Fireplexity vs Perplexity:
what you actually trade.

What each one actually is

Fireplexity is an open-source AI answer engine published by Firecrawl, now at v2. It is a Next.js and TypeScript app, MIT licensed, sitting at roughly 2,000 GitHub stars. It takes a question, calls Firecrawl's /search endpoint for live web, news and image results, scrapes the pages it picks with /scrape, and streams a synthesized answer back with inline citations pointing at the pages it read. It ships with a Groq key for inference, detects stock tickers and renders charts for them, and deploys to Vercel in about five minutes. You need two API keys: FIRECRAWL_API_KEY and GROQ_API_KEY.

Firecrawl were explicit about the point of it: prove that a developer can build a domain-specific answer engine without renting one. The pitch line was “stop waiting for someone else to build the tool you need.”

Perplexity is a company. It runs its own crawler and index, its own model family (Sonar), and buys access to frontier models on top — Pro subscribers reach GPT‑5 and Claude Opus 4.6. Around the search box sits a product surface Fireplexity does not attempt: Deep Research, Labs, Perplexity Computer, image and video generation, file uploads, apps on every platform, and Comet, a browser that has been free since October 2025. Pricing runs Free, Pro at $20/month ($16.67 billed annually), Max at $200/month or $2,000/year, Enterprise Pro from $40/seat/month, and Enterprise Max at $325/seat/month.

Head to head

The cost question, with the arithmetic shown

“Open source is cheaper” is the claim everyone makes and nobody prices. Here is the actual math, at list prices, for one Fireplexity query.

Assume a typical query: one search returning ten results, six of those pages scraped for context, about 15,000 tokens of scraped markdown fed in, and an 800-token answer out through gpt-oss-120b on Groq.

So a heavy individual user is not obviously better off self-hosting. A product serving thousands of queries a day is on a completely different curve, because Perplexity's consumer pricing is per seat and Fireplexity's cost is per call.

The honest comparison for anyone building software is Perplexity's Sonar API, which is also metered. Sonar Pro lists at $3 per million input tokens and $15 per million output, plus a per-request fee billed separately — between $6 and $14 per thousand requests depending on the search context size you ask for. Note that with Sonar you do not pay input tokens on the retrieved context; that is what the request fee covers. So a thousand requests with a short prompt and an 800-token answer runs about $0.60 of input, $12 of output and $6–14 of request fees: call it 1.9¢ to 2.7¢ per query. Base Sonar at $1/$1 is cheaper; Sonar Reasoning Pro sits at $2/$8.

Fireplexity at ~1.1¢ is in the same order of magnitude — perhaps half the price, and highly sensitive to how many pages you scrape. Scrape twelve pages instead of six and you have spent the difference. Cost is not the reason to self-host. Control is.

Citations: where both of them are weaker than they look

Both systems put numbered citations next to their sentences, which reads as proof. It is not quite proof, and the problem is architectural rather than a Perplexity failing.

Independent review of Perplexity has found real sourcing problems at scale — one 2026 assessment reported that 45% of responses carried at least one significant issue, 31% had sourcing problems and 20% had major accuracy issues, with citations pointing at homepages rather than articles, at mirrors and secondary blogs rather than the publisher of record, and occasionally at pages where the quoted sentence simply is not present. A 2026 paper titled Cited but Not Verified makes the structural point directly: retrieval-augmented generation attaches a source to a claim without ever checking that the source supports the claim.

Fireplexity has exactly the same architecture and therefore exactly the same failure mode. What differs is what you can do about it. The prompt, the content-scoring step that decides which scraped pages make it into context, and the citation-mapping logic are all in a repository you can read. If your domain needs verbatim-quote checking, or a rule that discards syndicated copies in favour of the original, you can write it. On Perplexity you can only report the bad answer.

That is the sharpest real distinction between the two, and it is not about cost or features: one of them lets you fix the failure mode.

The scraping overhang

Perplexity is being sued a lot. CNN filed on 28 May 2026 over more than 17,000 scraped stories, videos and images — the seventh major publisher suit in under two years, joining near-identical claims from The New York Times, Dow Jones, the New York Post, Encyclopedia Britannica, Merriam-Webster and the Chicago Tribune. The recurring theory is that AI summaries substitute for the visit rather than sending traffic back, and publishers remain unconvinced by the company's trust-and-transparency pitch and its publisher revenue-share program.

It would be a mistake to read this as a point for self-hosting. Fireplexity retrieves by scraping live pages too. Running it yourself does not remove that exposure — it transfers it from a company with a legal department to you. If you deploy an answer engine over content you do not own, at any scale, the questions about robots directives, terms of service and attribution are now your questions. Restricting Fireplexity to your own documentation, your own catalogue, or licensed sources is the deployment that sidesteps this entirely, and it is also the deployment where Fireplexity is most clearly the right tool.

What Fireplexity does not give you

Fireplexity is a demonstration that happens to be production-shaped, not a product. Be clear-eyed about the gap:

• No accounts, auth, or rate limiting. Ship it publicly as-is and your API keys fund strangers' queries.
• No evaluation harness. You have no way to tell whether your prompt change improved answers except by reading them.
• No Deep Research, Labs, or agentic browsing. It answers a question from a handful of pages; it does not run a twenty-minute multi-step investigation or drive a browser.
• No apps. No iOS, no Android, no desktop client, no Comet-style browser integration, no shareable thread history unless you build it.
• No frontier model included. The default Groq setup is fast and very cheap; it is not Claude Opus 4.6. You can point it at one — and then your per-query cost changes by an order of magnitude.
• You own the operations. Uptime, key rotation, cost spikes, abuse, dependency upgrades on a Next.js app.

Meanwhile Perplexity's actual moat is not the answer box — that part is genuinely commoditized, which is the whole thesis Fireplexity demonstrates. The moat is everything around it: the index, the distribution across every platform, the model deals, and a browser people leave open all day.

If Fireplexity is not the right Perplexity alternative

Fireplexity is one of several open-source Perplexity alternatives, and it is not the most popular. Being honest about that is more useful than pretending otherwise, because the alternatives differ in a way that actually decides which one you should run: how they get the web.

The Vane versus Fireplexity decision is the real one, and it is architectural. Vane — renamed from Perplexica in March 2026, same maintainer and codebase, around 20,000 GitHub stars — bundles a private SearXNG instance, so it answers from the snippets and results that existing engines already have. Point it at a local model through Ollama and the whole stack runs offline at zero marginal cost. Fireplexity instead scrapes the pages themselves on every query, which gets you full article text and JavaScript-rendered content rather than snippets, at a metered per-query price.

So: if you want a free self-hosted Perplexity alternative to use, run Vane. If you want fresh full-page context, or a few hundred readable lines to embed in your own product, Fireplexity is the better starting point. Twenty thousand stars against two thousand reflects the first use case being far more common than the second.

If you would rather not host anything, the hosted alternatives split by purpose: ChatGPT with search is the closest mass-market competitor with stronger reasoning but less systematic citations; You.com is nearest to Perplexity in philosophy and has added multi-step research agents; Kagi is the privacy-first, user-funded option with AI as an opt-in rather than the product; Exa is the developer-facing search API; and Gemini and Claude win on freshness-plus-Google-integration and on careful reasoning respectively.

So which one

And if you are a developer choosing a retrieval backend rather than a subscription, the real bake-off is Fireplexity against the Sonar API. Fireplexity wins on transparency and source control at a slightly lower price; Sonar wins on latency, on not maintaining anything, and on an index that does not need to be scraped fresh on every query. Both are defensible. Only one of them lets you open the box.

Common questions