How much does search functionality cost to build into an app?

Search is one of those features that looks simple from the outside and turns into a month of engineering work once someone starts building it. A basic search bar takes a few days. Search that actually works, ranking relevant results first, ignoring typos, filtering by category, suggesting completions as you type, is a fundamentally different engineering problem.

The cost range is wide for a reason. A $3,000 search feature and a $15,000 search feature are not the same product. This article explains what separates them, what the infrastructure will cost you every month, and when it makes sense to pay for a managed service instead of building your own.

The simplest search works by scanning your database for records where a column contains the exact word someone typed. Type "blue shirt" and it returns every product with those exact words in the title. It takes a developer a day or two to wire up and costs almost nothing to run. The problem is that it is brittle in ways your users will notice immediately.

Type "shirts" instead of "shirt" and you get zero results. Misspell one word and the search fails silently. Search for "blue top" when your products are labeled "shirts" and nothing comes back. These are not edge cases. They are how real people search.

Full-text search solves this by analyzing the meaning of words, not just the characters. It treats "shirt" and "shirts" as the same concept. It understands that "top" and "blouse" belong in the same neighborhood as "shirt." It ranks results by how closely they match what the person was actually looking for, not by whether a string appeared in a database column.

The business difference is measurable. Baymard Institute research from 2021 found that 70% of e-commerce sites return zero results for legitimate product queries that use different words from what the product catalog uses. A search that fails a query returns nothing and loses a sale. Full-text search recovers those queries.

The cost difference is real too. Basic keyword search: $2,000–$4,000 to build, near-zero to run. Full-text search built from scratch: $6,000–$10,000 to build, depending on how much data you have and how sophisticated the ranking needs to be.

When a developer says "we should add Elasticsearch," they mean they want to use a purpose-built search engine instead of asking your main database to do a job it was never designed for. Your database stores your data. Elasticsearch is a separate system designed specifically to make that data searchable, fast, flexible, and accurate regardless of how the user phrases their query.

Here is what that means for your business. When a user types something into the search bar, the query goes to Elasticsearch instead of your main database. Elasticsearch has already processed all your content, products, articles, users, whatever, and indexed it in a way that makes searching fast. Results come back in under 200 milliseconds for most apps, even with hundreds of thousands of records. More importantly, they come back ranked: the most relevant results appear first, not just the records that happen to contain the query string.

Algolia is a hosted version of the same idea. Instead of setting up and managing your own Elasticsearch server, you send your data to Algolia's infrastructure and use their API to search it. The tradeoff is straightforward: less setup and maintenance work up front, but a monthly fee for as long as your app runs.

Building an Elasticsearch integration, setting up the search engine, writing the code that keeps it in sync with your database as data changes, and connecting it to your app's frontend, typically takes 2–4 weeks of engineering time. At AI-native team rates, that is $4,000–$8,000. A Western agency doing the same integration would bill $15,000–$25,000 for identical scope.

Algolia integration is faster. Because Algolia handles the infrastructure, the engineering work is mainly about connecting your data and configuring how results should be ranked. That compresses the timeline to 1–2 weeks and the cost to $3,000–$5,000 at AI-native rates, versus $10,000–$18,000 at a Western agency.

A working search engine is the foundation. What sits on top of it determines whether search becomes a feature users trust or one they ignore.

Filters let users narrow results by category, price range, date, location, or any attribute your data has. They sound simple, but the engineering work is in making them compose correctly. A filter for "under $50" combined with a filter for "in stock" combined with a text query should return results that satisfy all three conditions, ranked by relevance. Getting that logic right, building the UI that controls it, and keeping it fast as filter combinations multiply adds $2,000–$4,000 to the base search cost.

Autocomplete, the suggestions that appear as someone types, is a separate system from search itself. It works by maintaining a list of common queries and popular results, then surfacing the most likely completions based on the first few characters typed. Done well, it reduces the number of keystrokes a user needs before finding what they want and catches typos before the query is even submitted. Adding this to an existing search setup costs $2,000–$3,500.

Relevance tuning is the part that often gets skipped and becomes a problem six months after launch. Out of the box, a search engine ranks results by how closely the text matches the query. But that is not usually what you want. A product that sold 500 units last week is probably more relevant than one that sold two, even if the text match is slightly weaker. A recent article is more relevant than a five-year-old one. Surfacing promoted results above organic ones without making the promoted ones feel like ads is its own problem. Configuring this ranking logic takes time, figure $1,500–$3,000, but it is what separates search that users trust from search that users learn to route around.

Combine all three additions and you are adding $5,500–$10,500 on top of the base integration cost.

This is the question founders forget to ask. Build costs are one-time. Infrastructure costs run forever.

If you build search on your own infrastructure using Elasticsearch, the monthly cost depends on how much data you have and how many queries you handle. A small app with under 100,000 records and modest traffic can run Elasticsearch for $80–$150/month on a basic cloud server. As your data grows and your traffic increases, that cost scales. A mid-size app handling a million records and real search volume should budget $300–$600/month. You own the infrastructure, so there is no per-query fee, but you also own the maintenance.

Algolia prices by the number of searches and the number of records you have indexed. Their free plan covers 10,000 searches and 10,000 records per month, which covers early-stage testing. Once you have real users, the cost ranges from $500/month for a small app to $2,000+/month for a product with meaningful search volume. You get a managed service in exchange: no servers to maintain, no search engine to upgrade.

One number worth keeping in mind: search infrastructure is one of the few app components where the monthly cost can exceed the build cost within a year. A $5,000 Algolia integration at $1,000/month costs more in infrastructure after five months than it cost to build. That is not necessarily wrong. If search drives conversions, the ROI justifies it. But it should be part of the decision from the start, not a surprise after launch.

Algolia and similar managed services cost more to run but less to set up and almost nothing to maintain. The right choice depends less on the dollar figures and more on what your team's time is actually worth.

Managed search makes sense when your engineering team is small and search is not a core differentiator. If you are a two-person startup building a marketplace and search is just a way to help users find listings, not a feature that makes your product meaningfully better than alternatives, paying $800/month to not think about it is probably the right call. A 2022 Stripe report found that early-stage startups spend an average of 30% of engineering time on infrastructure and tooling rather than product features. Every hour spent tuning a self-hosted search engine is an hour not spent on the thing that makes your product worth finding.

Self-hosted search makes sense at scale or when search is genuinely central to what you do. A job board, a content platform, or a product catalog with millions of SKUs will outgrow Algolia's free and starter tiers quickly. At $3,000–$5,000/month in Algolia fees, a self-hosted setup that costs $400/month and $8,000 to build pays back its build cost in under three months.

There is a middle path that most AI-native teams recommend for early-stage products: start with Algolia for launch speed, build with clean enough code that switching to self-hosted Elasticsearch later is a two-week project rather than a rewrite. That costs slightly more up front in engineering discipline but avoids the trap of being locked into a pricing tier you cannot afford once you have real traffic.

Timespade has built search into marketplaces, SaaS products, and content platforms across the full range, basic keyword search to full Elasticsearch integrations with custom ranking logic. The total cost for a production-ready search feature, including filters, autocomplete, and relevance tuning, runs $8,000–$14,000 at AI-native team rates. A Western agency doing the same scope typically quotes $28,000–$45,000. The search itself is identical. The legacy tax is not.

If you want to scope your app's search requirements and get a build estimate, book a discovery call here.