TOOLS·2026·GUIDE

Top 10 AI Discovery Platforms to Find Real Tools in 2026

I know you don’t have time to scroll social feeds to find new AI tools. Discovery platforms act like search engines for AI, helping you find real, working tools and models for any task.

This guide lists 10 practical discovery platforms you can actually use today, with what they’re best for, when to use them, and direct links.

Top AI Discovery Platforms

1. There’s An AI For That (TAAFT)

What it is: One of the biggest AI tool directories, often described as “Google for AI apps,” with tens of thousands of tools across categories like writing, coding, marketing, and design.
Best for: Quickly finding AI tools by task (for example, “podcast summarizer” or “SEO outline generator”) instead of by model name.
Key strengths:
- Massive coverage, constantly updated
- Strong search by use case and categories
- Useful for inspiration and alternative options
Link: There’s An AI For That

2. TopAI.tools

What it is: A high-traffic AI tools search engine and directory focused on helping you find the exact AI app you need for a given task.
Best for: Engineers who want a clean UI with filters (free/paid, category, popularity) and less noise.
Key strengths:
- Daily updates with new tools
- Strong filters and categories
- Good balance between breadth and curation
Link: TopAI.tools

3. FutureTools

What it is: AI tools directory plus AI news and learning content, listing tools across many categories such as SEO, video, coding, and productivity.
Best for: Discovering emerging and niche tools while also keeping up with AI news and trends.
Key strengths:
- Categorized search (for example, SEO, video, coding)
- News and “how to use tools” content
- Good place to find less mainstream tools
Link: FutureTools

4. Futurepedia

What it is: One of the early, large AI directories listing thousands of tools across verticals like marketing, coding, productivity, and image.
Best for: Broad exploration when you want lots of options in a given category.
Key strengths:
- Wide tool coverage
- Category-based navigation
- Good for initial “what exists?” research
Link: Futurepedia

5. AIcyclopedia

What it is: AI tools directory aimed at creators and productivity users, covering many content and workflow-focused tools.
Best for: Creators, solopreneurs, and small teams looking for productivity-focused AI tools.
Key strengths:
- Broad but approachable catalog
- Strong for content, social media, and creator workflows
Link: AIcyclopedia

6. AI Tools Directory

What it is: A simple, categorized online directory for AI tools with keyword search and category filters.
Best for: Quick scanning of tools by category when you want minimal distraction and a straightforward list.
Key strengths:
- Simple UX with low friction
- Easy to use as a checklist of tools in a niche
Link: AI Tools Directory

7. AI Scout

What it is: AI tools directory often used by founders to list and promote their AI products.
Best for: Finding younger or indie AI tools that might not yet be mainstream but are already listed in startup-friendly directories.
Key strengths:
- Good visibility for early-stage tools
- Useful if you want to spot up-and-coming products
Link: AI Scout

8. EasyWithAI

What it is: AI tools directory with significant traffic and strong commercial intent, used by many AI SaaS founders.
Best for: Discovering tools that other builders consider worth submitting and promoting.
Key strengths:
- Active startup and indie ecosystem
- Good signal that listed tools are at least somewhat maintained
Link: EasyWithAI

9. Toolify.ai

What it is: AI tools marketplace/directory that often appears alongside TAAFT and TopAI in traffic-based rankings.
Best for: Cross-checking popular tools and finding alternatives when you already know roughly what you need.
Key strengths:
- Large and active catalog
- Useful for validation: “Is this tool popular elsewhere too?”
Link: Toolify.ai

10. AI Directory (aidir.wiki)

What it is: Community-driven AI directory with a wiki-style structure, listing a wide range of AI tools.
Best for: Explorers who like community-driven catalogs and want a second look at tools not yet dominant on the big sites.
Key strengths:
- Wiki-like approach and feel
- Alternative view of the AI tool ecosystem
Link: AI Directory

When to Use Discovery Platforms

Use these discovery platforms when you are in “What exists?” mode, not when you are ready to pick a final model:

Good for:
- Getting a landscape view of tools for a domain (for example, code-to-SQL, AI meeting notes)
- Finding non-obvious tools and new launches
- Building a first shortlist of tools to test
Not enough for:
- Deep technical comparison (latency, tokens per second, quality scores)
- Production decisions (SLAs, pricing stability, rate limits)

For evaluation, you will rely on benchmarking platforms like Artificial Analysis, Hugging Face leaderboards, Onyx, and LLM-Stats (covered in a separate article). For hands-on testing, you will use unified access platforms like OpenRouter and AIMLAPI (another dedicated article).

Example Workflow for Engineers

You can reuse this section later in a more complete “how to choose models” guide, but it also fits here as a practical example.

Start with There’s An AI For That and TopAI.tools to search by problem (for example, “requirements to user story generator”).
Cross-check promising tools on FutureTools and Futurepedia to see if they appear in multiple directories, which is a signal of traction.
Shortlist 3–5 tools or models mentioned repeatedly across different platforms.
Move to benchmarking platforms to check whether the underlying models are strong on your task type.
Finally, plug the shortlisted models into unified access platforms like OpenRouter or AIMLAPI to test them on your real data and workflows.

FAQ

How many discovery platforms do I actually need?

In practice, 2–3 platforms are enough for most engineers. For example, combining There’s An AI For That, TopAI.tools, and FutureTools gives you broad coverage without overwhelming you.

Are these platforms only for non-developers?

No. While the UX is friendly, they are very useful for engineers to scan the solution space before picking a specific model or provider.

How do I avoid getting overwhelmed by thousands of tools?

Search by your workflow, not “AI” in general. Narrow by category (for example, SQL, logs, podcasts) and deliberately limit yourself to a maximum of 3–5 tools per problem.

What’s the next step after discovery?

After discovery, move to benchmarking platforms to compare quality and cost, then to unified access platforms to test your shortlisted models on your real data. Those are covered in separate, focused articles in this series.