Citation dashboards miss one corner of AI search entirely: the people who built their own Perplexity. The stack has matured to the point where running a private answer engine on your own hardware stopped being weekend hackery and became a docker command. One open-source project that does it crossed 36,000 GitHub stars on the strength of a single viral pitch: a full Perplexity clone, running 100 percent locally, for zero dollars.
Three names keep coming up around it, Perplexica, Ollama and SearXNG, and people confuse them all the time, even though they're three different layers of one machine. I'll untangle them here. And since this blog cares about visibility, I'll finish on the question the other write-ups skip, which is what self-hosted AI search means for the sites being searched.
The stack in one picture
Any Perplexity-style answer needs three jobs done. Something has to search the web. The results then need reading and turning into an answer, and a third piece sits behind the interface orchestrating the other two. In the local version, SearXNG does the searching and a model served by Ollama does the writing, while Perplexica orchestrates. You can swap out any one layer and the machine keeps running, which is exactly why the ecosystem settled on this trio: each is the least locked-in option for its job.
Perplexica, which is now called Vane
The first thing to know, because it trips up every search for this tool, is that Perplexica renamed itself to Vane about six months ago. Nothing else changed. The repository and the maintainer are the same, it's still MIT-licensed, and the rename commits sit right there in the file history.
Vane is the orchestration and interface layer. It's an AI answering engine that searches the web in real time and cites every source it uses, and its Speed, Balanced and Quality modes let you pick your own trade-off. Beyond general web search it can pull from discussions or academic sources, and it accepts file uploads and handles image and video search as well.
On the model side it's promiscuous by design. It runs local models through Ollama, and it will just as happily call hosted ones from OpenAI and Anthropic, or from Groq and Google. SearXNG ships bundled as its default retrieval layer, which is the sensible choice and the reason the next section exists.
If you want to poke at it, the project's GitHub is the canonical starting point. The health signals look the way you'd hope for a community tool this popular, with a four-digit commit count and active issue traffic, plus a steady release rhythm.
SearXNG, the retrieval layer
SearXNG is the oldest idea in the stack and the least AI of the three. It's a free, self-hostable metasearch engine: it takes one query, fans it out to other search services, up to 264 of them, and merges what comes back without tracking or profiling anybody. It predates the AI search wave. The wave adopted it because an answer engine's first problem is getting good web results without paying a search API for every query.
If you just want to try it bare, around seventy public instances run at searx.space. For an AI stack you self-host it, partly to keep rate limits sane and partly because that privacy promise only fully holds on your own box.
The r/LocalLLaMA crowd is fond of it for good reason. A well-upvoted thread there on giving a local agent web access without paid search or scraping APIs lands on SearXNG as the answer, and the logic is simple: real web access changes what local AI is for, and this is the free way to get it.
Giving a local agent web access without paid search/scrape APIs: SearXNG + Scrapling
I wanted web access for a local-first agent without reaching for Tavily, Serper, Firecrawl, etc. For this agent path, I wanted no paid API keys, a search service I control, and page extraction I can run myself. What I ended up with is two t...
The same community is upfront about the catch. Free web results at scale is the hard problem no self-hosted project fully solves, because public engines rate-limit aggressively and a heavy research workload will run straight into that. At personal or small-team volume, SearXNG is quietly excellent.
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Ollama, the model layer
Ollama is where the intelligence runs. It began as the easiest way to serve open models on your own machine, with one command to pull a model and run it, and it has grown well past that. There's a desktop app now and a model library. There's also a cloud tier that hosts larger open models in the US, Europe and Singapore for workloads your laptop can't hold, free to start, with a Pro tier at $20 a month.
It's worth being precise about what Ollama does in this stack, because the marketing around it drifts. It's a model server, and it doesn't search anything by itself. The 2026 homepage leans hard into coding agents, which tells you where its nine million developers actually spend their time.
For our purposes the relevant sentence is simpler: point Vane at your Ollama endpoint and pick an open model that fits your hardware, and the answer-writing layer of your search engine runs entirely on silicon you own, with prompts that never leave the building.
The honest state of the ecosystem
The viral threads tend to skip this part. A community survey of local research tools this May found two healthy flagships and a long tail of semi-abandoned experiments. Some had hundreds of open issues nobody answered, others hadn't seen a commit in eight months, and pull requests sat hanging for weeks.
Underneath that sits a wider pattern. Chat and coding are both solved locally, but deep multi-step research synthesis still needs more scaffolding than it should, and every project that promised it for free eventually ran into the same rate-limited wall.
So set your expectations by use case. For daily question-answering with citations, the Vane stack is good, and the privacy is absolute rather than promised. Deep research reports are possible but patchy, and they take more assembly. Replacing a team-wide Perplexity subscription only works if someone owns the maintenance, because on a self-hosted stack the repairs are yours too.
For the curious, setup takes about thirty minutes. Docker compose brings up Vane with SearXNG bundled, ollama pull fetches an open model sized to your RAM, and the settings screen wires the two together. A mid-range machine handles retrieval and the interface easily, and the model is the only layer that cares about your hardware budget.
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Choosing your mix
You don't really face a binary between all-local and all-hosted, since the whole appeal of this stack is that each layer picks its own side. Three configurations cover most teams.
The fully local setup is Vane with its bundled SearXNG, plus Ollama serving an open model on your own hardware. You get total privacy and zero per-query cost, and answer quality tracks whatever model your RAM can hold. On a 16 GB machine that means a capable small model, while a workstation with a real GPU can run something genuinely competitive. I'd point regulated environments here, and anyone whose search history is sensitive in itself.
Most self-hosters quietly settle on the pragmatic middle, which is local retrieval with a hosted brain. SearXNG does the searching on your box and an API model writes the answers. Your queries still stay out of search vendors' logs, the synthesis jumps to frontier quality, and the cost comes to API pennies where a subscription used to be.
The third option is an Ollama cloud hybrid. You keep the stack local and point Vane at Ollama's hosted tier when a job outgrows your hardware, then point it back, so the interface stays the same and the capability stretches, for twenty dollars a month at the ceiling.
The cost math against a Perplexity subscription is unromantic. You pay in hardware you own and electricity you already pay for, and in time, which is the line item that really counts. Setup is thirty minutes, but keeping three moving projects updated is a recurring chore someone has to own. Price that in before declaring victory over your subscription, along with the failure mode: a stack nobody maintains degrades into a chat window that can't search, usually in the week you need it.
What this means for the sites being searched
This last part is for marketers, and it points somewhere you might not expect. People using these stacks are invisible to you. Their queries hit no vendor's logs and their answers render on private hardware, so no citation tracker anywhere can sample what a thousand self-hosted instances told their owners this week. A slice of AI search has gone permanently dark, and it skews toward the technical, privacy-conscious buyers B2B teams care about.
Influencing them doesn't take anything new, though. SearXNG retrieves through the same mainstream indexes your SEO already targets, so ranking is still the ticket into these answers. These stacks also read raw HTML without executing JavaScript, exactly like the hosted engines' own fetchers, which makes server-rendered, plainly structured pages the shared prerequisite on every surface, hosted or local.
What changes is the measurement, while the playbook stays put. You can track your citations across the seven big engines weekly and treat that as your proxy for the dark slice. The theory behind it is a reasonable one: a page the measurable engines keep citing is winning the invisible retrievals too, since they all drink from the same indexes.
One prediction, which I'd hold loosely. As answer engines consolidate and their source selection tightens, the self-hosted stack becomes the escape hatch power users reach for, the way RSS readers and ad blockers were. I find that reassuring, because the weirdest corner of AI search rewards the same strategy as the mainstream one. The brands that stay visible there will be the ones that won retrieval the boring way, with pages worth citing on domains that rank.
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