# Dust vs nOps

Dust records 547,217 monthly visits with +1.8% month-over-month growth and Contact for Pricing pricing. nOps records 97,959 monthly visits with +13.8% growth and Usage-based pricing.

| Metric | [Dust](https://openaitools.app/tool/dust) | [nOps](https://openaitools.app/tool/nops) |
| :--- | :--- | :--- |
| Website URL | https://dust.tt | https://www.nops.io |
| Categories | AI Assistant, Large Language Models (LLMs), AI Workflow, AI Knowledge Management | AI For Finance, AI Copilot, AI Productivity Tools |
| Pricing Model | Contact for Pricing | Usage-based |
| Monthly Visits | 547,217 | 97,959 |
| MoM Growth Rate | +1.8% | +13.8% |
| Primary Market | France | United States |
| Direct Visits | 456,919 | 32,028 |
| Review Score | 4.4 | 4.4 |
| Saved Count | 5 | 2 |
| Supported Platforms | Website | Website |

## Dust

Dust positions itself as 'multiplayer AI' for human-agent collaboration. Rather than giving each employee a private chatbot, it lets teams build agents that have shared access to company knowledge, connected tools, conversations and notifications, so work can move back and forth between people and AI. Agents can be reused across departments, and human review stays part of the loop where it matters.

The platform's differentiator is context. According to Dust, most AI tools can retrieve a single Slack message or CRM record, whereas its agents pull context together across Slack, CRM, documents and other company systems to answer with the full picture and then act on it. Over 70 connectors cover messaging, calendars, CRM, file storage, project management, data and finance tools, and agents can reach MCP servers through the same gateway.

Governance is a first-class feature. Dust offers a dual-layer permission model that separates what agents can access from who can use them, SCIM-synced groups, admin-gated overrides, audit logs and analytics for monitoring adoption, usage, cost and ROI. It is SOC 2 Type II certified, offers US or EU data residency and supports single-tenant deployment. Teams can build agents with 20+ frontier and open-source models from OpenAI, Anthropic, Google, Mistral and others, choosing the right model per workflow. The company says more than 3,000 organizations and 300,000+ agents run on the platform.

**Pricing & Plans:** Contact for pricing. Dust offers a free trial after signup, but its plans are oriented toward teams and enterprises with usage-based credits, volume pricing and dedicated support; you need to talk to sales for exact figures.

- **Agents with company context:** Connect 70+ tools including Slack, Notion, Google Drive, GitHub, Salesforce and Zendesk so agents answer with real company knowledge instead of generic guesses.
- **70+ connectors and MCP support:** Reach messaging, CRM, docs, project management, data, finance and MCP servers through one platform, so agents can act across systems rather than just chat.
- **Model flexibility:** Build agents with 20+ frontier and open-source models from OpenAI, Anthropic, Google and Mistral, and pick the best model for each task.
- **AI-native governance:** A dual-layer permission model, SCIM-synced groups, admin-gated overrides, audit logs and usage analytics keep company data locked down while teams move fast.
- **Multiplayer collaboration:** People and agents share context, tools and notifications, so cross-functional workflows run with reusable skills and human review built in.

**Strengths:** Deep context gathering across 70+ enterprise connectors; Strong enterprise governance with permissions, audit logs and SOC 2 Type II; Model-agnostic: choose among 20+ frontier and open-source models; Shared, reusable agents that scale across departments

**Limitations:** Pricing is sales-led, so costs are hard to estimate before a demo; Setting up connectors and permissions takes real admin work; Best value comes with broad rollout, which is a heavier commitment for small teams

[Profile (Markdown)](https://openaitools.app/tool/dust.md)

## nOps

nOps is built on the observation that cloud commitments are routinely mismanaged: most teams capture only a fraction of the discounts available to them, and the manual work of buying and rebalancing Reserved Instances and Savings Plans does not scale beyond a monthly cadence. nOps addresses this with an autonomous optimization engine that continuously adjusts commitment levels across AWS, Azure, and GCP to raise Effective Savings Rate while minimizing lock-in risk.

The platform's second pillar is visibility. nOps gives hourly insight into multicloud, Kubernetes, SaaS, and AI costs, with allocation, reporting, dashboards, forecasting, and anomaly detection. A dedicated AI cost view allocates spend to specific models, teams, and features across providers such as Bedrock, OpenAI, Claude, and Cursor, so organizations can see which AI workloads are driving spend and where token efficiency, model choice, or caching could cut it.

nOps reports managing more than $5B in annual cloud spend across 500+ customers, from startups to enterprises, and presents customer references from companies such as CommentSold, Kurtosys, Arlo, and BENlabs. It is available on AWS Marketplace and integrates with Slack, Jira, Datadog, and Snowflake. Pricing combines a fixed fee for cost visibility and allocation with a share-of-savings model for autonomous rate optimization, plus a 14-day free trial.

**Pricing & Plans:** Usage-based and results-based. Autonomous Rate Optimization is charged as a share of realized savings; Cost Visibility & Allocation is a fixed fee based on cloud spend. A 14-day free trial and a free 30-minute savings analysis are available. nOps is also purchasable through AWS Marketplace.

- **Autonomous commitment management:** An autonomous engine maps your usage patterns hourly, buys the optimal mix of Reserved Instances, Savings Plans, and committed use discounts, and rebalances continuously to keep discounts high without locking you into long commitments.
- **Cost visibility and allocation:** See multicloud, Kubernetes, SaaS, and AI spend at hourly granularity, with business context, forecasting, anomaly detection, and unit economics that show what actually drives cost.
- **AI cost intelligence:** Allocate hourly AI spend to the model, account, team, customer, and feature behind it, covering providers such as Amazon Bedrock, OpenAI, Claude, and Cursor, with recommendations for token efficiency, model choice, and caching.
- **Savings tracking and workflows:** Track Effective Savings Rate, commitment coverage, and realized savings in real time, and route alerts and tasks into tools like Slack, Jira, Datadog, and Snowflake.
- **FinOps AI agent:** A FinOps AI agent answers cost questions and performs routine tasks in natural language, reducing the manual spreadsheet work that slows optimization.

**Strengths:** Automated commitment purchasing and rebalancing removes recurring manual work; Savings-share pricing aligns platform cost with measured results; Hourly multicloud, Kubernetes, SaaS, and AI cost visibility in one platform; AI cost allocation by model, team, and feature for emerging AI workloads

**Limitations:** Pricing for the visibility layer is quote-based and scales with cloud spend; Value depends on having meaningful committed-spend volume to optimize; Savings-share model means you hand over a percentage of realized savings; Full value requires connecting cloud accounts and granting read access

[Profile (Markdown)](https://openaitools.app/tool/nops.md)

## Frequently Asked Questions

### How do Dust and nOps compare in monthly traffic?

Dust has 547,217 monthly visits, while nOps has 97,959. Traffic estimates describe audience size, not product quality.

### How do their growth rates compare?

Dust has a month-over-month growth rate of +1.8%; nOps has +13.8%.

### How does pricing compare?

Dust: Contact for Pricing. Contact for pricing. Dust offers a free trial after signup, but its plans are oriented toward teams and enterprises with usage-based credits, volume pricing and dedicated support; you need to talk to sales for exact figures. nOps: Usage-based. Usage-based and results-based. Autonomous Rate Optimization is charged as a share of realized savings; Cost Visibility & Allocation is a fixed fee based on cloud spend. A 14-day free trial and a free 30-minute savings analysis are available. nOps is also purchasable through AWS Marketplace.

## Alternatives

| # | Tool Name | Monthly Visits | MoM Growth Rate | Pricing Model | Summary | Profile (Markdown) |
| :--- | :--- | :--- | :--- | :--- | :--- | :--- |
| 1 | [Notion](https://openaitools.app/tool/t) | 173,441,670 | +0.1% | Freemium | Provides a unified connected workspace for team documents and databases, but lacks Dust's advanced multi-source enterprise knowledge indexing. | [.md](https://openaitools.app/tool/t.md) |
| 2 | [Salesforce Agentforce (formerly Einstein)](https://openaitools.app/tool/salesforce-einstein) | 111,910,743 | +2.2% | Freemium | Integrates deeply with CRM ecosystems to automate enterprise workflows, but is tightly bound to the Salesforce software suite. | [.md](https://openaitools.app/tool/salesforce-einstein.md) |
| 3 | [Glean](https://openaitools.app/tool/glean) | 3,301,875 | +0.1% | Contact for Pricing | Offers robust enterprise-wide permission-aware search and knowledge management, but with less focus on custom multi-model agent workflows. | [.md](https://openaitools.app/tool/glean.md) |
| 4 | [Abacus.AI](https://openaitools.app/tool/abacus-ai) | 3,278,636 | -1.7% | Contact for Pricing | Combines multi-model chat assistants and automated machine learning optimization, but with a heavier focus on data science pipelines. | [.md](https://openaitools.app/tool/abacus-ai.md) |
| 5 | [Taskade](https://openaitools.app/tool/taskade) | 539,714 | -9.4% | Freemium | Turn one prompt into live business apps, AI agents and automated workflows | [.md](https://openaitools.app/tool/taskade.md) |
| 6 | [Gumloop](https://openaitools.app/tool/gumloop) | 379,557 | -21.1% | Freemium, Paid, Contact for Pricing | Excels at visual no-code web scraping and data automation pipelines, but lacks Dust's dedicated internal knowledge base architecture. | [.md](https://openaitools.app/tool/gumloop.md) |
| 7 | [Tines](https://openaitools.app/tool/tines) | 273,260 | +24.4% | Freemium, Contact for Pricing | Focuses on secure enterprise workflow automation and security orchestration, but requires more manual setup for custom AI agents. | [.md](https://openaitools.app/tool/tines.md) |
| 8 | [Kore.ai](https://openaitools.app/tool/kore-ai) | 143,328 | -18.2% | Contact for Pricing | Delivers enterprise-grade conversational AI and workflow automation, but focuses more heavily on customer service and enterprise contact centers. | [.md](https://openaitools.app/tool/kore-ai.md) |
| 9 | [Sim AI](https://openaitools.app/tool/sim-ai) | 140,953 | +20.6% | Freemium, Paid (per user/month), and Enterprise with custom pricing; self-hosting free under Apache 2.0 | Open-source workspace to build, deploy and govern AI agents | [.md](https://openaitools.app/tool/sim-ai.md) |
| 10 | [Agno](https://openaitools.app/tool/agno) | 140,302 | -3.3% | Contact for Pricing, Free | Self-driving agent platform you run in your own cloud | [.md](https://openaitools.app/tool/agno.md) |

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Source: https://openaitools.app/compare/dust-vs-nops

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