How do you choose between unified search and federated search? Well, imagine asking three friends for the best Italian food recipes. 

The first time you do, one friend offers magazine recipes, another offers cookbook recipes, and a third gives you online recipes. To find the best, you have to ask each friend individually – then get each friend’s list of top recipes, and then sift through and compare the recipes to find the one you want. Possible, but very time consuming.

A graphic illustrates the concept of siloed search

Let’s imagine another scenario. You have one Italian food-loving friend who has all kinds of recipes at their fingertips. But when you say, “Give me a recipe for your best Italian dish,” your hyper-organized friend gives you three separate stacks and says, “I put the best recipes from magazines in the first stack. The best recipes from cookbooks are in the second. And the best online recipes are in the third.”

A graphic illustrates the concept of federated search

This is certainly easier than having to ask three separate people. But you’re still on your own to figure out which recipe is the best overall. 

It’s not hard to imagine an even better scenario. You have one friend who tracks recipes across various sources. When give them the same request for “best Italian food recipe,” your friend says, “Here you go!” and presents you with one list of Italian food recipes, ranked by awesomeness. 

A graphic illustrates the concept of unified search

This list might include recipes from many sources, but what’s important for you is that the list is sorted by a category of your choice—that is, their awesomeness. You don’t care where each recipe comes from, and your friend knows that, which makes her the best Italian food-friend ever and your future go-to person when it comes to any sort of recipe.

Types of Query Systems

These three scenarios perfectly illustrate the difference between three types of search systems. The first one is largely outdated traditional search (which involved logging into several different systems and searching each one independently), the second one is called federated search (you log in once and search across content sources, but the different content sources still don’t talk to each other), and the last one is unified search (and your best Italian food friend) who knows that relevance is the most important thing when it comes to search and does everything to get you there. 

With traditional search being largely—thankfully—phased out these days, it’s important to focus on the difference between the latter two: unified search vs federated search. What is that difference, and what makes one better than the other?

When federated search first came along, it was a huge step forward from traditional search. Traditional search required users to log into different disparate systems to find the different pieces of information they needed. That meant they had to know which data silo to look in, as well as remember all the passwords and logins for each database. 

Federated search changed all that as it gave users a single interface in which to query across all these silos. While most traditional search methods only indexed a single data source at a time, federated search allowed indexing multiple data sources at once, and then presenting the results to users in one unified interface.

How exactly does it work? Federated search retrieves information from a variety of sources via a search application that’s built on top of search engine(s). When a user makes a single query, the federated search engine simultaneously searches multiple, usually disparate databases and ecosystems, returning results from all sources and presenting them in a single user interface.

Federated search became invaluable in complex organizations that have thousands of sources in the cloud and on-premise, where searching this via traditional search was simply unfeasible.

Still, there are both pros and cons to this method.


  • Fast and easy to implement: Federated search is relatively simple and fast to implement. Since it is a query-time merging method that doesn’t require setting up a central index (compared to unified search, which we’ll discuss in a moment), it can be set up pretty quickly.
  • Indices do not have to be standardized: Another advantage of federated search is that indices do not have to be standardized. If one dataset is in one format, and the other is in another format, that’s not a problem. 
  • Always up-to-date: Since each individual index is maintained separately, as long as each of them is kept up-to-date, the federated search engine does not have to consistently go back and index the source to make sure the content and results are up to date.


  • Impossible to achieve search relevance: The main problem is that with federated search it is impossible to achieve true relevance. Because each index is searched separately, the results are ranked by source—and ranking by relevance across all content sources is impossible. (Remember your organized friend who keeps recipes from different sources neatly stacked in separate piles?)
  • Poor search user experience: Users’ expectations from search today are sky-high. If you think a search experience consists of a search box plus a results page, think again. Users today expect much more from their search experience: they expect websites to interpret their misspellings correctly, anticipate their next move, and learn from their previous clicks and searches.

That means that a good search experience today requires different additional capabilities such as autocomplete, query suggestions, filtering and faceting, and so on. This is nearly impossible to achieve in federated search. Unless each separate index supports each of these capabilities, your federated search won’t be able to offer these.

  • Slow response times: In federated search, the response times will often be slow. Because it searches across multiple indices, the response time will be only as fast as its slowest content source.

It remains true and will always be true that content lives in multiple disparate systems and silos. That won’t change. But users don’t need to know whether you’re pulling your recipes from a magazine or a cookbook: they couldn’t care less, they just want to start cooking (or buy the best pair of shoes or the best spice grinder), and they want to get to their goal as fast as possible. The secret is to create a user experience where users don’t have to be distracted by irrelevant information such as which data source the right content is hiding in.

That’s where unified search comes in.

Unified search differs from federated search in that it creates a single unified index of content from across all content repositories — including the option to crawl or push content stored in the cloud or on-prem via connector library or APIs — and ranks the entire corpus in a single user interface, applying relevance across the board and delivering meaningful results.

With unified search, when the index is created, it captures all the information needed in order to apply consistent relevance ranking rules across all of your content for the queries which come in. 

Here’s how unified search compares to federated search.


  • Faster response times: Unified search has faster query response times than federated search. This is because results are sourced from one unified index as opposed to searching multiple systems with query-time merging. By contrast, in federated search, the response times are only as fast as its slowest content source.
  • Better relevance: With unified search, relevance is applied across the entire corpus of content and can even self-optimize with the application of machine learning. That means that results will always be ranked by relevance, regardless of the source. 
  • Better UX experience: Unified search guarantees better user experience. Search results are blended and pages can be built from a component library or APIs. Sorting, filtering, faceting and other capabilities of the search UX are built on top of the unified index.


  • Slower set up time: Unified search might take longer to set up than federated search, as the set up is dependent on the number, size, and location of sources. However, for most organizations, the additional time and effort are worth it.

Unified Search vs Federated Search: Which Is Right For You?

Let’s go back to our Italian food recipesfor a moment. 

Which of these three scenarios makes you more excited about rolling up your sleeves to cook? Likely, when you hear the first one and how much work it will require, you think “I won’t even bother.”

The second one sounds a bit more doable but still quite daunting (and makes you want to find new cooking-obsessed friends).

We bet the third one makes you want to jump from your chair ASAP and run to your kitchen and try making a mouth-watering dish. 

In today’s world, people don’t want to spend time on searching and sorting. In fact, a good search experience today is one that doesn’t even require you to search. 

Can you get there with federated search? We say that if your content lives in more than one database (as is often the case), it will be nearly impossible.

Here is a summary comparing unified search vs federated search, to help you decide which one is right for you:

  Unified Search Federated Search
 Definition Creates a single, unified index of content from all data sources, that is searched at one time. Sends a query to separate search engines for each data source made available by the platform.
Content ability to crawl or push content stored in the cloud or on-prem via connector library or APIs relies upon a query federator that depends on the underlying search engine where the content resides
Relevance Tuning apply relevance across the entire corpus of content and can even self-optimize with the application of machine learning  each search engine must be tuned independently, making it nearly impossible to provide a relative ranking across all content sources
Query Speeds faster query response times as results are sourced from one unified index as opposed to searching multiple with query-time merging query response times become an issue when the response for the results automatically defaults to the slowest content source
Search UI search results are blended and pages can be built from a component library or APIs search results are segmented by the system of origin and a user must navigate between sources
Sorting, Filters & Facets sorting, filtering, faceting and other capabilities of the search UX are built on top of the unified index all “federates” must support a capability (e.g. sort, filter, facets) for it to be made available in the UI
Index Setup Time set up is dependent on the number, size, and location of sources lower set up time as content is not crawled or standardized in an index
Security use an early-binding permission retrieval method to import item permissions at crawling time multiple security measures that need consideration as these are handled at the individual index level 

Today the search UX is incredibly important to users. A good search is one where you barely feel you’re doing the work of searching, but one that gets you to your desired outcome faster than you can say “lasagna.”

Don’t believe us? Give our trial a spin, which will allow you to build a search prototype in minutes! 

Dig Deeper

Wondering where your search experience falls on the search spectrum? Find out with our Coveo Relevance Maturity Model. You can also get a search audit done by experts! 

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