Comparison guides for teams choosing providers, routing layers, and managed AI platforms.
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.
AWS Bedrock and Google Vertex AI are not priced like a single-model API. Compare why platform, region, model, and throughput choices matter more than the headline rates most teams start with.
Read articleShould you route through Hugging Face or call model providers directly? Compare credits, pass-through billing, endpoint costs, operational trade-offs, and when each approach is actually cheaper.
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 comparison of Amazon Bedrock, Vertex AI, and Azure OpenAI for developers and product teams. What each platform does well, where costs show up, and when to choose which.
Read articleA practical decision guide for developers choosing between direct model APIs and an AI gateway. When a gateway helps, when it adds unnecessary complexity, and how to decide based on traffic, routing, and cost control.
Read articleClosed models lead on managed reliability and enterprise support. Open models win on control, flexibility, and unit economics. Here's how to choose in 2026 with real vendor examples and pricing snapshots.
Read articleOpen-weight models now rival the closed frontier on quality — but whether they're actually cheaper depends entirely on how you host and measure them. Here's how to track the true, blended cost of open and closed models in one view using Hugging Face and StackSpend.
Read articleA practical guide to choosing AWS, GCP, or Azure. Cost patterns, strengths by workload, and how to decide when you're starting fresh or already multi-cloud.
Read articleA practical map of the 2026 LLM tooling stack: when to use Bedrock, LiteLLM, Vertex AI, and OpenRouter, plus additional tools for routing, safety, observability, and cost control.
Read articleImprove RAG quality by treating retrieval as a funnel: lexical search, dense retrieval, reranking, and only then generation.
Read articleImprove retrieval quality by deciding when to rewrite the query, split it into parts, or route it to a different retrieval path before generation.
Read articleMany LLM question-answering systems should be grounded in SQL, tools, or explicit evidence rather than treated like generic RAG.
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 articleSummaries only work in production when they are shaped for a real decision. Choose operational, executive, or structured summaries on purpose.
Read articleProduction chat is a workflow system, not just a prompt. Design memory, retrieval, containment, and human handoff together.
Read articleScope multimodal LLM features more realistically by separating where vision and voice help from where classical OCR, ASR, or deterministic pipelines are enough.
Read articleUse LLMs to create labels, summaries, and semantic features offline, then let cheaper downstream models handle the hot path.
Read articleStack Spend now supports Hugging Face. Track Inference Endpoints, Spaces, and Jobs—bringing open-source LLM costs into the same dashboard as OpenAI, Anthropic, and Cursor. One view for closed and open models.
Read articleOn Hugging Face, the biggest cost surprise is usually a GPU-backed Inference Endpoint or Space left running after testing. How GPU cost accumulates and how to catch it.
Read articleA practical guide to deep research agents in 2026: what they do, how the workflow works under the hood, which vendors provide it, and where each option is strongest or weakest.
Read articleConnect providers in minutes. Get 90 days of visibility and start receiving daily cost updates before the invoice lands.