#cost-optimization

Token optimization patterns for MCP-based agents. Quantifies upfront loading overhead and reduces token costs by 70-98% through four techniques — Progressive Discovery, tool compression proxy, Code Execution, and prompt cache alignment.

Version-specific GPU checkpoint/restore constraints and an EKS graceful-drain and warm-start evidence procedure (Experimental)

GPU resource management and cost optimization using Karpenter, KEDA, and DRA on EKS

Unifying the three layers of inference caching (KV/Prefix, Prompt, Semantic) into a single decision framework with hit-rate targets, measurement points, and tuning levers for each layer.

LLM Gateway-level semantic caching strategy and implementation options comparison (GPTCache, Redis Semantic Cache, Portkey, Helicone, Bifrost+Redis)

Complexity-based automatic model routing — comparison of LLM Classifier, LiteLLM, and vLLM Semantic Router approaches, RouteLLM research reference, and cost savings

LLM platform FinOps methodology — token metering, showback/chargeback strategies, agentic cost models, budget policies, and gateway integration

A customer-facing decision guide for evaluating and choosing self-hosted open-weight LLM deployment from the perspectives of token economics and data sovereignty.

Deep optimization strategies for minimizing inter-service communication latency and reducing cross-AZ costs in EKS. Covers Topology Aware Routing, InternalTrafficPolicy, Cilium ClusterMesh, AWS VPC Lattice, and Istio multi-cluster

Operational best practices for EKS Hybrid Nodes — mixed mode workload placement, configuration validation with Cluster Insights and nodeadm debug, monitoring architecture, and cost optimization based on tiered vCPU-hour billing.