Redefining the Economics of Analytics

Historically, analytics solutions - such as Intrusion Detection Systems (IDS), AI-based security analytics, anomaly detection tools, or network and application performance monitoring analytics (NPM and APM) - have typically been deployed as hardware-based appliances.

Why this Approach is Broken

NetOps, DevOps and SecOps teams are not free to choose their preferred solutions. Purchase and deployment of hardware is commonly managed by the IT Operations team, which strongly influences what is chosen because their role is to deploy and manage the solutions. They frequently have power of veto, and their priority is manageability rather than functionality.

Hardware-based analytics appliances are costly to evaluate, test, deploy and turn on, especially in large global networks. Because they are costly, fewer appliances are purchased than is required, leading to blind spots in network coverage.

The speed of purchase and deployment is glacial. From CAPEX budget approval to final deployment takes months for large, multinational organizations. Moving or replacing appliances to meet changing needs is costly, time-consuming, and slow. And because functionality is tied to a physical appliance, deploying new analytics capability often means a rip-and-replace.

Five-year depreciation cycles are common. Which means the lifetime of appliances is expected to be at least five years too. This fast-changing cybersecurity landscape, that can leave organizations tied to solutions that are well past their effective use-by-date.

The CAPEX costs of purchasing multiple appliances from multiple vendors, and the OPEX costs of managing them, is taxing already strained budgets - leaving little to nothing in the pot for purchasing new functionality as needs change.

How a Platform Approach Redefines the Economics

Deploying a common analytics platform, such as our EndaceProbes, requires just one hardware CAPEX and deployment cycle. Once the platform has been deployed, and the northbound integration is done, new analytics tools can be deployed virtually, in software, and pushed out to the platform where the network traffic to be analyzed resides.

Deploying analytics functions in software on EndaceProbes is significantly cheaper and faster than deploying hardware appliances, since you are not paying again for the packet capture capability. The cost savings are significant. Additionally, the operational cost of supporting multiple appliances, from multiple vendors, is dramatically reduced.

A Cost Comparison

The table below compares the cost and timeframe of deploying a new analytics function as a dedicated appliance versus deploying that function in software hosted on an analytics platform that is already deployed in the network. It assumes a deployment of 150 appliances and is based on a real-world model that we built for a customer.

Appliance Approach Platform Approach

Appliance Approach

Platform Approach






Budget allocation

6-12 months


1-3 months


RFP preparation

3 months


1 month


RFP process and “bake-off”

3 months


1 month


Purchasing and delivery

3 months

$150k / appliance

< 1 month


Deployment and configuration

6 months

$50k / appliance

< 1 week


Northbound integration

3 months




Workflow integration

1 month


1 month



2 years



As you can see, the incremental cost of deploying new analytics functions as virtual software applications hosted on the EndaceProbe Analytics Platform is less than 25% of the cost of deploying analytics appliances. Which means you can deploy four times the analytics functionality, or extend visibility further across the network to remove blind spots.

Not only is it far less expensive to deploy analytics virtually, it's also far faster, which enables far greater agility in deploying analytics functions. To find out more, read our Redefining Analytics Deployment article.

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