# Community Questions **URL:** \ **Category:** FAQs\ **Tags:** questions\ **Created:** [January 19, 2026, 4:26pm UTC](https://rival.discourse.group/t/community-questions/27 "2026-01-19T16:26:00Z")\ **Posts on this page:** 1\ **Page:** 1
**Author:** ![laurabraun](https://avatars.discourse-cdn.com/v4/letter/l/f19dbf/32.png) [@laurabraun](https://rival.discourse.group/u/laurabraun)\ **Post date:** [January 19, 2026, 4:26pm UTC](https://rival.discourse.group/t/community-questions/27/1 "2026-01-19T16:26:00Z")
### Discourse Questions / Community Activity _The following are questions from our community. Feel free to add additional questions, and we’ll answer them as soon as we can._ 1. ### **For AI functions running in CortexOne, what are best practices for designing invocation workflows that balance latency and throughput?** 1. In CortexOne, we recommend designing AI function workflows around **intent-based execution**. For low-latency use cases (UI interactions, real-time responses), single-step synchronous invocations work best. For high-throughput or compute-heavy workloads, batching inputs and using asynchronous execution helps optimize cost and performance. Many teams combine both by routing requests dynamically based on payload size or SLA. 2. **How do you reliably orchestrate multi-step AI workflows (with dependencies) in the Marketplace environment?** 3. **What strategies work best for scaling AI function pipelines that integrate with event-driven systems (e.g., Kafka)?** 4. **In CortexOne, what metrics or tools do you use to benchmark and optimize GPU/CPU allocation for AI functions?** 5. **What techniques have you found effective for minimizing cold start latency in serverless AI workloads?** 6. **How do teams efficiently manage cost vs performance for high-throughput AI inference jobs?** 7. **What’s your approach to integrating CortexOne AI functions with existing CI/CD frameworks like GitHub Actions or GitLab CI?** 8. **How do you ensure robust observability and debugging for AI workflows—especially across distributed services?** 9. **Has anyone integrated CortexOne with real-time data sources (e.g., WebSockets, streaming logs)? What worked well (and what didn’t)?** 10. **How do you set up automated testing for AI functions, particularly for edge cases (noisy input, format shifts)?** 11. **What deployment patterns help you safely roll out new versions of AI functions without breaking live workload SLAs?** 12. **Do you use canary testing or feature flags for AI function updates? How do you implement rollbacks if issues appear?** 13. **What are effective best practices for securing AI API keys, tokens, and function secrets in CortexOne workflows?** 14. **How do you handle RBAC and least-privilege access for Marketplace plugins and AI function integrations?** 15. **What frameworks or tools do you use to audit and protect sensitive data in AI pipelines?** 16. **What patterns have you adopted for feedback loops that allow AI functions to learn from production results?** 17. **What are the trade-offs you see between orchestrating inference with local execution vs calling external managed model APIs?** 18. **Has anyone experimented with dynamic cost-based routing of AI tasks (e.g., choose local vs remote based on current spend)?** 19. **What folder/structure conventions do you use for organizing AI functions, data schemas, and shared libs in a Marketplace project?** 20. **How do you document shared AI workflows for new team members without overwhelming them?**