Model Strategy

Select & Configure
Foundation Models

A comprehensive engineering approach to selecting the right models, implementing flexible routing patterns, ensuring system resiliency, and managing the full model lifecycle.
Model Selection

Capability & Cost Benchmarking

Choosing the right FM is a trade-off between Reasoning Capability, Latency, and Cost. We use empirical benchmarks to map models to your specific business use cases, ensuring you don't overpay for intelligence you don't need.

Select up to 3 models to compare

Capability Analysis

Claude 3.5 Sonnet
Context200k
Cost Idx$40
Complex TasksCoding
Llama 3 70B
Context128k
Cost Idx$15
PrivateLow Cost
Frontend App
AI Gateway
Abstraction Layer
AWS Bedrock
Claude 3.5
OpenAI
GPT-4o
Flexible Architecture

Decoupled Model Routing

Hardcoding model IDs is technical debt. We implement an Abstraction Layer (via AWS Lambda or API Gateway) that allows you to switch providers instantly via configuration, without modifying application code. This prevents vendor lock-in and allows for instant upgrades.
App Configuration (AppConfig)
Live Reload
config.json
{
"model_route": "production_v1",
"providers": {
"production_v1": {
"service": "aws_bedrock",
"model_id": "anthropic.claude-3-sonnet"
}
}
}
Resilient AI Systems

Unbreakable Architecture

LLM APIs can fail. We design systems that ensure Continuous Operation during service disruptions. We implement Circuit Breaker Patterns to prevent cascading failures and Cross-Region Inference to route traffic to healthy zones automatically.

Fallback Strategies

  • 1
    Graceful DegradationSwitch to a smaller, faster model (e.g., Llama 3 8B) if the primary large model (e.g., GPT-4) times out.
  • 2
    Cached ResponseServe a semantically similar historical response if all models are unreachable.
Live Traffic Simulator
App
Circuit
Breaker
Primary Model
US-East-1
Fallback Model
AP-East-1
Deployment & Lifecycle

Customization at Scale

Managing custom FMs requires rigorous MLOps. We implement pipelines to deploy domain-specific Fine-Tuned Models using parameter-efficient techniques like LoRA. Our Model Registry ensures version control, enabling instant Rollback Strategies if a new deployment degrades performance.
Adapter Pattern (LoRA)

Instead of deploying massive full-weight models, we attach lightweight "Adapters" to a frozen base model. This reduces deployment cost by 90% and allows multi-tenant serving.

Immutable Artifacts

Every model version is hashed and stored in a Model Registry. We never overwrite; we only append new versions.

Model Registry Console

System Operational
v1.0.0
Llama-3-8B + None
Archived
v1.1.0
Llama-3-8B + LoRA-Finance-v1
Active (Prod)
v1.2.0-rc
Llama-3-8B + LoRA-Finance-v2
Canary
Registry
Llama-3-8B
LoRA-Finance-v1
RUNNING
Inference Container
Serving