Phind
FreemiumAI-powered search engine built for developers to get instant, cited answers to coding and technical questions. Note: Shut down January 2026.
What is Phind?
Phind was an AI search engine built specifically for software developers, pairing live web retrieval with model reasoning to answer technical questions grounded in current documentation rather than stale training data. It understood code context, cited its sources so you could verify an answer against the underlying docs, offered a VS Code integration, and was widely used for debugging, API questions and framework migration where general chatbots tended to invent plausible-sounding methods. It is no longer available: Phind shut down permanently in January 2026, one of several search products that closed or pivoted during the consolidation of 2024 to 2026, alongside Neeva, Arc and You.com's consumer search. The pattern reflects how hard it became to sustain a standalone search product against both Google and well-funded AI assistants. Developers looking for equivalent tooling generally use Perplexity, or the search modes built into the major assistants.
Key Features
How to Use Phind
✅ Best For
- Software developers needing citation-backed technical answers
- DevOps engineers troubleshooting infrastructure issues
- Students learning programming with sourced explanations
- Technical researchers cross-referencing API documentation
❌ Not For
- General chat or creative writing tasks
- Non-technical research and content creation
- Current users since the platform is no longer operational
Reviews
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Pricing
- ✓Unlimited Phind Fast model access
- ✓basic features (historical
- ✓no longer available)
- ✓GPT-4 access
- ✓higher limits
- ✓deep research mode (historical
- ✓no longer available)
Prompts to Try
Why is my React useEffect hook running twice in development mode and how do I fix it?
What is the most efficient way to implement rate limiting in a Node.js Express API?
Explain the difference between SQL INNER JOIN and LEFT JOIN with real examples
How do I set up Docker multi-stage builds to reduce image size for a Python Flask app?