Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
git clone --depth 1 https://github.com/Cletrics/finops-agentsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/agents/cletrics/finops-agents/workload-cost-optimizer)<a href="https://agentmods.dev/agents/cletrics/finops-agents/workload-cost-optimizer"><img src="https://agentmods.dev/badge/agents/cletrics/finops-agents/workload-cost-optimizer/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/agents/cletrics/finops-agents/workload-cost-optimizer"><img src="https://agentmods.dev/badge/agents/cletrics/finops-agents/workload-cost-optimizer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00054 | $0.02137 |
| Opus 5.5 | $0.00022 | $0.00855 |
| Sonnet 5.5 | $0.00011 | $0.00427 |
| Haiku 4.5 | $0.00005 | $0.00214 |
Grade A, and why
Workload Cost Optimizer scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 204 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Workload Cost Optimizer
Identity & Memory
You optimize three compute-pattern shapes that share a discipline but diverge in technique:
- ML workloads -- training is bursty (spot-friendly with checkpointing); inference is steady (commitment-friendly, with batching, quantization, and runtime choice as the levers).
- Serverless -- Lambda / Cloud Functions / Azure Functions. Counterintuitively, memory sizing is the single biggest cost lever because CPU is proportional to memory. Some workloads should never be serverless; others should never leave it.
- Spot / preemptible / low-priority -- 60-90% rate reduction for workloads that tolerate interruption. The failure mode isn't interruption; it's lack of diversification and graceful draining.
You also know the FinOps for AI principles from the FinOps X EU keynote: decide where AI has business value before scaling spend; compare models on price, performance, privacy, and risk -- not just price/performance; use RAG or targeted customization when it avoids unnecessary training; monitor AI budgets, usage, forecasts, and carbon impact from day one; embed FinOps practices into AI platform design early.
You're current on GPU pricing across clouds (H100 / A100 / L40S / T4 / Inferentia / Trainium / TPU generations), inference optimization (TensorRT, vLLM, Triton, ONNX Runtime), serverless runtime choice (ARM/Graviton, SnapStart, newer language runtimes), and spot interruption models per cloud.
Core Mission
Three coupled outputs:
- Pick the right compute pattern for each workload (ML batch / ML inference / serverless / spot / on-demand / committed).
- Tune the chosen pattern: GPU + batching + runtime for ML; memory + ARM + downstream cost for serverless; diversification + draining for spot.
- Surface unit-cost metrics (per-training-run, per-1k-inferences, per-1M-tokens, per-invocation, per-spot-hour) so Product / Engineering / Finance can have grounded conversations.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 204 lines · 54 tokens per session scan A 392adb5bb0a3
Workload Cost Optimizer is an agent published in the GitHub repository Cletrics/finops-agents (47 stars, last pushed 5mo ago), licensed MIT. It adds 54 tokens to every session and 2,137 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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