Avoiding Vendor Lock-In in AI
Avoid AI vendor lock-in by standardising on MCP, portable eval harnesses, and VPC deployment — so models and tools swap without re-architecting workflows.
Avoid AI vendor lock-in by standardising on MCP, portable eval harnesses, and VPC deployment — so models and tools swap without re-architecting workflows.
Last updated:
Architecture choices
Proprietary agent platforms tie you to one vendor's runtime. Services-led delivery on open patterns preserves flexibility as models commoditise.
Derisk360 builds in your cloud with documented interfaces — you own the deployment.
Practitioner perspective from production implementations
Focused on deployment risk — not model hype
Applicable to banking, insurance, and regulated enterprises
Related resources
- Derisk360 vs Databricks for Enterprise AI
Derisk360 vs Databricks for Enterprise AI — decision guide for enterprise AI buyers.
- Vendor vs Services for Enterprise AI
Vendor vs Services for Enterprise AI — practical enterprise AI deployment guide from Derisk360.
- Model Context Protocol
What is Model Context Protocol? Model Context Protocol (MCP) standardises how agents connect to tools, data, and enterprise systems.
Ready for an AI implementation partner?
Book a discovery call and we'll map your highest-value use case — and exactly how we get it into production.
Frequently asked questions
- What is Derisk360?
- An enterprise AI services firm running accelerators and production implementations with embedded FDEs.
- Who writes Derisk360 insights?
- Practitioners — Forward Deployed Engineers and delivery leads with production experience in regulated enterprises.
- How do I apply this insight?
- Book a discovery call at derisk360.com/book. We map your use case and scope a governed production accelerator.