The top search options for SilverStripe in 2026

1. SilverStripe Native Search

Best for: small to medium websites with simple needs

SilverStripe includes a native search capability out of the box. It’s quick to implement and works well for basic keyword matching across page content.

Pros:

• Fast to set up
• No external infrastructure required
• Low cost

Cons:

• Limited relevance tuning
• Not suited for large or complex content sets

For smaller websites, native search can be enough. But as content grows, most organisations start to feel its limitations.

2. Apache Solr

Best for: enterprise-grade search with structured content

Apache Solr has long been a go-to search engine for enterprise SilverStripe builds. It offers powerful indexing, faceting, and filtering capabilities.

Pros:

• Highly configurable
• Strong performance at scale
• Advanced filtering and faceted search

Cons:

• Requires infrastructure and maintenance
• More complex to implement
• Less flexible for modern AI-driven experiences

Solr is still a solid choice, particularly for government and large content-heavy platforms, but it’s increasingly being challenged by newer approaches.

3. Elasticsearch

Best for: scalable, flexible, modern search experiences

Elasticsearch is one of the most popular search engines in the world and integrates well with SilverStripe.

Pros:

• Excellent scalability
• Real-time indexing
• Strong ecosystem and tooling
• Supports relevance tuning and analytics

Cons:

• Can become costly at scale

Elasticsearch strikes a balance between power and flexibility, making it a strong default choice for many modern builds.

4. AI-Powered Search Solutions

Best for: personalised, intent-driven discovery

This is where things get interesting. AI search solutions are rapidly changing how users interact with content.

Rather than relying purely on keywords, AI search understands intent, context and behaviour.

Technologies like:

• Vector search
• Semantic search
• Natural language queries

can be layered on top of platforms like Elasticsearch or used via external services.

Examples include:

• Azure Cognitive Search
• Algolia
• Custom AI layers using embeddings and large language models

Pros:

• Handles natural language queries
• Improves relevance dramatically
• Enables personalisation and recommendations

Cons:

• Higher implementation complexity
• Requires careful tuning and governance
• Cost considerations

For organisations focused on user experience, AI search is quickly becoming the new standard.

5. Hybrid Approaches (The New Best Practice)

Best for: future-proof, high-performing platforms

Increasingly, we’re seeing hybrid search architectures emerge.

This might look like:

• Elasticsearch for indexing and speed
• AI models for semantic understanding
• Behavioural data to refine results over time

This approach allows organisations to combine the reliability of traditional search with the intelligence of AI.

Choosing the Right Approach

The right solution depends on a few key factors:

• Content volume and complexity
• Performance requirements
• Budget and infrastructure
• User expectations

A simple content site might thrive with native search, while a large-scale platform with diverse audiences will benefit from an AI-powered or hybrid solution.

Where Toast Fits In

At Toast, we design and build SilverStripe platforms with search as a core experience, not an afterthought.

From implementing robust Elasticsearch solutions to layering in AI-driven search and personalisation, we help organisations turn search into a powerful tool for engagement, discovery, and conversion on your platform.

Get in touch today