Fiscal Observability

AI-driven cost estimates and waste prevention before you deploy. See the bill before you commit.

What it is

A "Check Engine Light" for your cloud bill. We predict costs based on your infrastructure code *before* deployment and track actual spend *after*, alerting you to anomalies.

When to use it

Preventing "Bill Shock" at the end of the month due to over-provisioned resources.
Right-sizing recommendations for underutilized instances.
Allocating costs strictly to specific teams or projects for showback/chargeback.

Use Cases

Real-world scenarios

The $15,000 Typo

The Problem

An engineer accidentally upgraded a test cluster to `x2.8xlarge` instances instead of `t3.large`, costing $15k in a single weekend.

The Solution

DeployN's Price Check ran on the PR. It flagged a +4,000% cost increase and blocked the deployment requiring CFO approval.

Result: Avoided a $15,000 mistake before a single packet was sent to the cloud.

Spot Instance Optimization

The Problem

Batch processing jobs were running on On-Demand instances needlessly, wasting budget.

The Solution

Fiscal AI analyzed the utilization patterns and suggested moving the EKS nodegroup to Spot Instances.

Result: Determined a potential 70% cost reduction for that workload with one config change.

Storage Waste Detection

The Problem

Teams kept creating snapshots and unattached EBS volumes that piled up unnoticed.

The Solution

The "Waste Report" identified $800/mo in unattached storage and obsolete snapshots.

Result: Cleaned up waste instantly, freeing up budget for new tools.

How It Works

Step 1:Price Sync

We pull live public pricing from AWS, Azure and GCP APIs daily.

Step 2:Resource Mapping

We correlate your Terraform resources (instance type, volume size) to pricing SKUs.

Step 3:Forecast

We calculate the estimated hourly and monthly burn rate for the proposed change.

Step 4:Anomaly Alert

After deployment, we watch CloudWatch/Billing metrics for unexpected spikes.

Measurable Outcomes

25%

Avg Savings

Typical reduction in monthly cloud spend by fixing waste.

100%

Budget Safety

Hard stops on budget limit breaches prevents overspending.

Granular

Visibility

See costs broken down by PR, Commit and User.

Technical Implementation

Spot Instances

We automatically recommend Spot Instances for stateless workloads like EKS nodes, saving up to 90%.

Cost Policy Config

HCL

policy "restrict_expensive_instances" {
  rule "instance_type_check" {
    condition     = contains(["t3.micro", "t3.small", "m5.large"], aws_instance.type)
    error_message = "Only t3/m5 types allowed in Dev environment"
  }
}

ARCHITECTURE FLOW

This diagram represents the logical flow of data within the Fiscal Observability module, demonstrating how it integrates with your existing stack.

Common Questions

Is this accurate?

Estimates are based on public list prices. We can accurately forecast trends, though your specific enterprise discounts may vary.

Does it support Reserved Instances?

Yes. If you input your RI coverage, we can adjust the estimates accordingly.

Can I set budgets per project?

Yes. You can define a "Max Monthly Burn" for the "Dev" workspace and block deploys that exceed it.

What regarding data privacy?

We only look at metadata (instance types, counts). We do not access customer data inside databases.

Does it work for serverless?

Yes. We estimate Lambda/Function costs based on configured memory and provisioned concurrency.

Ready to implement Fiscal Observability?

Join the waitlist to get early access and start building your visual infrastructure today.

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