# LLMWare vs Patched

LLMWare records 5,178 monthly visits with +8.4% month-over-month growth and Contact for Pricing pricing. Patched records 3,111 monthly visits with +30% growth and Paid, Contact for Pricing pricing.

| Metric | [LLMWare](https://openaitools.app/tool/llmware) | [Patched](https://openaitools.app/tool/patched) |
| :--- | :--- | :--- |
| Website URL | https://llmware.ai | https://patched.codes |
| Categories | AI Developer Tools, Large Language Models (LLMs), AI Models, AI Workflow | AI Workflow, AI Developer Tools, AI Agent, Large Language Models (LLMs) |
| Pricing Model | Contact for Pricing | Paid, Contact for Pricing |
| Monthly Visits | 5,178 | 3,111 |
| MoM Growth Rate | +8.4% | +30% |
| Primary Market | United States | United States |
| Direct Visits | 2,011 | 1,226 |
| Review Score | 4.4 | 4.1 |
| Saved Count | 0 | 1 |
| Supported Platforms | Website | Website |

## LLMWare

LLMWare is aimed at organizations that cannot send sensitive data to public model providers. Instead of an API you call over the internet, it gives you a private AI workflow engine — Model HQ — that runs on your own hardware. The pitch is straightforward: keep financial, legal, and regulatory data inside your security perimeter while still automating the document-heavy work that eats up staff time.

Model HQ is a no-code workflow builder. You download a client agent of roughly 200 MB, pull from a library of 250+ optimized small language models, and chain models, data, and logic into multi-step automations. Typical workflows include extracting and summarizing thousands of documents, classifying and routing support tickets, turning raw datasets into reports, pulling structured data out of images, and running jobs on a schedule. Models up to 32 billion parameters can run on modern AI PCs.

Deployment is flexible across three environments: AI PCs for individuals and small teams, data centers for batch inference and RAG over sensitive data, and private cloud for larger or time-sensitive operations. These can be linked into hybrid setups. Because inference happens locally, there are no per-token charges, and the company also ships an open RAG framework and models on Hugging Face for developers. Pricing is enterprise-oriented and quoted on request.

**Pricing & Plans:** Contact for pricing. Starter access to Model HQ is available by downloading the client agent, and the platform is designed to remove per-token inference costs by running models locally. Enterprise deployments across AI PCs, data centers, and private cloud are quoted directly by LLMWare (Ai Bloks LLC).

- **No-code AI workflows:** Model HQ lets you chain models, data sources, and logic into repeatable multi-step workflows without coding, tuning, or managing infrastructure.
- **Private document processing:** Point the platform at your documents to extract, summarize, and analyze thousands of files automatically, with integrated on-device RAG.
- **250+ local models:** Run up to 32B-parameter models locally, optimized across CPUs, GPUs, and NPUs, with no data leaving your environment.
- **Business and vision automation:** Classify, route, and summarize tickets, analyze datasets and CSV files, extract structured data from images, and trigger actions.
- **Scheduled, zero-cost inference:** Schedule workflows to run automatically, deploy them to teams and devices, and avoid per-token inference charges.

**Strengths:** Keeps sensitive data inside your own security perimeter; No per-token inference costs when running locally; 250+ models and workflows available without coding; Flexible deployment across AI PCs, data centers, and private cloud

**Limitations:** Requires local infrastructure or hardware (AI PC, data center, or private cloud); Enterprise-focused, with pricing on request rather than self-serve; Initial setup and model downloads take time; Best value assumes you have AI-capable hardware

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

## Patched

Patched positions itself differently from workflow orchestration tools: the pitch is execution, not orchestration. Teams describe a process, and Patched is meant to run it end to end, handling judgment-heavy and ambiguous cases that normally stall in a queue. The vendor reports throughput figures such as hundreds of thousands of workflows executed monthly, sub-hour average turnaround and resolution rates above 80 to 90 percent across published use cases.

The platform is aimed squarely at compliance-sensitive environments. It advertises compliant-by-design deployment on dedicated infrastructure, full auditability, SLA ownership and SOC 2 compliance, with named use cases in fraud and risk, banking customer operations and reconciliation. Because these teams cannot simply ship data to a shared public endpoint, the emphasis on isolated infrastructure and evidence trails is central to the offer.

Commercial terms are engagement-led: pricing scales with operational volume and prospects start by bringing their hardest workflow to a scoping call. The site is run by Patched Codes, Inc. and presents logos of publicly listed enterprises as references. Onboarding is quoted in weeks, with the vendor claiming operational deployment in roughly two to five weeks depending on the workflow.

**Pricing & Plans:** Pricing scales with operational volume and is quoted per engagement; the company asks prospects to book a call and start with their hardest workflow. No self-serve tier or public price list is shown on the homepage, and the pricing page is not publicly accessible to crawlers.

- **End-to-end workflow execution:** Patched takes a described process and runs it to completion, rather than producing outputs for a human to assemble and finish.
- **Auditability and compliance controls:** Systems are deployed on dedicated infrastructure with auditing built in, targeting regulated industries where evidence and traceability are mandatory.
- **SLA ownership:** The vendor takes responsibility for outcomes and resolution metrics, not just for keeping a system running.
- **Fraud and risk resolution:** Purpose-built for manual investigation backlogs, high false-positive rates and deadline pressure, with published case metrics from customer deployments.
- **Reconciliation and banking operations:** Handles cross-system referencing and zero-error-margin reconciliation work as well as high-volume banking customer requests.

**Strengths:** Focuses on completing regulated work end to end rather than selling another orchestration canvas; Compliance-first design with dedicated infrastructure and full auditability; Vendor accepts SLA ownership for outcomes, not just uptime; Published throughput and resolution metrics for specific operational use cases

**Limitations:** Pricing is not published and requires a sales conversation, which slows evaluation; No self-serve signup or free tier for hands-on testing; Deployments are quoted in weeks, so it is not an instant-solution tool

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

## Frequently Asked Questions

### How do LLMWare and Patched compare in monthly traffic?

LLMWare has 5,178 monthly visits, while Patched has 3,111. Traffic estimates describe audience size, not product quality.

### How do their growth rates compare?

LLMWare has a month-over-month growth rate of +8.4%; Patched has +30%.

### How does pricing compare?

LLMWare: Contact for Pricing. Contact for pricing. Starter access to Model HQ is available by downloading the client agent, and the platform is designed to remove per-token inference costs by running models locally. Enterprise deployments across AI PCs, data centers, and private cloud are quoted directly by LLMWare (Ai Bloks LLC). Patched: Paid, Contact for Pricing. Pricing scales with operational volume and is quoted per engagement; the company asks prospects to book a call and start with their hardest workflow. No self-serve tier or public price list is shown on the homepage, and the pricing page is not publicly accessible to crawlers.

## Alternatives

| # | Tool Name | Monthly Visits | MoM Growth Rate | Pricing Model | Summary | Profile (Markdown) |
| :--- | :--- | :--- | :--- | :--- | :--- | :--- |
| 1 | [Zapier](https://openaitools.app/tool/zapier-com) | 5,065,248 | +0.4% | Freemium, Free Trial, Paid | Automate work across thousands of apps — no code required | [.md](https://openaitools.app/tool/zapier-com.md) |
| 2 | [n8n](https://openaitools.app/tool/n8n) | 4,522,449 | -7.4% | Freemium | Open-source workflow automation built for AI agents and technical teams | [.md](https://openaitools.app/tool/n8n.md) |
| 3 | [Harvey](https://openaitools.app/tool/harvey-ai) | 1,832,998 | +26.1% | Contact for Pricing | Domain-specific AI for law firms, in-house legal teams, and professional services | [.md](https://openaitools.app/tool/harvey-ai.md) |
| 4 | [Invisible Technologies](https://openaitools.app/tool/invisible-technologies-inc) | 1,364,520 | +64.4% | Contact for Pricing | Enterprise AI infrastructure where software and expert humans work together | [.md](https://openaitools.app/tool/invisible-technologies-inc.md) |
| 5 | [KaneAI](https://openaitools.app/tool/kaneai) | 901,340 | +13.2% | Freemium, Paid, Contact for Pricing | GenAI-native end-to-end QA agent that plans, writes and heals tests in plain language | [.md](https://openaitools.app/tool/kaneai.md) |
| 6 | [Open WebUI](https://openaitools.app/tool/open-webui) | 804,534 | -2.8% | Freemium | Self-hosted AI workspace for local and cloud models | [.md](https://openaitools.app/tool/open-webui.md) |
| 7 | [CREAO](https://openaitools.app/tool/creao) | 619,749 | +39.2% | Freemium | AI agents that run recurring business work for your team | [.md](https://openaitools.app/tool/creao.md) |
| 8 | [AnythingLLM](https://openaitools.app/tool/anythingllm) | 512,269 | +12.9% | Freemium | Own your intelligence: a private AI assistant that runs entirely on your machine | [.md](https://openaitools.app/tool/anythingllm.md) |
| 9 | [Gumloop](https://openaitools.app/tool/gumloop) | 379,557 | -21.1% | Freemium, Paid, Contact for Pricing | Build, share, and control AI agents that actually do the work | [.md](https://openaitools.app/tool/gumloop.md) |
| 10 | [Tines](https://openaitools.app/tool/tines) | 273,260 | +24.4% | Freemium, Contact for Pricing | A secure environment where teams build, deploy and govern AI agents, apps and automations | [.md](https://openaitools.app/tool/tines.md) |

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Source: https://openaitools.app/compare/llmware-vs-patched

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