Total Cost of Ownership: Griffin AI vs Mythos

Procurement teams ask for list price. Finance teams want a three-year TCO. Security leaders want to know whether the program will deliver the outcomes the budget assumes. These three questions produce three different answers about the same platform, and the gap between them is where most AI-for-security investments under-perform expectations. Griffin AI and Mythos-class general-purpose AI-for-security tools have meaningfully different TCO shapes, and understanding why matters before signing the contract.

What TCO actually includes

Five categories, all of which appear on the budget line one way or another:

• License or subscription. The visible number on the invoice.
• Token spend. When the platform uses frontier models, every meaningful operation has a token cost. This often appears on a separate cloud-provider bill.
• Integration engineering. The hours required to wire the platform into your CI, SIEM, ticketing, and identity systems.
• Operational overhead. The ongoing care-and-feeding — token rotation, eval re-baselining, incident response, vendor management.
• Switching cost. What it would take to leave the platform if it doesn't work out.

A three-year TCO model that captures all five is the right level of analysis for an enterprise procurement decision. List price alone is a poor proxy.

Where Griffin AI's architecture helps TCO

Three levers:

Token spend is gated, not constant. Griffin AI uses the deterministic engine for routine analysis and calls a frontier model only at specific high-leverage reasoning points. A typical scan of a medium-sized service costs cents in token spend, not dollars. Mythos-class pure-LLM tools call the model on every analysis step. At scale, the difference between "model call per finding" and "model call per high-leverage reasoning step" compounds materially.

Integration engineering is bounded. The structured findings API integrates with existing SIEM and ticketing on day one. Customers report 1–2 weeks of integration work to reach steady-state coverage. Mythos-class platforms more commonly require ongoing integration work because the LLM-mediated outputs require continuous parser maintenance.

Eval harness reduces operational drift. When the underlying frontier model bumps versions, Griffin's eval harness either passes the regression gate or doesn't. Customers don't experience model drift directly. Mythos-class platforms surface model variance to customers, which means ongoing investigation and recalibration.

A simple three-year model

For a 300-engineer organisation with 50 repos and one production environment, a typical Griffin AI deployment looks roughly like:

• Year 1: license + integration ($X), token spend ($Y), engineering hours (3 FTE-weeks).
• Year 2: license ($X), token spend ($Y), engineering hours (1 FTE-week, mostly maintenance).
• Year 3: license ($X), token spend ($Y), engineering hours (1 FTE-week).

The shape is front-loaded but stable.

A typical Mythos-class deployment at the same scale has a different shape:

• Year 1: license ($X'), token spend ($Y'), engineering hours (5–8 FTE-weeks because of integration variance).
• Year 2: license ($X'), token spend ($Y' growing), engineering hours (3–5 FTE-weeks because of model-drift handling).
• Year 3: license ($X'), token spend ($Y' growing further), engineering hours (3+ FTE-weeks).

The shape is uneven, with rising token spend and persistent engineering load.

The exact numbers depend on the deal, the workload, and the organisation. The shape — front-loaded vs trailing — is the structural difference.

Hidden costs that sink Mythos-class deployments

Three that consistently surface in customer postmortems:

• Token cost variability. Costs spike during incident response (more analysis, more model calls) precisely when budget visibility is lowest.
• Schema drift maintenance. The integration code that worked in Q1 needs adjustment in Q3 after a model upgrade. The team that built it has moved on.
• Eval gap incidents. A model upgrade changes behaviour in a way nobody noticed for three weeks. The post-incident review identifies a control that was supposed to be in place and wasn't.

Each of these is a recurring cost that doesn't appear on the invoice.

The switching cost question

When evaluating any AI-for-security platform, ask: what would it take to leave?

For Griffin AI, the structured findings, SBOM exports, and policy definitions are in standard formats (CycloneDX, SPDX, SARIF, OSV). A switch is meaningful work but is bounded by data export tooling that exists.

For Mythos-class platforms, the data is often in the vendor's proprietary shape, the integrations are bespoke, and the natural-language findings have no canonical export format. Leaving means rebuilding most of the program.

Switching cost is upstream of long-term pricing leverage. Vendors who lock you in price like vendors who can.

What procurement should evaluate

Four artefacts that surface TCO honestly:

1. A reference architecture diagram with token-spend annotations per workflow.
2. A worked TCO model the vendor is willing to defend over three years.
3. A switching plan: what would the vendor do if you decided to leave in year two?
4. A post-incident review template: how do you handle model drift in their platform?

If a vendor cannot produce these in a few weeks of evaluation, the TCO answer is "we don't know" — which is itself the answer.

How Safeguard Helps

Safeguard publishes a reference TCO model, documents token-spend behaviour per workflow, and supports data export in standard formats so the switching cost is bounded. The engine-plus-LLM architecture means token spend grows linearly with high-leverage reasoning, not with overall scan volume — which is the structural reason customers report stable year-over-year TCO. For organisations whose finance teams want defensible three-year cost projections rather than aspirational year-one pricing, Safeguard is built to be the platform the model can reflect honestly.