ChatGPT caída spikes: check error rate, p95, queue, cost
A search spike is a symptom; use error rate, p95 latency, queue depth, and cost per 1k tokens to choose reroute, scale, degrade, or pay.
Serving
Sizing a local AI camera stack: model, RTSP, Docker, latency, cost
A five-part sizing model for local vision inference: camera source, model coverage, Docker serving, latency, and continuous power cost.
How to Price a Model Endpoint: GPT-6 Astra on OpenRouter
Treat a third-party endpoint as a routing policy, then check provider cost, throughput, fallback filters, routing mode, and failover before production.
Private AI Cloud Cost Test for Enterprise Inference
Run a GPU-hour and token-volume test to decide whether VMware private AI cloud inference beats public per-token pricing for enterprise products.
Score cloud APIs, self-hosted models, and inference exchanges
A serving topology is a data-placement decision: compare egress, p95 latency, residency, tenancy, and migration cost before choosing.
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Size inference GPUs by tokens per second per dollar, not by model size;
Pick the cheapest GPU that holds latency, memory headroom, and cost per token inside your real token-length and cache-hit profile.
Budget TTFT: prefill, batching, quantization, and routing for streaming LLM products
Treat TTFT as a prefill cost: set a prompt-token budget, then choose batching, quantization, and routing that buy first-token latency without wrecking throughput or spend.
Cut AI Outage Cost: SLOs, Fallbacks, Downtime Budgets
Treat every AI serving dependency as a costed failure surface: map it, set an SLO, define a fallback, cap downtime cost, and drill it.
Ship Reliable LLM Inference: A Production Architecture for Batching, KV-Cache Reuse, Autoscaling, Routing, and SLO Observability
Turn model capability into a cost-bounded service with batching, cache reuse, routing, autoscaling, rate limits, and SLO observability.