AICost.ai cost and governance engines
Where enterprises struggle most with AI cost and governance:
- Agents that loop
- Reasoning tokens
- Knowledge assistants (RAG)
- Voice agents
- Coding assistant seats
- Shadow AI
- Review, evals and guardrails
- Compliance evidence
124 AICost decision engines measure, optimize and govern each step. Here are the 30 enterprises use most.
AICost.ai Cost & Governance Engines
Measure the real cost
See what each step costs across the full AI workflow, not just the token line.
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Prices each agent task as turns and context grow
“Each task takes 4-8 LLM calls. What does that actually cost - and what happens when an agent loops forever?”
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Adds up planner, tools, memory and checker for an agent
“We're building 4 different agents. What's the full architecture cost across all 5 components?”
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Prices the hidden thinking tokens of reasoning models
“The headline price barely changed, so why did our spend jump 3x when we turned on reasoning?”
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Prices ingest, search, rerank and answer for RAG
“What does a 1M-doc RAG pipeline actually cost to run end-to-end?”
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Prices speech-to-text, model and speech per minute
“Voice agents have 5+ moving parts. What's the full stack cost per minute of conversation?”
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Prices wiring agents into CRM, ERP, ticketing and SSO
“The model is the easy part. What does it cost to connect the agent to our CRM, ERP, ticketing and SSO - and keep those connections working?”
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Measures failed and repeated calls
“Traffic did not change, so why did our token bill jump 30% the week the provider was flaky?”
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Prices traces and spans for agent workloads
“Agents generate a lot of spans. Priced per span, tracing can rival the model bill. Per-trace or self-host - what is cheapest at our volume?”
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Splits seats, API and agent spend across vendors
“We pay a dozen AI vendors across seats, API keys and agent platforms. Where is the money actually going, and what is leaking?”
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Counts real tokens per request from your prompt
“We've estimated 1,500 input tokens per request. Is that right? My monthly bill says we're using 4,800.”
Find the best cost levers
Price every lever before you pull it, inside your quality floor.
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Finds repeat prompts worth caching, by vendor
“Anthropic offers prompt caching. My RAG bot has long system prompts. Worth setting up?”
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Sends each step to the cheapest capable model
“Half our queries are simple - why pay Sonnet rates for them? How do I build smart routing?”
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Finds work that can wait for batch discounts
“AWS sales said batch saves 50%. Sounds great - but how much of my AI workload can actually run in batch mode?”
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Trims long agent context and prices what you keep
“Every compaction vendor quotes 60%. What do I actually keep after paying to run it and after the agent re-asks for what got paraphrased away?”
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Stacks caching, routing and batching correctly
“Every vendor quotes a percentage. If I do routing AND caching AND batching, do I really save 90%?”
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Runs one workload across model families
“We are on Anthropic. Finance wants to know if Gemini or OpenAI would be cheaper. Every comparison I find is a rate card.”
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Finds where open models on your GPUs pay off
“We spend $40K/mo on Anthropic. Should we self-host an open-source model on our own GPUs?”
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Checks coding-assistant seats against output
“Finance flagged our AI tool line after the budget blowups in the news. Renewal is next month - is $X/seat actually worth it, and for which tier?”
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Models the bill at 10x and 100x usage
“We're at 5K AI requests/day. Board wants to 100× to 500K/day. What does that bill look like - and when does it break?”
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Rolls up all cost layers into a go or no-go
“Tokens are a fraction of the real cost. When I add eval, guardrails, humans-in-the-loop, integration and upkeep, does this agent still clear ROI?”
Turn policy into controls
Keep every saving inside your rules, with budgets, gates and evidence.
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Sets per-run ceilings, daily caps and a kill switch
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Prices injection, PII and safety screening
“Every request needs injection and PII screening. What does that safety layer add as a percent over our token bill?”
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Prices human approval on risky agent actions
“We have humans reviewing a share of agent actions. Does the cost of that review pay for itself in incidents avoided?”
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Prices scoring a golden set on every release
“We ship prompt and model changes constantly. What does it cost to score a golden set every release so we catch regressions early?”
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Estimates yearly cost by EU AI Act risk tier
“The EU AI Act puts our use case in a risk tier. What does staying compliant actually cost us per year?”
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Scores data class against where a model comes from
“The open-weight Chinese models would cut our inference bill by 80%. Can we actually use them on patient data, and if not, is there a version we can?”
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Measures exposure to one AI vendor
“We're 85% on Anthropic. Board asked: what if Anthropic raises prices 50% or has a 6-week outage?”
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Re-forecasts the year as agent use compounds
“We're six months in and running hot. Do I quietly hold a reserve, or take a revised number to the CFO now - and how big?”
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Projects month-end spend from month-to-date
“We're 11 days into the month and at 47% of monthly budget. Are we going over?”
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Prices re-testing when vendors retire models
“Vendors deprecate models and shift behavior under us. What does it cost per year to keep re-baselining prompts and migrating?”
What you gain
- Lower AI coststep-level savings
- Fewer surprise billsrunaway prevention
- Safer go-livebudgets and approvals
- Better ROI decisionsforecast before scaling
- Controlled scaleinside your governance envelope
See all 124 AICost engines
The most complete cost and governance coverage for every AI workflow you run: agents, RAG, voice, coding, media and more.
The same engines work in every AI workflow you run: lower cost, with governance built in.