Multi-Provider AI That Never Fails
Single-provider AI is a single point of failure. I design architectures that route between Claude, GPT-4, Gemini, and local models based on task type, cost, and availability—with graceful degradation when any provider fails.
Your AI capability shouldn't depend on any one company's uptime.
Why Single-Provider AI Fails
Most AI integrations are tightly coupled to one provider. When that provider has issues—and they all do—your application fails too.
Single-provider dependency
When your provider goes down, everything goes down
One-size-fits-all model selection
Using expensive models for simple tasks, weak models for complex ones
No cost guardrails
Token costs spiral unpredictably, budgets blow out
Hardcoded integrations
Switching providers requires rewriting your entire AI layer
No graceful degradation
Rate limits or outages cause complete system failure
Provider Selection Matrix
Each provider has genuine strengths. The router selects based on what you're actually asking—not a one-size-fits-all default.
Claude (Anthropic)
Ideal for: Multi-step reasoning, document analysis, creative writing
GPT-4 (OpenAI)
Ideal for: General tasks, structured outputs, vision analysis
Gemini (Google)
Ideal for: Speed-critical tasks, multimodal pipelines
Self-Hosted vLLMs
Ideal for: High-volume workloads, sensitive data, predictable costs at scale
Architecture Components
A provider-agnostic layer that sits between your application and the AI providers. Swap providers without touching application code.
Router Layer
Analyses incoming requests and selects the optimal provider based on task complexity, cost constraints, and current availability.
Provider Abstraction
Unified interface across all providers. Your application code never touches provider-specific APIs directly.
Cost Controller
Real-time token tracking, budget alerts, and automatic model downgrading when approaching limits.
Fallback Chain
Configurable cascade: if primary fails, try secondary, then tertiary, then cached response or graceful error.
Response Cache
Semantic caching for repeated queries. Same question yesterday? Instant response at zero cost.
Observability
Latency, cost, success rate, and model usage dashboards. Know exactly where your AI budget goes.
Self-Hosted vLLMs: Scalable & Elastic
At scale, API costs become unsustainable. Self-hosted vLLMs give you the power of large language models with elastic infrastructure that scales with demand—and costs that don't spiral.
Elastic Scaling
Scale from zero to hundreds of GPUs based on demand. Pay for compute when you need it, scale down when you don't.
Predictable Costs
No per-token pricing surprises. At scale, self-hosted models cost a fraction of API calls.
Data Sovereignty
Your data never leaves your infrastructure. Critical for regulated industries, sensitive IP, and privacy requirements.
Custom Fine-Tuning
Train on your domain data. A fine-tuned 7B model often outperforms generic 70B models on your specific tasks.
No Rate Limits
Your infrastructure, your throughput. No waiting for API quotas or dealing with throttling.
Model Selection Freedom
Run Llama, Mistral, Qwen, or any open model. Switch models without changing providers.
When to Self-Host
- +Processing 100K+ tokens/day consistently
- +Sensitive data that can't leave your network
- +Need for custom fine-tuned models
- +Predictable budgets without per-token surprises
The Stack
RAG Pipelines: Your Knowledge, AI-Powered
Generic LLMs don't know your business. RAG (Retrieval Augmented Generation) grounds AI responses in your actual documents, policies, and data—reducing hallucinations and building domain-specific intelligence.
Document Ingestion
Ingest PDFs, docs, wikis, code repositories, databases—any knowledge source. Chunking strategies optimised for retrieval quality.
Vector Embeddings
Convert documents to semantic vectors using models matched to your domain. Store in purpose-built vector databases.
Hybrid Retrieval
Combine semantic search with keyword matching. Neither alone is sufficient—hybrid retrieval gets the best of both.
Context Assembly
Smart context window management. Retrieve relevant chunks, rerank by relevance, fit within token limits.
Grounded Generation
LLM responses cite sources. Reduce hallucinations by grounding answers in your actual documents.
Continuous Updates
Knowledge bases that stay current. Incremental indexing as documents change—not periodic full rebuilds.
Build Domain-Specific Knowledge
Internal Knowledge Base
Policies, procedures, historical decisions. Employees ask questions, get answers grounded in actual company documentation.
Customer Support AI
Product docs, FAQs, support history. AI that actually knows your product, not generic responses.
Research & Analysis
Technical papers, reports, market data. Query your research corpus with natural language.
Graceful Degradation Patterns
When things go wrong—and they will—the system adapts instead of failing.
Primary provider rate limited
Route to secondary provider with equivalent capability
User sees no difference, request completes normally
All external providers unavailable
Fall back to local model or cached responses
Degraded but functional—better than error pages
Budget threshold reached
Switch to cheaper models or reduce response quality
System stays online, cost stays controlled
Complex task on simple model
Decompose into subtasks, distribute across appropriate models
Better results than forcing one model to do everything
Cost Optimisation
Smart routing doesn't just improve reliability—it cuts costs. Simple queries go to cheap models. Complex queries go to capable models. Repeated queries hit the cache.
40-60%
99.9%
0
Technical Stack
Ready for Resilient AI?
Whether you're starting fresh or refactoring existing integrations, I can design an AI architecture that won't fail when your primary provider does.