# Dust vs PredictOPs

Dust records 547,217 monthly visits with +1.8% month-over-month growth and Contact for Pricing pricing. PredictOPs records 0 monthly visits with 0% growth and Free Trial, Contact for Pricing pricing.

| Metric | [Dust](https://openaitools.app/tool/dust) | [PredictOPs](https://openaitools.app/tool/predictops) |
| :--- | :--- | :--- |
| Website URL | https://dust.tt | https://predictops.com |
| Categories | AI Assistant, Large Language Models (LLMs), AI Workflow, AI Knowledge Management | AI Monitor, Log Management, Large Language Models (LLMs), AI For Data Analytics |
| Pricing Model | Contact for Pricing | Free Trial, Contact for Pricing |
| Monthly Visits | 547,217 | 0 |
| MoM Growth Rate | +1.8% | 0% |
| Primary Market | France | Global |
| Direct Visits | 456,919 | 0 |
| Review Score | 4.4 | 4.1 |
| Saved Count | 5 | 1 |
| 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)

## PredictOPs

PredictOPs is a Gen-AI AIOps platform aimed at IT operations, infrastructure and service-management teams that need to move from reactive firefighting to anticipatory operations. It ingests the log events, network data and cloud-service signals an organisation already produces, then applies generative-AI reasoning and patent-pending machine-learning algorithms to surface what is likely to go wrong, not just what already has. Core pillars span log data monitoring with real-time visibility, alert correlation that cuts noise and pinpoints root cause, end-to-end microservice management with auto-scaling optimisation, and adaptive anomaly detection that tracks changing system behaviour.

The platform is designed to integrate rather than replace: it works with existing data sources and can be customised to an organisation's requirements, while automated workflows can trigger remediation with or without human involvement. Failure-rate analysis and infra log behaviour analysis add proactive trend detection, so teams can schedule preventive maintenance before failures occur. Deployment is pitched as scalable for both startups and large enterprises, with a PredictOPs Assurance promise around reliability.

Commercials are gated behind a sign-up and demo flow: prospective users create an account, request a free trial, and receive a guided demonstration covering log monitoring, alert correlation, microservices management, anomaly detection and failure-rate analysis. No public price list is shown, so costs are quoted on request.

**Pricing & Plans:** Free trial with pricing available on request — you sign up, request a trial and get access via a demo; no public price list is published.

- **Log data monitoring:** Real-time visibility into system log data with predictive analytics and actionable insights that flag issues before they affect operations.
- **Alert correlation:** Intelligently correlates alerts to reduce noise, clarify incidents and expose the root cause behind cascading failures.
- **Microservice management:** End-to-end visibility across a microservices architecture, with auto-scaling optimisation tuned to real-time demand.
- **Adaptive anomaly detection:** Early-warning anomaly detection using algorithms that adapt to shifting system behaviour in dynamic environments.
- **Failure rate analysis:** Proactive failure-rate trend analysis that helps teams schedule preventive maintenance before outages happen.

**Strengths:** Combines Gen-AI reasoning with ML algorithms rather than rule-only alerting; Works on top of existing log, network and cloud data sources; Automated workflows can trigger remediation with or without human approval; Scalable design positioned for both startups and large enterprises

**Limitations:** No public price list — costs require contacting sales; Feature depth and integrations are only visible after a demo request; Patent-pending claims and assurance language are hard to validate without a trial

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

## Frequently Asked Questions

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

Dust has 547,217 monthly visits, while PredictOPs has 0. 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%; PredictOPs has 0%.

### 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. PredictOPs: Free Trial, Contact for Pricing. Free trial with pricing available on request — you sign up, request a trial and get access via a demo; no public price list is published.

## 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 | [Datadog](https://openaitools.app/tool/datadog) | 5,175,760 | -4.8% | Freemium, Free Trial, Paid, Contact for Pricing, Free | Unified observability, security and incident response for cloud-scale engineering teams | [.md](https://openaitools.app/tool/datadog.md) |
| 4 | [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) |
| 5 | [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) |
| 6 | [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) |
| 7 | [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) |
| 8 | [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) |
| 9 | [Arize AI](https://openaitools.app/tool/arize-com) | 249,028 | +3.2% | Paid, Contact for Pricing | Agent observability, evaluation, and continuous improvement for production AI | [.md](https://openaitools.app/tool/arize-com.md) |
| 10 | [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) |

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

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