15 credits/day (roughly a $25 daily limit), and I'm using Sonnet (not a more expensive model). Even then, I can burn through the entire daily budget in just a few debugging sessions. It feels like the credits disappear much faster than I expected.

For comparison, I have a $20/month Claude subscription, and I use it extensively for coding without ever worrying about hitting limits.

Am I missing something about how Cortex Code consumes credits? Are there best practices for reducing usage (prompting, context size, session management, model settings, etc.), or is this just the expected cost?

I'd really appreciate hearing how others are using Cortex Code in practice and what you've done to keep costs under control.

Curious if anyone here has experience building and deploying dbt-based bioanalytical/life sciences data models in Snowflake.

I’m particularly interested in learning how others have approached a few challenges:

• Incremental modeling when source systems lack reliable change tracking (e.g., no updated_timestamp, weak CDC support, limited audit metadata). How did you handle incrementals, late-arriving changes, reconciliation, or full vs hybrid refresh strategies?

• Architecting across multiple source systems, especially in environments supporting legacy-to-modern migrations. How did you model, harmonize, and reconcile data across systems while maintaining lineage, historical continuity, and a stable consumption layer?

• Life sciences / regulated environments (GxP, LIMS, clinical, bioanalytical, etc.): any lessons learned around validation, auditability, governance, semantic modeling, or balancing flexibility with compliance?

Would love to hear about patterns, tradeoffs, war stories, or things you wish you had done differently. Thanks in advance!

Has anyone here worked on NetSuite data EL/ETL into Snowflake? What tools or approaches have you used?

I’m currently evaluating options such as Fivetran, AWS Glue, and custom Python pipelines. Would love to hear about your experiences, including pros/cons, scalability, maintenance effort, CDC/incremental load support, and any lessons learned.