# Swept vs ModelOp

ModelOp is an AI governance platform for large enterprises. It inventories, approves and monitors the models and agents a company builds and buys, on infrastructure you host. Swept does that too, and it's where your people use AI every day, with approved tools and policyholder data checked on every request.

_Reviewed Nov 12, 2026 from 12 public sources._

## The short answer

- **ModelOp:** Choose it if you run a model risk program on your own infrastructure and want one inventory, with approvals and monitoring, for every model and agent.
- **Swept:** Choose it if you also need to see and control how staff use AI: approved tools, policyholder data checked on every request, spend caps by person and a log of every interaction, plus model testing and agent supervision.
- **Switching:** If you already run ModelOp. Swept can take over the program, or run beside it while you move.

## Where each one sits

A carrier's AI shows up in four places, and Swept covers all four: the AI your people use, agents at work, AI in vendor software, and the models you build, which Swept tests for bias and hallucinations. ModelOp covers the models and agents you build and the vendor AI on its inventory.

## Side by side

| Capability | Swept | ModelOp |
| --- | --- | --- |
| **Keep your data safe** | | |
| Staff use Claude, ChatGPT, Gemini and Copilot, approved and in their native form | Built in: Through a private gateway | Not offered: Governs AI; doesn't provide AI tools to staff [1] |
| Policyholder data stopped before it reaches a model | Built in: Checked on every request | Monitoring only: LLM monitors flag PII in model responses [2] |
| Alerts when vendor software ships new AI | Built in: Including features that arrive in an update | Partly: Inventories vendor and embedded AI; no new-feature alerts stated [1] |
| **Unshackle the full power of AI** | | |
| Agents that work with your core systems, without rip and replace | Built in: Built and run for you | Governs agents: Agent records and approvals; no workflow agents offered [1] |
| **Control costs and optimize ROI** | | |
| Caps and budgets by person and team, metered live | Built in | Cost tracking: Token and cost tracking by team or use case; caps not stated [1] |
| How you pay | On usage, not seats | Private offer; AWS Marketplace lists a non-production instance at $25,000 a month [6] |
| **Governance and supervision** | | |
| Governance mapped to NAIC and state requirements | Built in | Not listed: Controls name SR 11-7, the EU AI Act, NIST AI RMF and ISO 42001 [3] |
| Live inventory of models, agents and AI tools | Built in: Including vendor AI | Built in: ML, generative AI, agents, vendor tools and AI embedded in SaaS [1] |
| Models and agents tested for bias and hallucinations | Built in: On your own data, before and after release | Built in: Bias, drift and performance monitors, plus LLM checks [2] |
| Policy enforced at runtime, on every request and agent action | Built in: Plus drift alerts for agents | Through partners: Enforced through partner layers: Kong, Microsoft, AWS and NVIDIA [5] |
| Audit-ready log of every AI interaction | Built in: Plus board-ready reporting | AI life cycle: Approvals and evidence for each governed AI asset [1] |
| **Avoid vendor lock-in** | | |
| Switch AI providers without rebuilding your rules | Built in: One set of guardrails across providers | Through Kong: Kong's AI gateway applies ModelOp approvals across providers [4] |

## What is ModelOp?

ModelOp is an AI governance platform based in Chicago. Its Enterprise AI Command Center keeps one inventory of ML models, generative AI, agents, vendor tools and AI embedded in SaaS, with risk tiering, approval workflows and monitoring for bias, drift and performance. [1]

It runs on infrastructure you host and enforces policy at runtime through partners such as Kong. Its public materials don't describe a governed place for staff to use ChatGPT or Claude. [4]

| ModelOp at a glance | |
| --- | --- |
| Headquarters | Chicago [11] |
| CEO | Dave Trier, since February 2026 [8] |
| Funding | $10M Series B led by Baird Capital, August 2024 [9] |
| Recognition | A Visionary in the 2026 Gartner Magic Quadrant for AI Governance Platforms [7] |
| Deployment | Self-hosted: on-prem, private or public cloud [1] |
| Pricing | Private offer; non-production listed at $25,000 a month on AWS Marketplace [6] |
| Customers | Include Prudential Financial [10] |
| Reviews | 4.9 out of 5 from 6 reviews on G2 [12] |

## What each does well

### ModelOp

- One inventory for ML, generative AI, agents, vendor tools and AI embedded in SaaS, including AI nobody registered. [1]
- Risk tiering, approval workflows and scheduled monitors, with controls mapped to SR 11-7, the EU AI Act, NIST AI RMF and ISO 42001. [3]
- Runs on infrastructure you host, on-prem or in your own cloud. [1]

### Swept

- Approved access to the AI your people want, with policyholder data checked before it reaches any model.
- Models and agents tested for bias and hallucinations on your own data, before and after release.
- Policy enforced at runtime, on every request and every agent action.
- Spend caps by person and team, and a log of every AI interaction.

## Try one real task: an adjuster drafts a coverage letter from a claim file

**With ModelOp:** ModelOp isn't the tool the adjuster types into. If the drafting tool is registered in ModelOp, it gets a risk tier, an approval and scheduled monitors, and a partner gateway like Kong can block it until it's approved.

**With Swept:** The adjuster uses Claude or ChatGPT through Swept. Policy numbers and health details are caught before the request leaves your environment, and the draft is logged for review.

## ModelOp alternatives for insurance carriers

Carriers usually look past ModelOp for one of two reasons: they need to see and control how staff use generative AI, or they want a platform they don't have to host. Most alternatives cover one slice. Swept covers the whole job in one platform.

- **Swept.** Best for: Carriers that want to see and control how staff use AI, with approved tools, policyholder data checked on every request and a log of every interaction, plus model testing and agent supervision in the same platform.
- **[Monitaur](/compare/monitaur).** Best for: Model risk teams documenting and validating pricing, underwriting and claims models for examiners.
- **[Credo AI](/compare/credo-ai).** Best for: Governance teams that want a formal program record: an AI registry, risk reviews and policy packs mapped to regulations.
- **[Holistic AI](/compare/holistic-ai).** Best for: Teams that want AI discovery plus deep testing, red teaming and agent oversight.
- **[OneTrust AI Governance](/compare/onetrust).** Best for: Carriers that run privacy and third-party risk in OneTrust and want AI reviews in the same workflow.

## Questions carriers ask

### Is Swept a replacement for ModelOp?

For most carriers, yes. Swept keeps the AI inventory, tests models and agents for bias and hallucinations, and supervises agents. It also gives staff a governed place to use AI, with data and spend controls. If your model risk team already runs ModelOp, Swept can run beside it.

### What are the best ModelOp alternatives for insurance carriers?

It depends on the gap. Swept fits carriers that need staff on approved AI with policyholder data checked on every request, plus model testing and agent supervision. Monitaur fits model risk teams documenting insurance models for examiners, Credo AI fits teams building a formal governance record, and Holistic AI fits teams that want deep testing and red teaming.

### Can ModelOp monitor how employees use ChatGPT or Claude?

It can find unregistered AI such as custom GPTs, agents and MCP tools, and a partner gateway like Kong can block tools that haven't been approved. Its public materials don't describe approved staff access with data checks on each request. Swept puts staff on approved tools through its own gateway, checks policyholder data on every request and logs each interaction.

### Does ModelOp test models for bias and hallucinations?

It monitors bias, drift and performance, using the open source Aequitas library for bias, and its LLM monitors cover PII, fact checking and accuracy. Swept tests models and agents for bias and hallucinations on your own data, before and after release.

### Is ModelOp available as SaaS?

No. ModelOp is self-hosted, on-prem or in a private, public or hybrid cloud, and says it isn't a public SaaS. That suits teams that want to run it themselves. Swept runs in a private cloud set up for your carrier, so you don't host or maintain it.

### How much does ModelOp cost?

ModelOp doesn't publish production pricing and sells by private offer. Its AWS Marketplace listing shows a non-production instance at $25,000 a month. Swept charges on usage, so you pay for the AI your team actually uses. Ask us for a usage estimate based on your headcount.

### Is ModelOp a good fit for a regional carrier with a small IT team?

It's built for large enterprises and runs on infrastructure you host, so your team operates it. Swept runs in a private cloud set up for your carrier, so a small team isn't building or running the program.

### Which one helps more with the NAIC Model Bulletin?

ModelOp's controls library names SR 11-7, the EU AI Act, NIST AI RMF and ISO 42001, not the NAIC Model Bulletin. Swept covers both sides the bulletin asks about. It keeps the inventory and test results for your models and agents, and records how AI is used, by whom and with what data.

## Sources

Public documentation as of Nov 12, 2026. Where something isn't stated, we say so. ModelOp is a trademark of its owner.

1. [ModelOp Enterprise AI Command Center](https://www.modelop.com/enterprise-ai-command-center)
2. [ModelOp monitoring module](https://www.modelop.com/learning/modules/monitor)
3. [ModelOp controls](https://www.modelop.com/controls)
4. [ModelOp and Kong partner on zero trust security for agentic AI, July 2026](https://www.modelop.com/blog/modelop-kong-partnership-zero-trust-security-agentic-ai)
5. [ModelOp unveils full-stack AI policy enforcement with NVIDIA, November 2026](https://techintelpro.com/news/ai/enterprise-ai/modelop-unveils-full-stack-ai-policy-enforcement-with-nvidia)
6. [ModelOp on AWS Marketplace](https://aws.amazon.com/marketplace/pp/prodview-qsyul3zzy3jnc)
7. [ModelOp named a Visionary in the 2026 Gartner Magic Quadrant for AI Governance Platforms](https://www.modelop.com/blog/modelop-named-visionary-gartner-magic-quadrant-ai-governance-platforms)
8. [A leadership update at ModelOp, February 2026](https://www.modelop.com/blog/an-exciting-leadership-update-at-modelop)
9. [ModelOp raises $10 million, August 2024](https://www.modelop.com/blog/press-release-modelop-raises-10-million-to-accelerate-innovation-of-its-leading-ai-governance-software)
10. [ModelOp customer stories](https://www.modelop.com/customer-stories)
11. [ModelOp company](https://www.modelop.com/company)
12. [ModelOp reviews, G2](https://www.g2.com/products/modelop/reviews)

## See Swept next to what you run today

→ [Talk to us](/contact)