Running LLMs locally with Ollama, llama.cpp, or vLLM gives you privacy and control, but those models lack real-time web knowledge. Adding a search API for grounding bridges that gap. The best grounding API returns structured results that a local model can consume through tool calls or context injection. We ranked five options by compatibility with local inference, result quality, and cost.
Scavio's MCP server at mcp.scavio.dev/mcp works with any MCP-compatible client running on top of local models. The structured JSON output is designed for tool-call consumption, and six-platform coverage gives local models grounding data that web-only APIs cannot match.
Full Ranking
Scavio
Multi-platform grounding for Ollama and llama.cpp agents
- MCP server compatible with local inference stacks
- Six platforms for diverse grounding data
- Structured JSON maps to tool-call format
- 250 free credits for evaluation
- Requires internet for API calls (local model, remote search)
- No local deployment option for the search API
Tavily
Web grounding with AI pre-processing for context windows
- AI summaries reduce token count for small context windows
- 1K free credits for testing with local models
- LangChain integration works with local model backends
- AI summaries add hallucination risk to grounding data
- Web only, no product or social grounding
- Summaries may not suit factual grounding needs
Brave Search API
Simple web grounding with independent index
- Independent index for non-Google-dependent grounding
- $5 free monthly credit
- Clean JSON snippets
- Web only
- Free tier removed Feb 2026
- No MCP server or framework adapters
YaCy + llama.cpp
Fully local grounding pipeline with no external calls
- Completely local with yacy_expert RAG
- No internet required once index is built
- Total privacy and data sovereignty
- Index quality depends on crawl scope and freshness
- Significant infrastructure requirements
- Slow indexing and search compared to cloud APIs
Perplexity Sonar
AI-enhanced grounding for complex queries
- AI processing with citations for grounding
- Good for complex research queries
- Pro tier for deeper searches
- Token costs on top of request pricing
- Higher total cost at scale
- No official local model integration
Side-by-Side Comparison
| Criteria | Scavio | Runner-up | 3rd Place |
|---|---|---|---|
| MCP compatibility | Yes (hosted server) | Community adapter | No |
| Works with Ollama | Via MCP client | Via LangChain | Custom wrapper |
| Grounding platforms | 6 platforms | Web only | Web only |
| Fully local option | No (remote API) | No | No |
| Cost per grounding | $0.005 | Free to $0.03 | $0.005 |
| Result structure | Tool-call JSON | AI summaries | JSON snippets |
Why Scavio Wins
- The MCP server provides the fastest integration path for local LLM stacks: configure the MCP client to point at mcp.scavio.dev/mcp and your Ollama-hosted model can call search as a tool.
- Six-platform grounding gives local models access to Google, YouTube, Amazon, Walmart, Reddit, and TikTok data, far richer than web-only alternatives.
- Structured JSON output maps to tool-call response format, which local models trained on tool-use can parse without additional prompting.
- At $0.005 per credit, the API cost is negligible compared to the GPU cost of running local inference, so grounding adds minimal overhead to the total cost.
- For fully local and offline grounding, YaCy + llama.cpp is the only option, but the index freshness and quality tradeoffs make it unsuitable for most production grounding needs.