Why it matters: Platform teams managing AI coding assistants hit rigid subscription limits and lose cost visibility. This LLM Gateway approach enables self-managed quotas, per-team budgets, and multi-provider flexibility at enterprise scale.

AWS Solutions Architecture published a detailed guide on deploying LiteLLM as a unified gateway for Claude Code and other agentic coding assistants. The architecture—built on Amazon ECS, CloudFront, Application Load Balancer, RDS, and Amazon Bedrock—solves a critical problem: developers using Claude Code hit daily token limits and inflexible subscription quotas, while platform teams lack cost attribution and usage controls.

The LiteLLM Gateway provides API key management, rate limiting (per-user, per-team, by budget), cost tracking, and multi-provider support (Claude, OpenAI, Azure). Platform teams deploy the gateway via Docker/Terraform, configure team budgets and rate limits through a management UI, and distribute API keys. Developers configure Claude Code with environment variables pointing to the gateway instead of direct Anthropic/Bedrock endpoints. The result: centralized cost visibility, custom usage policies, and the flexibility to switch models or providers without reconfiguring developer tools.

For platform engineers, this pattern extends beyond coding assistants. The same gateway architecture applies to any LLM-powered tooling where cost control, usage tracking, and provider flexibility matter—chatbots, documentation assistants, code review agents, or infrastructure automation tools.

KEY TAKEAWAY: If you're deploying Claude Code or similar AI assistants to developer teams, implement an LLM Gateway for cost control and flexibility. Deploy LiteLLM on ECS backed by Bedrock, configure per-team budgets and rate limits, and gain centralized visibility into token consumption. This breaks dependence on vendor quota systems and enables custom policies aligned with your organization's needs. Bonus: you can switch models or providers without touching developer configurations.

Why it matters: AI systems moved to production in 2025, but traditional observability fails to capture what makes them succeed or fail—model quality, token costs, and non-deterministic behavior require fundamentally new monitoring approaches.

Dotan Horovits argues that 2025 marked the transition of AI workloads from experimentation to production, exposing critical gaps in how we measure system health. Traditional uptime and latency metrics miss what actually matters for AI systems: model accuracy, hallucination rates, content safety, and per-request token costs that now exceed typical compute expenses. AI workloads flip conventional observability assumptions—lower throughput but 2-30 second response times, massive payloads, and non-deterministic outputs that make standard debugging impossible without full prompt/response context.

The observability challenge spans six layers: application code, orchestration frameworks (LangChain, LlamaIndex), agentic workflows, model inference, RAG/vector databases, and underlying infrastructure. Each layer requires specialized instrumentation. Projects like OpenTelemetry and OpenLLMetry are standardizing AI observability signals, while platforms like Langfuse enable token-level cost attribution and quality tracking.

For platform teams, this means rethinking monitoring strategies. Track model degradation over time, measure hallucination rates in production, attribute token costs to business units, and monitor for bias and safety violations. Traditional APM tools won't surface these issues.

KEY TAKEAWAY: Start measuring AI-specific metrics now: token consumption per request, model response quality scores, hallucination detection rates, and cost per inference. Implement full prompt/response logging for debugging non-deterministic failures. Consider adopting OpenLLMetry for standardized AI telemetry if you're running LLM workloads in production.

Why it matters: HolmesGPT automates the investigative work that normally requires senior SREs, connecting observability data to identify root causes during incidents.

HolmesGPT is an agentic troubleshooting tool designed specifically for cloud-native environments. When an alert fires, it queries your observability stack—Prometheus metrics, Kubernetes events, application logs—and uses LLMs to correlate symptoms, trace dependencies, and propose likely failure modes. The agent doesn't just surface data; it reasons about relationships between services, recent deployments, and resource constraints.

The tool integrates with incident management workflows, acting as a first responder that gathers context before humans join. For on-call engineers, this means arriving to incidents with preliminary analysis already complete: potential root causes ranked by likelihood, related recent changes surfaced, and remediation options suggested based on historical patterns.

KEY TAKEAWAY: HolmesGPT represents a shift from AI as a chatbot to AI as an active participant in incident response. It's most valuable for teams drowning in alert noise or struggling with complex microservices dependencies. The tool won't replace experienced SREs, but it can compress the time between alert and diagnosis by automating the initial investigative grunt work.