When my team first rolled out an internal assistant powered by GPT, adoption took off fast. Engineers used it for test cases, support agents for summaries, and product managers to draft specs. A number of weeks later, finance flagged the bill. What began as a couple of hundred dollars in pilot spend had ballooned into tens of hundreds. Nobody could say which teams or features drove the spike.
That have isn’t rare. Corporations experimenting with LLMs and managed AI services quickly realize these costs don’t behave like SaaS or traditional cloud. AI spend is usage-based and volatile. Every API call, every token, and each GPU hour adds up. Without visibility, bills scale faster than adoption.
Over time, I’ve seen 4 practical approaches for bringing AI spend under control. Each works best in numerous setups.
1. Unified Platforms for AI + Cloud Costs
These platforms provide a single view across each traditional cloud infrastructure and AI usage—ideal for firms already practicing FinOps and searching to incorporate LLMs of their workflows.
Finout leads on this category. It ingests billing data directly from OpenAI, Anthropic, AWS Bedrock, and Google Vertex AI, while also consolidating spend across EC2, Kubernetes, Snowflake, and other services. The platform maps token usage to groups, features, and even prompt templates—making it easier to allocate spend and implement policies.
Others like Vantage and Apptio Cloudability also offer unified dashboards, but often with less granularity for LLM-specific spend.
This works well when:
- Your org has an existing FinOps process (budgets, alerts, anomaly detection).
- You must track cost per conversation or model across cloud and LLM APIs.
- It’s worthwhile to explain AI spend in the identical language as infra spend.
Tradeoffs:
- Feels heavyweight for smaller orgs or early-stage experiments.
- Requires establishing integrations across multiple billing sources.
In case your organization already has cloud cost governance in place, starting with a full-stack FinOps platform like Finout makes AI spend management feel like an extension, not a brand new system.
2. Extending Cloud-Native Cost Tools
Cloud-native platforms like Ternary, nOps, and VMware Aria Cost already track costs from managed AI services like Bedrock or Vertex AI—since those show up directly in your cloud provider’s billing data.
This approach is pragmatic: you’re reusing existing cost review workflows inside AWS or GCP without adding a brand new tool.
This works well when:
- You’re all-in on one cloud provider.
- Most AI usage runs through Bedrock or Vertex AI.
Tradeoffs:
- No visibility into third-party LLM APIs (like OpenAI.com).
- Harder to attribute spend at a granular level (e.g., by prompt or team).
It’s an excellent start line for teams still centralizing AI around one cloud vendor.
3. Targeting GPU and Kubernetes Efficiency
In case your AI stack includes training or inference jobs running on GPUs, infra waste becomes a primary cost driver. Tools like CAST AI and Kubecost optimize GPU usage inside Kubernetes clusters—scaling nodes, eliminating idle pods, and automating provisioning.
This works well when:
- Your workloads are containerized and GPU-intensive.
- You care more about infrastructure efficiency than token usage.
Tradeoffs:
- Doesn’t monitor API-based spend (OpenAI, Claude, etc.).
- Focus is infra-first, not governance or attribution.
In case your largest cost center is GPUs, these tools can deliver fast wins—and might run alongside broader FinOps platforms like Finout.
4. AI-Specific Governance Layers
This category includes tools like WrangleAI and OpenCost plugins, which act as API-aware guardrails. They allow you to assign budgets per app or team, monitor API keys, and implement caps across providers like OpenAI and Claude.
Consider them as a control plane for token-based spend—useful for avoiding unknown keys, runaway prompts, or poorly scoped experiments.
This works well when:
- Multiple teams are experimenting with LLMs via APIs.
- You wish clear budget boundaries, fast.
Tradeoffs:
- Limited to API usage; doesn’t track cloud infra or GPU cost.
- Often must be paired with a broader FinOps platform.
Fast-moving teams often pair these tools with Finout or similar platforms for full-stack governance.
Final Thoughts
LLMs feel low cost in early stages—but at scale, every token and each GPU hour adds up. Managing AI cost isn’t nearly finance; it’s an engineering and product concern too.
Here’s how I give it some thought:
- Need full-stack visibility and policy? Finout is essentially the most comprehensive AI-native FinOps platform available today.
- Totally on AWS/GCP? Extend your native cost tools like Ternary or nOps.
- GPU-bound workloads? Optimize infra with CAST AI or Kubecost.
- Concerned about rogue API usage? Governance layers like WrangleAI offer fast containment.
Whatever path you select, start with visibility. It’s inconceivable to administer what you may’t measure—and with AI spend, the gap between usage and billing can get expensive fast.
In regards to the creator: Asaf Liveanu is the co-founder and CPO of Finout.
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