Introduction
LinkedIn's GenAI platform powers numerous AI features across the professional network. This case study examines the architecture decisions and lessons learned building an enterprise GenAI platform.
Platform Requirements
Scale
- Millions of daily requests across products
- Sub-second latency for interactive features
- High availability (99.9%+ uptime)
Flexibility
- Multiple model support (GPT, Claude, Llama)
- Easy feature development for product teams
- Rapid iteration on prompts and models
Architecture Overview
Core Components
+------------------+
| API Gateway |
+--------+---------+
|
+--------------+--------------+
| | |
+-------v----+ +------v-----+ +-----v------+
| Prompt | | Model | | Response |
| Manager | | Router | | Processor |
+------------+ +------------+ +------------+
| | |
+--------------+--------------+
|
+--------v---------+
| Model Serving |
| (vLLM, TGI) |
+------------------+
Prompt Management
- Version control for prompts
- A/B testing framework
- Evaluation pipelines
Model Routing
- Cost-based routing: Use cheaper models when sufficient
- Latency-based routing: Route to fastest available
- Capability-based routing: Match model to task
Key Design Decisions
Build vs. Buy
LinkedIn chose to build:
- Model serving infrastructure
- Prompt management system
- Evaluation framework
And buy/use:
- Base models (mix of proprietary and open-source)
- Vector databases
- Observability tools
Multi-Model Strategy
Benefits:
- Avoid vendor lock-in
- Cost optimization
- Capability matching
Challenges:
- Prompt compatibility
- Quality consistency
- Operational complexity
Lessons Learned
1. Observability is Critical
- Log all inputs and outputs
- Track latency distributions
- Monitor for quality degradation
2. Prompts are Code
- Version control everything
- Review changes carefully
- Test before deployment
3. Start Simple
- MVP first, optimize later
- Don't over-engineer routing
- Focus on user value
Build your own GenAI platform with insights from our LLM Inference at Scale course.