Most "best search platform" articles are affiliate pages wearing a lab coat. The scoring is invented, the winner is whoever pays the most, and every product is described in the vendor's own words. This is not that, but it is also not neutral: we build one of the products on this list. You should read it the way you would read a competitor analysis written by a competitor, which is what it is.
What we can offer instead of false neutrality is the thing those articles never do — telling you honestly who each product is right for, including the cases where it is not us. If you finish this and buy Algolia, that is a good outcome, because you will have bought it for a reason instead of because it came pre-integrated with your commerce platform.
The four ways search gets sold
Before the products, the shapes. Nearly every option falls into one of four commercial models, and the model matters more than the feature list, because it determines what happens to you in year three.
- Hosted search SaaS. Your catalog lives on their infrastructure. You pay by records, queries, or both, forever. Fast to start, and the bill grows with your success.
- Enterprise search suites. Search plus personalization, service portals, analytics, machine learning. Sold to large organizations with a sales cycle to match.
- Open-source engines you run yourself. No licence fee, and a permanent operations job. The cost moves from an invoice to a payroll line, where it becomes harder to see.
- One-time licence, self-hosted. Buy the software, install it, own it. Rare in search, common everywhere else in software history.
The hosted SaaS platforms
Algolia
The default choice for a generation of e-commerce teams, and deservedly — the developer experience is superb, the documentation is excellent, and it is genuinely fast. If you are a retailer with a catalog in the tens of thousands and a team who wants search working this week, Algolia is a rational purchase.
The friction is pricing shape. You pay per record and per search operation, so the bill scales with exactly the two things you want to grow. Public reviews are consistent on this point: costs are described as hard to predict, tiers as a cliff, and growth as the thing that triggers the next painful conversation with procurement. Advanced filtering and A/B testing sit on higher plans. Keeping the index synchronized with a catalog that changes constantly is often a custom pipeline someone on your team has to own.
Right for: retail and mid-size e-commerce with predictable catalog growth and no data-residency constraints.
Klevu and Searchspring (now Athos Commerce)
Both merged into Athos Commerce in 2025, along with Intelligent Reach. Klevu's strength was always semantic understanding — it handles conversational queries like "something warm for hiking under a hundred" in a way keyword engines do not. Searchspring's was merchandising for mid-market retail.
The merger is the thing to think about. Existing features keep working, but the roadmap, the pricing decisions and the eventual migration timing now belong to a new private-equity-backed entity. That is not a scandal, it is what happens in software. It is also a risk you are now carrying without having chosen to.
Right for: consumer retail where shoppers describe what they want rather than name it.
Constructor and Bloomreach
Retail-first, heavily merchandising- and personalization-oriented, usually sold with an ongoing optimization service attached. Strong at what they do. Priced and structured for consumer commerce, which means a distributor buying one is paying for a great deal of machinery aimed at a shopper who browses, when their actual customer arrives knowing precisely what they want and typing it badly.
Right for: large consumer retailers with merchandising teams.
The enterprise suites
Coveo
A serious platform, and search is only part of it — knowledge bases, service portals, machine-learning personalization, behavioral analytics across the whole customer journey. If your problem is "our support portal, our website and our intranet all need intelligent search and they should learn from each other," Coveo is built for exactly that and there are few real alternatives.
If your problem is "customers cannot find a conduit fitting," you are buying an aircraft carrier to cross a river. The pricing, the implementation weight and the administrative overhead all assume the larger problem.
Right for: large enterprises unifying search across commerce, service and internal knowledge.
HawkSearch
The incumbent in distribution specifically, and the one most readers of this article will already have been pitched. Owned by Bridgeline Digital, pre-integrated into the B2B commerce platforms distributors actually run, sold through the agencies that build distributor storefronts, and now an official partner of the electrical distributors' trade association. The merchandising console is mature and the platform connectors are real.
Two things worth knowing. First, the commercial shape: it usually arrives recommended — bundled into the platform, or proposed by the agency building your site — rather than chosen after a comparison. That is a legitimate way to buy software, and it is not the same as having evaluated anything. Second, being built for distribution is not the same as being good at broken input. In our July 2026 field test, a distributor running it returned zero results for a single misspelled letter in "circuit breaker."
Right for: distributors who need a merchandising console and pre-built platform connectors, and who will actually check the search quality before signing.
The open-source engines
Elasticsearch and OpenSearch
The most flexible option on this list and the most misunderstood. Elasticsearch will do almost anything you can specify, which is precisely the problem: it is a toolkit, not a search product. Relevance is not something it has, it is something you build, and then keep building.
The real cost is rarely the licence. It is the cluster — a typical production deployment is several nodes, because the JVM consumes memory before doing any useful work — and the engineer who keeps it alive and keeps tuning the analyzers, synonym lists and fuzziness settings as the catalog moves. In the field test, the Elasticsearch deployment we hit answered one conduit query with over 260,000 results. Somebody had built that. Nobody had maintained it.
Right for: teams with a search engineer they are keeping anyway, and requirements too specific for any product.
Solr, Typesense, Meilisearch, Vespa
Solr is the elder statesman, still running quietly under a great many catalogs. Typesense and Meilisearch are lighter, faster to stand up, and pleasant to work with at moderate scale — genuinely good choices for a smaller catalog and a competent developer. Vespa is what you reach for when you have a machine-learning problem wearing a search costume.
All of them share the open-source bargain: no licence fee, and the relevance work is yours. For a distributor catalog full of inconsistent manufacturer descriptions, that work is not small.
Right for: teams who want control and have the engineering time to spend on it.
Dartfind
Ours, so discount accordingly.
A compiled search engine you install on a server you already own, for one payment. No subscription, no per-record billing, no cluster. It runs on an ordinary x86 server, works with the internet physically disconnected, and makes no outbound connections — the catalog, the prices and the query stream never leave your network.
The design bet is on broken input. Typos, half-remembered part numbers, glued words and wrong keyboard layouts land on the right product because the tolerance is a property of the matching math rather than a set of rules configured over the top. There is no synonym dictionary to feed and no fuzziness threshold to tune, which means there is also nothing to decay when the person who set it up leaves. In production today it answers in around 20 milliseconds on a live catalog of 16,787 products, and the engine is built to hold catalogs up to 100 million records on a single machine.
What it does not do: there is no merchandising console. You get ranking controls your team can adjust and full access to your own logs, because it is your server, but if a merchandiser needs to pin products and run campaigns through a polished UI every day, that UI does not exist yet and one of the platforms above already has it. There are also no pre-built plugins for every commerce platform — it sits behind an API and gets integrated, which is days of work rather than an afternoon.
Right for: distributors and wholesalers with large, messy catalogs, customers who type badly, a renewal that keeps growing, and a server in a rack somewhere.
How to actually choose
Ignore the feature matrices, including ours. Ask four questions.
- What do your customers actually type? Pull the site search logs. If the queries are conversational and exploratory, you have a semantic problem and Klevu-style products are built for it. If they are part numbers, spec fragments and misspellings, you have a matching problem and you should be testing for that specifically.
- How many zero-result searches do you serve for products you stock? This is the number nobody looks at, and it is the size of the problem in one figure.
- Where does the cost go in year three? Not year one. Every model looks reasonable in year one.
- Who maintains relevance, and what happens when they leave? If the answer is "we have rules and dictionaries someone tunes," you have bought a permanent job, not a product.
Then run the thirty-second test: type a part number wrong on your own site and look at what comes back. It will tell you more than any comparison table on the internet, including this one.