CLIENT
B2B SaaS Company (Anonymized)
TIMELINE
12 weeks
SERVICES
LLM Orchestration, Infrastructure, Cost Optimization
STACK
LiteLLM, Kubernetes, Redis, Prometheus, Terraform
// 01
TheProblem
Six-figure monthly LLM spend. Zero failover. 30% of eng time on integration maintenance.
A fast-growing B2B SaaS company had integrated multiple LLM providers across their product — GPT-4 for complex reasoning, Claude for long-context tasks, and Mistral for high-throughput classification. Each integration was built independently by different teams, resulting in duplicated caching logic, inconsistent error handling, no cost visibility, and zero failover capability.
Monthly LLM spend had grown to six figures with no clear attribution to product features. When a single provider experienced an outage, multiple product features went down simultaneously. The engineering team was spending 30% of their time maintaining LLM integrations rather than building product features.
// 02
OurApproach
A centralized orchestration platform abstracting all providers behind a unified API.
We designed and deployed a centralized LLM orchestration platform that abstracts provider-specific details behind a unified API. The platform handles model routing, semantic caching, rate limiting, cost tracking, and automatic failover — allowing product teams to consume LLM capabilities through a single interface.
The routing layer implements intelligent model selection based on task complexity, latency requirements, and cost constraints. Simple classification tasks route to smaller, cheaper models while complex reasoning tasks use more capable (and expensive) models. A semantic cache built on Redis with embedding-based similarity matching reduces redundant API calls by 35%, directly impacting cost.
The infrastructure runs on Kubernetes with auto-scaling based on request queue depth, not just CPU utilization. This prevents the cold-start latency spikes that plagued the previous setup during traffic bursts. Prometheus and Grafana dashboards provide real-time visibility into per-feature, per-model cost attribution.
We implemented graceful degradation patterns: when a primary model provider is unavailable, the platform automatically routes to a fallback provider with comparable capabilities. Request retries use exponential backoff with jitter, and circuit breakers prevent cascade failures. All of this is transparent to consuming applications.
The entire infrastructure is codified in Terraform, enabling reproducible deployments and disaster recovery. We set up separate staging and production environments with traffic mirroring for safe testing of routing changes.
// 03
TheResult
45% cost reduction. 99.9% uptime. AI feature velocity tripled.
LLM inference costs dropped 45% in the first month through a combination of intelligent routing, semantic caching, and the elimination of redundant calls. Cost attribution dashboards revealed that two features accounted for 60% of spend, enabling targeted optimization.
The platform has maintained 99.9% uptime across all model endpoints, including during three provider outages that would have previously caused product-wide incidents. Failover events are now invisible to end users, completing in under 200ms.
Engineering velocity on AI features tripled because product teams no longer manage individual LLM integrations. New AI features that previously took 2-3 weeks to ship now deploy in 3-5 days, since the orchestration layer handles the operational complexity. The platform currently processes 2M+ requests daily across 14 product features.
// IMPACT
reduction in LLM inference costs
uptime across all model endpoints
to replace fragmented model integrations
“We went from managing 6 different LLM integrations with duct tape to a unified platform that auto-routes, caches, and fails over gracefully. Our AI feature velocity tripled.”