How to Keep AI Costs Under Control

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 few weeks later, finance flagged the bill. What began as a few hundred dollars in pilot spend had ballooned into tens of thousands. No one could say which teams or features drove the spike.

That experience isn’t rare. Companies 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 every GPU hour adds up. Without visibility, bills scale faster than adoption.

Over time, I’ve seen four practical approaches for bringing AI spend under control. Each works best in different setups.

1. Unified Platforms for AI + Cloud Costs

These platforms provide a single view across both traditional cloud infrastructure and AI usage—ideal for companies already practicing FinOps and looking to include LLMs in their workflows.

Finout leads in 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 teams, features, and even prompt templates—making it easier to allocate spend and enforce 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 want to track cost per conversation or model across cloud and LLM APIs.
• You need to explain AI spend in the same language as infra spend.

Tradeoffs:

• Feels heavyweight for smaller orgs or early-stage experiments.
• Requires setting up integrations across multiple billing sources.

If 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 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 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 a good starting point for teams still centralizing AI around one cloud vendor.

3. Targeting GPU and Kubernetes Efficiency

If 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.

If your largest cost center is GPUs, these tools can deliver fast wins—and can 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 let you assign budgets per app or team, monitor API keys, and enforce caps across providers like OpenAI and Claude.

Think of 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 need clear budget boundaries, fast.

Tradeoffs:

• Limited to API usage; doesn’t track cloud infra or GPU cost.
• Often needs to 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 cheap in early stages—but at scale, every token and every GPU hour adds up. Managing AI cost isn’t just about finance; it’s an engineering and product concern too.

Here’s how I think about it:

• Need full-stack visibility and policy? Finout is the most comprehensive AI-native FinOps platform available today.
• Mostly 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 choose, start with visibility. It’s impossible to manage what you can’t measure—and with AI spend, the gap between usage and billing can get expensive fast.

About the author: Asaf Liveanu is the co-founder and CPO of Finout.

Disclaimer: The owner of Towards Data Science, Insight Partners, also invests in Finout. As a result, Finout receives preference as a contributor.