LLMOps Blueprint
Select → Engineer → Evaluate → Deploy → Observe → Optimize. Production-grade operations for large language models.
Seven stages from model selection to continuous optimization
Click any stage for technical depth.
Model Selection
Evaluating API vs self-hosted, cost modeling, capability benchmarking.
Every LLMOps pipeline starts with the right model for the job. We profile candidates — OpenAI, Anthropic, Google, Meta, Mistral — against domain-specific tasks measuring accuracy, latency, and cost per token. A structured evaluation matrix weighs context window, license restrictions, data-residency requirements, and multi-modal capabilities. Cost projections model monthly spend at projected traffic volumes, comparing API pricing tiers against self-hosted infrastructure amortization.
Four deployment architectures compared
Each pattern trades simplicity for resilience and cost-efficiency. Most production systems evolve from single-model to router or fallback chain.
Single Model
One model serves all requests. Simple, predictable, easy to manage.
Low-complexity use cases with uniform request types
Model Router
Route by complexity and cost. Optimal cost-quality balance across workloads.
Mixed workloads with varying complexity and cost sensitivity
Fallback Chain
Primary → secondary → tertiary. Maximum reliability with graceful degradation.
Mission-critical applications requiring 99.9%+ uptime
Ensemble
Multiple models + aggregation. Highest quality but highest cost.
High-stakes decisions where accuracy outweighs cost
Real-time operational visibility
Every LLM call traced. Latency, cost, quality, and errors — all in one pane of glass.
Operationalize your LLM stack.
Describe your model usage and operational challenges. We'll design the full LLMOps pipeline from evaluation to monitoring.