Best for
Best for organisations already on AWS that want research, automation, analytics and app building behind one assistant.
- Freelancers
- Small and mid-sized
- Enterprise
No tools match
Press / to search, arrow keys to move, Enter to open
AI Chatbots & Assistants Comparison
At a glance
Best for
Best for organisations already on AWS that want research, automation, analytics and app building behind one assistant.
Best for
Best for developers and teams that want an assistant whose data never leaves the machine, with a cloud fallback for models too large to run locally.
Cataleo does not name a winner. Both statements come from the vendors themselves.
| Criterion | Amazon Quick | Ollama |
|---|---|---|
| Starting price | Free plan; Plus $20 / user / month Billed annually, $25 billed monthly. Max is $100 / user / month annually. The organisation plans (Professional $20, Enterprise $40 per user per month) require an AWS account and add a $250 per account per month infrastructure fee. | Free for local models; Pro from $20 / month Local models cost nothing. Pro is $20 per month or $200 per year and includes $60 of cloud usage credits a month with three concurrent requests; Max is $100 per month with $300 of credits and ten concurrent requests; Team is $500 per month with $1,000 of shared credits and unlimited users; Enterprise is quoted. Extra credits can be bought on every paid plan. |
| Free trial | Free plan, plus a 30-day Enterprise trial for up to 25 users with the infrastructure fee waived | Free plan with local models, starter cloud credits and one concurrent request |
| Commercial use | Yes | Not stated |
| Output | Documents, presentations, spreadsheets, images, dashboards, cited research reports and interactive web apps | Not stated |
| API | Yes, existing Amazon QuickSight APIs and SDKs continue to work unchanged | Yes, one REST API for local and cloud models, with Python and JavaScript libraries and a separate web search API |
| Hosting | Cloud (SaaS), plus a desktop application | Runs locally on macOS, Windows and Linux; cloud models run on Ollama's servers in the United States, Europe and Singapore |
| Ecosystem | AWS. Built-in connectors for Jira, Box, Canva, HubSpot, Notion, QuickBooks, Salesforce, ServiceNow, Shopify, Slack, Smartsheet and Zoom, plus MCP and OpenAPI for anything else; usable from the web, a desktop app, Chrome, Slack, Microsoft Teams and Microsoft 365 | Desktop app and command line for macOS, Windows and Linux, with integrations for VS Code Chat, Claude Code, OpenCode and n8n |
| Live web search and sources | Yes, Quick Research works across the web and your own data and delivers the result as a cited report | Yes, through a separate web search API returning a title, URL and content snippet per result; it needs a free account and an API key, and is also reachable over MCP |
| Files and long documents | Yes, Quick Index connects organisation documents and data sources so answers are grounded in them, and the desktop app works with local files | Yes, the desktop app takes files by drag and drop so a model can reason over text or PDFs, and code files can be passed in for understanding; for large documents the context length is raised in the settings, which the vendor notes costs more memory |
| Images, voice and video | Turns a conversation into documents, presentations, spreadsheets and images without leaving the app | Images for vision-capable models, passed as a file path, URL or base64 through the API and the libraries |
| Coding help | No coding assistant is documented. AWS aims Quick at anyone who works with information, stating that no technical skills or coding experience are required, and the apps, flows and automations it builds are described in natural language rather than written | Coding models in the library, plus integrations with terminal coding agents such as Claude Code and OpenCode and with VS Code Chat |
| Model choice | No model picker. AWS documents Quick as fully managed, with no infrastructure to provision, no models to host and no ML expertise required, and states that customer data is not used to train models on any plan | Yes, and it is the point: any model from the library or an imported custom one, run locally or offloaded to the cloud through the same call |
| MCP-ready | No | stated Yes |
| Deployment | Cloud (SaaS), Browser-based | Cloud (SaaS), On-premise |
| Support | Not stated | Email helpdesk, Community forum |
| Onboarding | Documentation and knowledge base | Documentation and knowledge base |
| Company size | Freelancers, Small and mid-sized, Enterprise | Freelancers, Small and mid-sized, Enterprise |
| Integrations | more entries Jira, Box, Canva, HubSpot, Notion, QuickBooks, Salesforce, ServiceNow, Shopify, Slack, Smartsheet, Zoom, Microsoft Teams, Microsoft 365 | VS Code, JetBrains, Zed, Xcode, Claude Code, Claude Desktop, OpenCode, Cline, Roo Code, Codex CLI, Goose, n8n, marimo |
Adds a column to the table, from the tools in this category.
Row where the values differ The small markers point at the lower number, the longer list or the stated feature. They describe the data, they are not a verdict.
The short version
Every line is one datapoint from the table above, picked automatically. Nothing here is written text.
The trade-offs
Amazon Quick
Strengths
Limitations
Ollama
Strengths
Limitations
Plans
Amazon Quick
Ollama
What users say
Amazon Quick
No reviews yet. Be the first to review this tool. Every review is checked for fairness before it appears, and the vendor cannot have one removed.
Ollama
No reviews yet. Be the first to review this tool. Every review is checked for fairness before it appears, and the vendor cannot have one removed.
Where this comes from
Amazon Quick https://aws.amazon.com/quick/ · checked against the official source on 2026-09-05
Ollama https://ollama.com · checked against the official source on 2026-09-12
This table lists factual criteria taken from information the vendors publish themselves. For how we put it together, see the methodology.