Teams usually weigh Outlay against four other things: a cloud FinOps suite, an LLM observability tool, the native provider console, or building it themselves. Here's the honest version of each.
| FinOps suite | LLM observability | Native console | Outlay | |
|---|---|---|---|---|
| Spend mapped to tickets / epics / roadmap | ✕cloud resources, not eng work | ✕traces & tokens, not scope | ✕per-key, per-model totals | ✓attributed to the work you plan |
| Forecast by open scope + budget guardrails | ✓for cloud, not AI-by-scope | ✕ | ✕ | ✓quarter forecast + pace alerts |
| Prompts never leave your environment | ✓doesn't see prompts at all | ✕ingests prompts/outputs to trace | ✓ | ✓metadata-only, classified locally |
| Cuts spend (routes down) with proof | ✕reports, doesn't act | ✕observes, doesn't act | ✕ | ✓shadow → non-inferiority → autopilot |
| Pricing aligned to your bill going down | ✕seat / % of cloud spend | ✕seat / event volume | ✕ | ✓share of realized savings |
Generalizations across categories — individual products vary. The point isn't that these tools are bad; it's that they answer a different question than "what is each epic costing us in AI, and what do we do about it?"
Excellent at cloud cost — compute, storage, Kubernetes — allocated to teams and cost centers. But AI coding-agent spend lives in SaaS invoices and provider APIs, not your cloud bill, and these suites allocate by cloud resource, not by ticket or epic. They'll tell you the line item is growing; they won't tell you which sprint drove it or which model to use instead. Outlay is the AI-native complement: same FinOps discipline, applied to LLM spend by scope of work.
Great for debugging quality, latency, and traces — and several show cost per call. The structural trade-off: to trace, they ingest your prompts and outputs, so your content sits in their data path. They also stop at observation — they surface cost, they don't forecast by roadmap scope or route spend down. Outlay classifies locally (only a category + token counts + ticket id ever reach us) and is built to act on the number, not just chart it.
Free, accurate, and already there — totals by API key, model, and workspace. The gap is the join: a key or workspace isn't an epic, and there's no forecast, no pace guardrail, and no cross-tool view (Claude Code + Cursor + direct API in one place). Outlay reads these consoles read-only and does the attribution, forecasting, and routing on top.
Entirely doable — a script that pulls the Anthropic admin API, joins PRs to tickets, and charts it. Most teams who try it find the join is the hard part (detached-HEAD CI agents, tracker teams, branches not named after tickets), and that a one-off dashboard rots. Outlay ships the attribution join with fidelity tiers, the cache-aware cost model, the forecast, and the proven routing as a product — read-only, metadata-only, free during the pilot. If it's not worth more than a weekend to you, build it; if AI is a real and growing line item, don't maintain it yourself.
Different category: gateways are a hosted layer your traffic flows through to reach many models, and routers pick a model by reading your prompt. Outlay's optional optimization engine sits on your own Claude key, decides from a local classification (so the prompt never reaches us), bills only a share of realized savings, and fails open to your direct Anthropic account. If you need one API across many providers or a multi-model ensemble for maximum quality, a gateway is the right tool — some teams run both: a gateway for breadth, Outlay to stop overspending on the easy requests and to map it all to the roadmap.
Two weeks, read-only, prompts never leave your environment. We'll map your real AI spend to your roadmap and show you where it's about to go over.