Runbooks and reference material for reducing AI spend, investigating spikes, and making model changes safely.
Topic overview
Start with the most useful StackSpend guides for this problem area, then move into the product workflow that fits what you are trying to do.
A practical guide to AI cost anomaly detection for teams using OpenAI, Anthropic, Bedrock, Vertex AI, and Azure OpenAI. Learn which signals matter, how to set thresholds, and how to investigate anomalies without noise.
Read articleA practical guide to AI cost observability for teams using OpenAI, Anthropic, Bedrock, Vertex AI, and Azure OpenAI. Learn what to measure, how to structure ownership, and how to turn raw usage data into useful cost decisions.
Read articleLLMOps and LLM FinOps overlap, but they are not the same job. Learn where tracing, prompts, evaluation, spend tracking, and cost controls fit in a modern AI operations stack.
Read articleA practical guide to where StackSpend, PostHog, Langfuse, Helicone, and Lunary fit across LLM FinOps, LLM observability, analytics, and multi-provider AI cost control.
Read articleA practical guide for product teams that need LLM spend tracking by feature, experiment, team, and customer. Learn what to instrument, what to review weekly, and how to connect model decisions to spend.
Read articleA step-by-step runbook for developers and product teams investigating a sudden OpenAI, Anthropic, Bedrock, Vertex AI, or Azure OpenAI spend increase.
Read articleA practical guide to making AI costs explainable. How developers and product teams should structure projects, workspaces, API keys, tags, and metadata to track spend by feature, team, and customer.
Read articleA practical guide for developers, product teams, and engineering leaders who need to track LLM API spend by provider, model, feature, team, and customer before the invoice arrives.
Read articleLower-cost models now handle most production AI tasks reliably. But switching without a process is how products break. Here is a task taxonomy, current 2026 pricing context, and a five-step evaluation framework.
Read articleAI and LLM spend can spike without warning. Learn the five most common causes of AI cost overruns and the monitoring approach that prevents them.
Read articleWhen your LLM spend spans OpenAI, Anthropic, and Cursor, visibility fragments. Learn how to consolidate LLM cost tracking across providers and avoid budget surprises.
Read articleYour OpenAI bill isn't high because OpenAI is expensive. It's high because you're paying for usage you didn't see coming—and you're finding out a month too late. Here's what usually causes it and how to fix it.
Read articleA practical OpenAI bill checklist for prompt size, retries, background jobs, embeddings, and model tier changes when spend jumps unexpectedly.
Read articleLearn how to monitor AI infrastructure in production with daily spend signals, alert thresholds, cross-provider visibility, and simple review loops.
Read articleA fast health check for AI cost tracking, monitoring, attribution, and optimization hygiene. Identify the first five fixes without a long read.
Read articlePrioritize the engineering tactics that lower AI spend fastest. Prompt compression, caching, smaller models, batching, and retrieval optimization with a clear savings vs effort ranking.
Read articleChoose the right architecture for knowledge and behavior. RAG, fine-tuning, and full-context each win in different scenarios—and hybrids are now the default.
Read articleChoose when to retrieve vs stuff more context. Embeddings and retrieval have different cost shapes than long-context prompting—this guide shows which wins for your workload.
Read articleUse schema-constrained outputs to make extraction, classification, and scoring workflows more reliable than free-form prompting.
Read articleUse LLMs for yes-no and limited-choice decisions only when the decision space, fallback path, and review thresholds are explicit.
Read articleSummaries only work in production when they are shaped for a real decision. Choose operational, executive, or structured summaries on purpose.
Read articleBuild tool-using LLM systems with the lightest orchestration that works: fixed workflows first, planner and executor loops only when the task truly requires them.
Read articleHuman review is not a fallback for bad AI design. Use it deliberately to control risk, protect quality, and keep automation economically sensible.
Read articleBuild a practical evaluation loop for LLM systems using task-specific metrics, regression sets, and release gates instead of ad hoc spot checks.
Read articleProduction chat is a workflow system, not just a prompt. Design memory, retrieval, containment, and human handoff together.
Read articleKeep LLM workflows inside acceptable risk boundaries with layered policy checks, review routes, and confidence-based gating.
Read articleThe highest-leverage AI architecture choice is often not using an LLM at all. Use this guide to reject bad LLM candidates early.
Read articleAI unit economics only matter when AI cost is a direct input to revenue. Internal tooling? Skip the complexity. Charge for AI? You need it. Here's when to bother, what to measure, and how to start.
Read articleConnect providers in minutes. Get 90 days of visibility and start receiving daily cost updates before the invoice lands.