?? Part3?? To move beyond lightweight wrappers and build enterprise-grade AI products
George Polzer
Sr. Product Manager AI/ML | EU & US Go-to-Market / MVP Consultant | Emerging Tech - Agentic AI, Agent Ops Focus??
?? Part3?? To move beyond lightweight wrappers and build enterprise-grade AI products, startups must adopt a robust tech stack that addresses scalability, compliance, integration, and governance. Below is an illustrative enterprise-ready stack.
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?? The Shift Needed for AI Wrappers
? From "Thin UX Wrappers" → to Enterprise AI Infrastructure
? From API Dependence → to Model-Agnostic AI Middleware
? From UX-first → to Enterprise integration, security, & efficiency
?? How Startups Can Build a Real Moat with AI Wrappers
? Multi-LLM Support: Enable enterprises to switch models on demand (GPT-4, Claude, Mistral AI, DeepSeek).
? On-Prem & Private Cloud: Offer self-hosted AI in secure enterprise environments.
? LLMOps & Observability: Track AI performance, governance, and compliance.
? Fine-Tuning & Customization: Let businesses train AI models on proprietary datasets.
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?? Enterprise AI Tech Stack Breakdown
1?? AI Model Deployment & Management
? Multi-LLM Switching (GPT-4, Claude, DeepSeek, Mistral)
? Fine-Tuning & Custom Models (Hugging Face, vLLM, LoRA)
? On-Prem AI Hosting (NVIDIA Triton, MLflow, OpenLLM)
? Scalable Vector Databases (FAISS, Weaviate, Pinecone)
2?? Enterprise Integration & Security
? API & SDK Integration: REST, GraphQL, gRPC
? SSO & RBAC: Okta, Auth0, Keycloak
? Data Compliance: SOC 2, HIPAA, GDPR
? Audit Logs & Governance: AI explainability (WhyLabs, Arize AI)
3?? Scalable Infrastructure & Optimization
? High-Performance AI Serving: vLLM, TensorRT, Ray Serve
? Serverless & Kubernetes Scaling: AWS Lambda, @K8s
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? AI Observability & Monitoring: Datadog, Prometheus Group, Grafana
4?? AI Cost Optimization & Multi-Tenant AI
? Cost & Token Monitoring (LangSmith, OpenTelemetry)
? Multi-Tenant Support (Kubernetes Namespaces, SageMaker Multi-Model)
? Model Routing & Load Balancing (LiteLLM, Cohere Router)
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Agent Ops: Essential for Enterprise AI Readiness
?? Agent Ops enhances AI wrappers with observability, debugging, and orchestration.
1?? AI Observability & Performance Monitoring
?? Tracks AI interactions (LLM calls, API requests, errors).
?? Monitors latency, cost, & token usage in production.
2?? AI Debugging & Compliance
?? Logs and replays AI calls for transparency.
?? Identifies errors, bias, & provider issues.
?? Supports compliance (SOC 2, GDPR).
3?? LLMOps & Multi-Agent Orchestration
?? Tracks AI agent workflows & execution paths.
?? Integrates with LangChain, CrewAI, @AutoGen.
?? Optimizes model switching & infrastructure efficiency.
?? The Future of Wrappers = Enterprise AI Middleware
Part1??: https://lnkd.in/d-u7kRw6
Part2??: https://lnkd.in/dAMmChbs
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?? Agentic Systems are the future of AI - AI Agent Ops Framework? (AOF) Unlocks the Potential
? Join the industry's dedicated AI Agent Ops Linkedin Group: https://lnkd.in/dMDFZMJa