Version: 1.0 Architecture
Status: Pre-Implementation (Ready to Build)
Last Updated: May 31, 2026
Build an enterprise-grade, multi-provider LLM Gateway that serves as the foundation for an AI operating system. This is NOT a chatbot, NOT a simple OpenAI wrapper, NOT a training platform.
This is a reusable enterprise AI infrastructure platform designed to:
- Abstract multiple LLM providers (Ollama, OpenAI, Anthropic, AWS Bedrock, Azure OpenAI)
- Route requests intelligently based on capabilities, cost, and performance
- Track usage, costs, and model performance
- Evaluate and compare models
- Support hundreds of teams
- Evolve into a comprehensive AI governance and strategy platform
LLM-GATEWAY/
├── README.md ← You are here
├── ARCHITECTURE.md ← Complete system architecture
├── PROJECT-STRUCTURE.md ← Detailed project layout
├── INTERFACES-AND-MODELS.md ← Domain interfaces & entities
├── DESIGN-DECISIONS.md ← Design patterns & rationale
└── IMPLEMENTATION-ROADMAP.md ← 10-phase implementation plan
| Document | Purpose |
|---|---|
| ARCHITECTURE.md | Start here for overall system design, layers, and concepts |
| PROJECT-STRUCTURE.md | File organization, folder layout, project dependencies |
| INTERFACES-AND-MODELS.md | Core interfaces, domain models, value objects |
| DESIGN-DECISIONS.md | Why certain patterns were chosen, tradeoffs, diagrams |
| IMPLEMENTATION-ROADMAP.md | Detailed timeline for building each phase |
┌──────────────────────────────────────────────┐
│ CLIENT APPLICATIONS │
│ (Teams, Services, External Integrations) │
└────────────────────┬─────────────────────────┘
│
┌────────────────────▼─────────────────────────┐
│ LLM GATEWAY API LAYER │
│ ├─ Validation & Auth │
│ ├─ Request Routing │
│ └─ Response Formatting │
└────────────────────┬─────────────────────────┘
│
┌────────────────────▼─────────────────────────┐
│ PROVIDER ABSTRACTION LAYER │
│ ├─ Ollama (Local) [Qwen, Llama, Mistral] │
│ ├─ OpenAI (GPT-4, GPT-3.5, Embeddings) │
│ ├─ Anthropic (Claude) │
│ ├─ AWS Bedrock (Multiple models) │
│ └─ Azure OpenAI (Enterprise Azure) │
└────────────────────┬─────────────────────────┘
│
┌────────────────────▼─────────────────────────┐
│ CORE SERVICES │
│ ├─ Model Registry (What models exist) │
│ ├─ Routing Engine (Which model to use) │
│ ├─ Request Pipeline (How to process) │
│ ├─ Evaluation Framework (How well works) │
│ └─ Comparison Engine (Model vs Model) │
└──────────────────────────────────────────────┘
PRESENTATION
↓ (depends on)
APPLICATION
↓ (depends on)
DOMAIN ← INFRASTRUCTURE
↓ (implements
COMMON interfaces)
Different LLM backends that can be swapped transparently.
Supported Providers:
├── Ollama (Local, self-hosted)
├── OpenAI (Cloud API)
├── Anthropic (Claude API)
├── AWS Bedrock (Enterprise AWS)
└── Azure OpenAI (Enterprise Azure)
All implement: ILLMProvider interface
Added via: Factory pattern
Testable: With mock implementations
Specific LLM models registered in the platform.
Example Models:
├── gpt-4 (OpenAI, $15/1M tokens)
├── claude-3-opus (Anthropic, $15/1M tokens)
├── llama2 (Ollama, free/local)
├── mistral (Ollama, free/local)
└── qwen:7b (Ollama, free/local)
Each Model has:
├── Name & Version
├── Provider & Capabilities
├── Context Window Size
├── Cost Metadata
├── Status (Active, Testing, Deprecated)
└── Performance Metrics
What clients want to do (not which model to use).
Supported Use Cases:
├── Chat (Conversational)
├── Summarization (Content compression)
├── Classification (Category assignment)
├── Extraction (Information extraction)
└── Research (Deep analysis)
Benefits:
✓ Client doesn't pick model
✓ Gateway picks best model for use case
✓ Models can be swapped transparently
✓ Cost optimization possible
✓ Performance optimization possible
How the gateway decides which model to use.
Routing Strategies:
├── Capability-Based (Use case → Model with capability)
├── Cost-Optimized (Minimize spend)
├── Performance-Based (Minimize latency)
├── Provider-Based (Prefer specific provider)
├── Tenant-Specific (Custom routing per tenant)
└── Hybrid (Combine multiple factors)
Decision Factors:
├── Requested use case
├── Available models
├── Tenant preferences
├── Cost budget
├── Performance SLO
└── Provider availability
How each request is processed.
Processing Steps:
1. Validation (Format, required fields)
2. Authentication (API key verification)
3. Authorization (Tenant access, quota)
4. Enrichment (Add metadata, context)
5. Routing (Select model & provider)
6. Invocation (Call provider API)
7. Logging (Store audit trail)
8. Metrics (Track performance, cost)
Each step is:
✓ Independent
✓ Testable
✓ Reorderable
✓ Skippable (with conditions)
✓ Extensible
| Component | Technology | Why? |
|---|---|---|
| Language | C# | Type-safe, excellent .NET ecosystem |
| Framework | ASP.NET Core 8+ | High performance, built-in DI, cross-platform |
| Database | PostgreSQL | Open source, JSON support, reliable |
| ORM | Entity Framework Core | .NET native, queryable, migrations |
| Logging | Serilog | Structured logging, context enrichment |
| API Docs | Swagger/OpenAPI | Standard, interactive documentation |
| Serialization | System.Text.Json | Fast, built-in, modern |
| HTTP Client | HttpClientFactory | Connection pooling, resilience |
// Resilience
Polly // Circuit breaker, retry policies
// Testing
xUnit // Test framework
Moq // Mocking
FluentAssertions // Assertions
WebApplicationFactory // Integration testing
// Cloud SDKs
AWSSDK.Bedrock // AWS provider
Azure.Identity // Azure auth
Azure.Security.KeyVault // Secret management
// Utilities
Humanizer // Human-readable strings
CSharpier // Code formatting
SonarAnalyzer // Code qualityPhase 1: Core Gateway Infrastructure (Weeks 1-2)
Phase 2: Provider Abstraction Layer (Weeks 2-4)
Phase 3: Model Registry (Weeks 4-6)
Phase 4: Routing Engine (Weeks 6-8)
Phase 5: Local Model Support (Ollama) (Weeks 8-10)
Phase 6: External Model Support (Weeks 10-14)
Phase 7: Request Pipeline & Logging (Weeks 14-16)
Phase 8: Evaluation Framework (Weeks 16-18)
Phase 9: Model Comparison (Weeks 18-20)
Phase 10: Platform Readiness & Evolution (Weeks 20-22)
Total: ~6 months for complete platform
See IMPLEMENTATION-ROADMAP.md for:
- Detailed objectives for each phase
- Specific deliverables
- Acceptance criteria
- Key challenges & mitigation
- Testing strategy
- Dependencies & risks
S - Single Responsibility
Each class has one reason to change
Example: RoutingStrategy only decides which model
O - Open/Closed
Open for extension (new providers, strategies)
Closed for modification (existing code unchanged)
L - Liskov Substitution
Any provider can replace any other
Any routing strategy can replace another
I - Interface Segregation
Clients depend on focused interfaces
Not forced to depend on unused methods
D - Dependency Inversion
Depend on abstractions (ILLMProvider)
Not concrete implementations (OpenAIProvider)
| Pattern | Purpose | Where |
|---|---|---|
| Factory | Create provider instances | ILLMProviderFactory |
| Strategy | Pluggable routing strategies | IRoutingStrategy |
| Pipeline | Chain request processing | IPipelineStep |
| Repository | Data access abstraction | IRepository |
| Adapter | Normalize provider APIs | Each provider implementation |
| Circuit Breaker | Resilience | Polly integration |
| Value Object | Domain concepts | TokenUsage, CostMetadata |
| Specification | Query composition | ISpecification |
# Development
dotnet 8.0 or later
Visual Studio 2022 or VS Code
PostgreSQL 14+
Docker & Docker Compose (for Ollama)
# Optional
.NET CLI tools
Entity Framework Core tools# 1. Clone repository
git clone https://github.com/your-org/llm-gateway.git
cd llm-gateway
# 2. Install dependencies
dotnet restore
# 3. Create database
dotnet ef database update -p src/LLMGateway.Infrastructure
# 4. Seed test models
dotnet run --project src/LLMGateway.API -- seed-models
# 5. Start local Ollama (optional)
docker-compose -f compose/docker-compose.yml up -d
# 6. Run API
dotnet run --project src/LLMGateway.API
# 7. Browse
http://localhost:5000/swaggerInfrastructure
├── 5+ providers integrated
├── 500+ models registered
├── 99.9% uptime
└── < 1% error rate
Performance
├── Request latency < 50ms (p95)
├── Provider call latency < 2000ms (p95)
├── Provider health checks < 100ms
└── Routing decision < 10ms
Features
├── Full request tracing
├── Comprehensive evaluation
├── Model comparison working
├── Evaluation framework storing data
└── Clear extension points
Learning
├── Prepared for governance platform
├── Prepared for knowledge platform
├── Prepared for agent platform
├── Prepared for prompt registry
└── Prepared for evaluation platform
This gateway is foundation for:
┌─────────────────────────────────────────────────────┐
│ AI OPERATING SYSTEM (Future) │
├─────────────────────────────────────────────────────┤
│ ┌──────────────┐ ┌──────────────────────────┐ │
│ │ Governance │ │ Knowledge Platform │ │
│ │ Platform │ │ (Prompts, Templates) │ │
│ └──────────────┘ └──────────────────────────┘ │
│ ┌──────────────┐ ┌──────────────────────────┐ │
│ │ Agent │ │ Prompt Registry │ │
│ │ Platform │ │ (Version, Evaluate) │ │
│ └──────────────┘ └──────────────────────────┘ │
│ ┌──────────────┐ ┌──────────────────────────┐ │
│ │ Evaluation │ │ Model Strategy Platform │ │
│ │ Platform │ │ (Optimize, Plan) │ │
│ └──────────────┘ └──────────────────────────┘ │
├─────────────────────────────────────────────────────┤
│ LLM GATEWAY PLATFORM (This Project) │
├─────────────────────────────────────────────────────┤
│ Providers | Registry | Routing | Evaluation | Audit │
├─────────────────────────────────────────────────────┤
│ Ollama | OpenAI | Claude | Bedrock | Azure OpenAI │
└─────────────────────────────────────────────────────┘
For deep dives into specific areas:
- System design: ARCHITECTURE.md
- Request flow: DESIGN-DECISIONS.md (Sequence Diagrams)
- Provider integration: ARCHITECTURE.md (Provider Abstraction section)
- Project layout: PROJECT-STRUCTURE.md
- Phase details: IMPLEMENTATION-ROADMAP.md
- Timelines & risks: IMPLEMENTATION-ROADMAP.md
- Interfaces: INTERFACES-AND-MODELS.md
- Entities: INTERFACES-AND-MODELS.md
- Value objects: INTERFACES-AND-MODELS.md
- Pattern rationale: DESIGN-DECISIONS.md
- Tradeoffs: DESIGN-DECISIONS.md
- Alternatives considered: DESIGN-DECISIONS.md
Before implementation begins, validate:
- Clean Architecture layers understood
- Dependency flow correct (no circular deps)
- Domain layer is provider-agnostic
- Extension points identified
- ILLMProvider interface complete
- Factory pattern for provider creation
- Each provider can be tested independently
- Streaming support designed
- Database schema appropriate
- Entities and value objects defined
- Repository pattern understood
- Migrations strategy clear
- Routing interface defined
- Multiple strategies designed
- Strategy selection logic clear
- Fallback handling designed
- Pipeline steps identified
- Step ordering correct
- Error handling per step
- Context propagation clear
- Unit test strategy clear
- Integration test approach defined
- Performance testing planned
- E2E testing strategy planned
Key decisions documented (see DESIGN-DECISIONS.md):
- Clean Architecture + DDD - Why layering matters
- Factory Pattern - Provider instantiation
- Strategy Pattern - Pluggable routing
- Pipeline Pattern - Request processing
- Repository Pattern - Data access
- Value Objects - Domain modeling
- Request Tracing - Observability
- ✅ Review ARCHITECTURE.md
- ✅ Review PROJECT-STRUCTURE.md
- ✅ Review INTERFACES-AND-MODELS.md
- ✅ Review DESIGN-DECISIONS.md
- ✅ Get stakeholder approval
- ✅ Review IMPLEMENTATION-ROADMAP.md
- ⬜ Adjust timeline based on team capacity
- ⬜ Set up development environment
- ⬜ Create project structure
- ⬜ Setup DI container
- ⬜ Configure logging
- ⬜ Create base project template
- ⬜ Setup CI/CD pipeline
- ⬜ Follow implementation roadmap
- ⬜ Create ADRs for runtime decisions
- ⬜ Update documentation as you learn
- ⬜ Regular architecture reviews
- Overall design: See ARCHITECTURE.md
- Project structure: See PROJECT-STRUCTURE.md
- Interfaces & models: See INTERFACES-AND-MODELS.md
- Design patterns: See DESIGN-DECISIONS.md
- Implementation timing: See IMPLEMENTATION-ROADMAP.md
✅ Complete architecture defined
✅ All interfaces specified
✅ All domain models defined
✅ Design patterns documented
✅ Sequence diagrams provided
✅ Implementation roadmap detailed
✅ Design decisions explained
✅ Tradeoffs documented
✅ Future evolution planned
✅ Success metrics defined
This is a comprehensive, production-ready architecture for an enterprise-grade LLM Gateway platform. It's:
- Extensible: New providers and strategies without code changes
- Observable: Full request tracing and metrics
- Scalable: Designed for hundreds of teams, billions of requests
- Testable: Clean architecture enables unit testing
- Maintainable: Clear separation of concerns
- Future-proof: Foundation for AI operating system
The foundation is solid. The implementation is well-planned. The future is extensible.
| Version | Date | Changes |
|---|---|---|
| 1.0 | May 31, 2026 | Initial architecture definition |
Ready to build? Start with ARCHITECTURE.md then follow IMPLEMENTATION-ROADMAP.md.