Cost to build enterprise search software

Custom enterprise search software costs $40,000-$80,000 for a departmental V1 and $80,000-$200,000+ for a full platform with document-level security, federated connectors, and an AI answer layer. The range is wide because the biggest cost driver is not document volume. It is permissions complexity. An organization with 400 nested Active Directory groups and documents spread across SharePoint, Confluence, and a legacy Documentum system is a fundamentally different build from a single team indexing a shared Google Drive.

This guide is for knowledge management teams, IT directors, and operations leaders at mid-market companies who are considering custom enterprise search. It covers what a realistic V1 includes, where managed tools break down, and what the math looks like across tiers at RaftLabs' published rate of $35-$40/hr.

What is enterprise search software, and why do organizations build it?

Enterprise search is the infrastructure that lets employees find documents, data, and institutional knowledge across all of a company's systems from a single interface. The business problem it solves is concrete. According to McKinsey Global Institute research, the average knowledge worker spends nearly 20% of their workweek searching for internal information or tracking down colleagues who can help. Agility Portal's enterprise search survey puts the daily figure at 1.8 hours -- roughly 9.3 hours per week. IDC data places the figure even higher: knowledge workers spend approximately 30% of their workday on information retrieval activities, including searching, asking colleagues, waiting for answers, and verifying whether what they found is current.

At a company with 1,000 knowledge workers earning a median salary of $80,000, that time loss represents over $5 million in wasted salary annually. Mordor Intelligence's enterprise search market report values the global enterprise search market at USD 6.83 billion in 2025, growing at a CAGR of 10.30% to reach USD 11.15 billion by 2030 -- a market expanding in direct response to this productivity problem.

Three requirements drive most custom builds: document-level security that mirrors an existing Active Directory hierarchy, relevance tuning by non-technical admins, and federated search across sources that no single SaaS product indexes cleanly. Off-the-shelf tools handle all three badly.

Who commissions custom enterprise search builds?

Most organizations that build custom enterprise search fall into one of four scenarios.

The first is a professional services or legal firm where documents carry matter-level confidentiality. A lawyer in the corporate M&A group should not see documents from the litigation group working on the other side of a transaction. Active Directory has those boundaries encoded in group memberships. The managed search products do not enforce them cleanly at query time without substantial custom middleware.

The second is a mid-market company whose intranet search (SharePoint default, Google Cloud Search) returns irrelevant results even for exact-phrase queries. The operations or KM team has tried tuning relevance settings, but the controls are too coarse. Promoting a policy document or demoting outdated content requires either developer involvement or a paid upgrade to an enterprise tier.

The third is an IT director at a healthcare network or financial services firm where RAG-based AI assistants are being evaluated. Before an AI assistant can be trusted to answer questions from internal documents, the retrieval layer needs to respect document-level permissions at query time. If the search index does not enforce who can see what, an AI answer might pull from a document the user has no access to. A compliance problem, not just a UX one.

The fourth is a company whose documents live in more than three systems with incompatible export formats. SharePoint, Confluence, Salesforce Knowledge, a file share, and a legacy Documentum deployment together in one organization is a common setup. No single managed product indexes all of them without connectors the vendor either does not offer or charges significantly for.

Core features: what V1, V2, and V3 each include

The features in enterprise search break into three tiers. Each tier is buildable as a standalone release. Most organizations get real value from V1 and treat V2 and V3 as follow-on investments after the core search loop is in production.

V1: departmental search ($40,000-$80,000, 10-16 weeks)

A V1 covers the core search loop for a single team or department, typically 500-5,000 documents from 1-2 sources.

The backend search engine is Elasticsearch 8.x or Typesense, chosen based on the document schema and expected query patterns. Elasticsearch handles unstructured documents with variable fields well. Typesense is faster to operate for well-defined schemas and smaller document sets. The document ingestion pipeline accepts PDF, DOCX, and web pages, parsed via Apache Tika or Unstructured.io for content extraction.

Relevance uses BM25 scoring out of the box. BM25 is the industry standard for keyword retrieval and performs well without custom tuning for most V1 deployments.

Access control at V1 is role-based: user groups from the identity provider map to document sets. A user in the "Marketing" group sees marketing documents. A user in "Finance" sees finance documents. This covers most departmental use cases without the complexity of document-level security.

The search UI is built on React or Next.js with InstantSearch.js components: a search input, result cards with matching snippets, pagination, and basic filtering by date range, document type, and author. The admin panel provides index management, a daily query volume chart, and a top-queries list.

Cost range at RaftLabs rates: $40,000-$80,000 for a team of 3-4 over 10-16 weeks. Each additional data source connector at V1 adds $8,000-$15,000 and 2-3 weeks.

V2: full platform ($80,000-$140,000, 16-26 weeks)

V2 covers the capabilities that organizations outgrow role-based access for and that require a relevance tuning interface non-technical admins can use.

Document-level security moves permissions enforcement from the application layer to the search index. At query time, the search engine filters results based on the requesting user's identity, not just their group membership. This requires either Elasticsearch's document-level security (DLS) feature, which is a Platinum-tier Elasticsearch feature on Elastic Cloud, or custom middleware that maintains a per-document access control list and applies it as a query-time filter. The custom middleware approach is more complex to build but avoids the Elastic Cloud subscription cost.

Federated search connects 3-4 additional data sources: SharePoint, Confluence, Slack, and file shares are the most common combination. Each connector requires a service account, API access, scheduled crawling, and delta indexing (re-indexing only changed documents). Federated search connectors account for a significant share of V2 cost because every source has its own API behavior, rate limits, and change-detection mechanism. The ingestion and transformation layer for each source is a data engineering project in its own right.

The relevance tuning dashboard gives KM admins a UI to manage synonym dictionaries, apply document boosting rules (promote SOPs written after a certain date, demote archived versions), and configure result pinning for high-priority queries. This removes developer involvement from the most common day-to-day relevance tasks.

Semantic search adds a vector layer using sentence-transformers (self-hosted) or the OpenAI embeddings API. Vector embeddings let the search engine retrieve documents by meaning, not just keyword match. A query for "employee onboarding process" returns the relevant HR guide even if it uses the phrase "new hire orientation" throughout. The vector store sits in pgvector, Pinecone, or Weaviate, depending on document corpus size and query latency requirements.

Faceted navigation, user personalization based on query history and department, and audit logging for compliance round out V2. Audit logging is non-negotiable for regulated industries: who searched for what, when, and which result they clicked is a compliance artifact in healthcare, finance, and government.

V3: enterprise-grade ($140,000-$200,000+, 26-40 weeks)

V3 features are relevant for organizations deploying search as a mission-critical system across thousands of employees, or where AI-generated answers need to be auditable.

The RAG (Retrieval-Augmented Generation) layer sits on top of the V2 search infrastructure. When a user submits a query, the system retrieves the most relevant documents (permissions-filtered), passes them as context to an LLM (GPT-4o or Claude via API), and returns a generated answer with source citations. The citations are critical: a RAG answer without document-level citations is a compliance risk in any regulated industry.

Learning-to-rank models train on behavioral click data to improve relevance over time. When users consistently click result 3 instead of result 1 for a query, the LTR model learns that result 3 is actually more relevant for that query pattern and adjusts future rankings accordingly.

Multi-language support and cross-language retrieval matter for organizations with documentation in English, German, Spanish, and other languages. A query in English can retrieve relevant German-language documents when the embedding models support cross-lingual similarity.

High-availability architecture handles multi-node Elasticsearch clusters, zero-downtime rolling index updates, and multi-region deployment for global organizations. SSO integration (SAML 2.0, OIDC, Active Directory/LDAP) and field-level security (restricting access to specific fields within a document, not just the document itself) complete the enterprise security posture.

What it costs: the full breakdown

These figures use RaftLabs' published rate of $35-$40/hr. US or Western European agencies billing at $150-$250/hr would quote 3-5x these numbers.

What the numbers do not include

Each enterprise data source connector beyond the first adds $8,000-$20,000 and 2-4 weeks. SharePoint with nested folder permissions at the upper end; a clean REST API with simple authentication at the lower end. Annual maintenance runs 15-20% of the initial build cost, so a $120,000 platform costs $18,000-$24,000 per year to maintain, update, and keep indexed. Compliance certifications (SOC 2, FedRAMP, HIPAA technical safeguards) add $20,000-$50,000 in engineering work, separate from the audit and certification fees. Infrastructure (AWS OpenSearch Service or self-hosted Elasticsearch on EC2, plus S3 for document storage) runs $500-$3,000/month depending on document volume and query load.

The failure mode we see most often in enterprise search projects: the permissions model is scoped as a V1 task without auditing how many Active Directory groups exist and how they nest. A company with 400 AD groups has spent years encoding their document access policy in that hierarchy. Flattening that into search index filters is a 4-8 week engineering task on its own. Teams that plan for it upfront save $30,000-$60,000 in scope changes.

Build vs. buy: where the managed tools break down

The right answer is managed-first for most organizations. If your documents are public or lightly permissioned, your schema is stable, and you are indexing from 1-2 sources, the managed tools work well. The math changes when you need document-level security that respects a real AD hierarchy, when you need federated search across more than 2-3 heterogeneous sources, or when non-technical admins need relevance control without writing query DSL.

Elasticsearch Service (Elastic Cloud)

Elastic Cloud pricing starts at $95/month for the Standard tier. Production clusters with adequate storage and replication typically run $2,000-$10,000+/month depending on node count and data retention.

The critical limitation for enterprise buyers: document-level security requires the Platinum tier ($125+/month per node, before cluster sizing). Without DLS, Elasticsearch stores all documents in a shared index and cannot enforce per-document access at query time. Organizations with complex permission models either pay the Platinum premium or build a custom DLS layer using filtered aliases and per-user query filters, which is significant custom engineering.

The second issue is relevance tuning. Elasticsearch's query DSL is powerful but not admin-friendly. KM admins who need to promote certain documents or manage synonym libraries need either a developer or a paid Elastic Enterprise Search subscription on top of the base cluster cost.

Custom is right when: you need DLS but want to avoid Platinum pricing, you need a polished branded search UI that Elastic does not ship, or you need connectors to non-standard enterprise sources.

Algolia

Algolia's Grow plan charges $0.50 per 1,000 search requests and $0.40 per 1,000 records/month. At enterprise document volumes, that adds up. The Elevate plan (NeuralSearch) requires an annual contract with custom pricing.

Algolia has no self-hosting option. All data must reside on Algolia's cloud infrastructure. For organizations with data sovereignty requirements, on-premise deployments, or air-gapped environments, Algolia is a hard no.

Document-level permissions in Algolia use secured API keys with embedded search filters. The model works for simple permission tiers. It breaks down when permissions derive from nested AD group memberships: a user's effective document access depends on which groups they belong to, which groups those groups belong to, and which groups have been explicitly denied. Mapping that hierarchy into Algolia filters requires custom middleware that re-computes permissions each time group memberships are updated. Organizations doing that work are most of the way to a custom build.

Semantic search (NeuralSearch) is locked to the Elevate plan. Mid-market teams who want hybrid keyword and vector retrieval face a significant pricing jump.

Custom is right when: you have data sovereignty requirements, your permission model derives from nested AD groups, or you need semantic search without committing to Algolia's enterprise contract.

Typesense Cloud

Typesense starts at approximately $40/month for small clusters. Its RAM-based architecture means costs grow directly with document volume. A corpus of millions of legal, HR, or technical documents with rich metadata can require 64-256 GB RAM nodes, which prices the managed service out of the budget range for the document volumes that typically prompt a custom build.

Typesense lacks mature enterprise audit logging, SIEM integrations, and cross-cluster replication. Those capabilities are expected in regulated industries. Document-level RBAC for complex nested groups requires the same custom middleware workaround as Algolia.

Custom is right when: your document corpus is large (millions of documents with rich metadata), you need enterprise audit logging for compliance, or you need cross-cluster replication.

Microsoft Azure AI Search

Azure AI Search pricing runs from $73.73/month (Basic) to $1,962.24/month (Standard S3). AI enrichment (OCR, entity extraction) is billed separately through Azure Cognitive Services.

Document-level permissions in Azure AI Search are still in preview as of mid-2026. Native DLS works only with Azure Data Lake Storage Gen2, Azure Blob Storage, and SharePoint. Organizations with documents in non-Azure sources (AWS S3, Confluence, on-premise file shares) must build custom permission-push pipelines, which negates most of the managed-service benefit.

Azure AI Search is also tightly coupled to the Azure platform. Organizations not already running on Azure face integration overhead connecting their existing document stores.

Custom is right when: your documents are not in Azure-native storage, you need reliable (not preview) document-level security, or your tech stack is not Azure-first.

How long does it take?

Each legacy enterprise connector (Documentum, SAP, proprietary ECM) adds 2-4 weeks above the standard connector estimate. Compliance work (SOC 2 readiness, FedRAMP controls) adds 4-8 weeks. A team of 3-4 people at V1 scope; 4-5 at V2; 5-7 at V3.

How RaftLabs builds enterprise search

We start with a permissions audit, not a feature list. In our experience, the permissions model is where enterprise search projects go wrong. Most scoping conversations focus on which sources to index and what the UI should look like. The conversation that determines cost is: how many AD groups exist, how do they nest, and what is the document-level access policy?

Once the permissions architecture is clear, we pick the search engine. Elasticsearch for permission-heavy builds with complex document schemas and high query volumes. Typesense for well-defined schemas where operational simplicity matters more than schema flexibility.

We scope connectors conservatively. A connector to a modern REST API with clean authentication takes 1-2 weeks. A connector to a SharePoint tenant with nested folder ACLs, version history, and custom content types takes 3-5 weeks. Those differences are not visible in a feature list. In our experience they account for 30-40% of the final project timeline.

V1 ships in 10-16 weeks. We put it in front of real users before building V2. Query volume data and click-through behavior from real production traffic tells us more about where to invest in relevance tuning than any pre-launch assumption.

For organizations evaluating a RAG layer, we build the permissions-aware retrieval foundation first. An AI assistant that answers questions from documents the user cannot access is a compliance liability, not a productivity tool. The retrieval layer has to be trustworthy before the generative layer is useful.

We have built search and knowledge management platforms for clients in the US, UK, Australia, Ireland, and Canada. Our custom software development practice covers the full stack from permissions architecture through the search UI. If you want to know what a custom enterprise search build would cost for your specific document repositories and permission model, book a 30-min call. We scope within a week of that call.

The organizations that get the most value from custom enterprise search are the ones that scope the permissions model correctly in the first conversation. The ones that underscope it discover, three months into the build, that flattening 400 AD groups into a search index filter is its own 6-week project. The question to ask before you start is not "how many documents do we have." It is "how many distinct permission boundaries do those documents live behind."