Luxury Product Search
Global product search across luxury goods catalog with multi-language support, typo tolerance for brand names and product lines, and sub-50ms response times for the premium shopping experience.
Documentation Search
Technical documentation search across the Supabase developer platform, handling code-aware queries, multi-project search, and integration with their open-source ecosystem.
Marketing Platform Search
Search infrastructure for email marketing platform covering campaigns, templates, and contact lists. Self-hosted deployment for data sovereignty with integration into their SaaS application stack.
Marketplace Product Discovery
E-commerce search across millions of marketplace listings with real-time indexing as sellers update products, synonym handling for product categories, and personalized ranking.
What Meilisearch Developers Actually Build
Before writing your job description, understand what Meilisearch work looks like in production. Here are real examples from companies using it:
E-commerce & Product Discovery
Louis Vuitton uses Meilisearch to power search across their luxury product catalog. Their implementation handles:
- Instant search with sub-50ms response times across thousands of luxury products
- Multi-language support for global storefronts (French, English, Japanese, Chinese)
- Typo tolerance that helps users find "Capucines" even when they type "capuceens"
- Faceted filtering by price, collection, and product category
Rakuten France relies on Meilisearch for their marketplace:
- Product search across millions of listings
- Real-time indexing as sellers add and update products
- Synonym handling for fashion and electronics terminology
- Personalized result ranking based on seller reputation
Developer Tools & Documentation
Supabase uses Meilisearch for their documentation search:
- Searching across thousands of technical documents
- Code-aware search that handles programming terms and syntax
- Multi-project search across their growing ecosystem
- Integration with their open-source developer platform
Docusaurus (Meta's documentation framework) offers Meilisearch integration:
- Static site search without external dependencies
- Self-hosted alternative to Algolia DocSearch
- Community-friendly option for open-source projects
SaaS & Business Applications
Brevo (formerly Sendinblue) uses Meilisearch in their marketing platform:
- Searching across email templates, campaigns, and contacts
- Fast filtering of large contact lists for segmentation
- Integration with their broader application stack
- Self-hosted deployment for data sovereignty
Meili (the company behind Meilisearch) powers their own cloud offering:
- Multi-tenant search infrastructure
- Geographic distribution for global latency
- Automatic scaling and high availability
Meilisearch vs The Alternatives: A Recruiter's Guide
meilisearchUnderstanding where Meilisearch fits helps you evaluate candidates and write better job descriptions.
Meilisearch vs Elasticsearch
| Aspect | Meilisearch | Elasticsearch |
|---|---|---|
| Setup time | Minutes | Hours to days |
| Learning curve | Gentle | Steep |
| Configuration | Minimal, opinionated | Extensive, flexible |
| Typo tolerance | Built-in, excellent | Requires configuration |
| Analytics/aggregations | Limited | Comprehensive |
| Scale (proven) | Millions of documents | Billions of documents |
| Use case sweet spot | Product search, docs | Complex analytics, logs |
When candidates mention Elasticsearch experience: They'll find Meilisearch simpler. The concepts transfer, but they may initially over-engineer solutions. Meilisearch's opinionated defaults handle most cases.
When your job needs Elasticsearch: If you need complex aggregations, the ELK stack for logging, or proven billion-document scale, Meilisearch isn't the right tool. Don't try to force it.
Meilisearch vs Algolia
| Aspect | Meilisearch | Algolia |
|---|---|---|
| Hosting | Self-hosted or Meilisearch Cloud | Managed only |
| Pricing | Infrastructure cost or usage-based | Usage-based (expensive at scale) |
| Data control | Full ownership | Third-party |
| Speed | Excellent (<50ms) | Excellent (<50ms) |
| Developer experience | Excellent | Excellent |
| Enterprise features | Growing | Mature |
Why companies switch from Algolia to Meilisearch: Cost (Algolia's pricing grows quickly), data sovereignty requirements, or desire for self-hosting. Candidates with Algolia experience will feel at home with Meilisearch—the concepts are nearly identical.
When Algolia is better: Enterprise features, AI recommendations, extensive analytics, or when you need zero-ops managed infrastructure. Meilisearch Cloud is catching up but isn't as mature.
Skills by Level: What to Expect
Junior Search Developer (0-2 years)
What they can do:
- Set up a Meilisearch instance and configure basic indexes
- Write queries using the search API
- Integrate search into a frontend application
- Configure basic settings (searchable attributes, displayed attributes)
- Debug common issues (document format, index configuration)
What they'll need help with:
- Optimizing relevance for specific use cases
- Designing index schemas for complex data
- Production deployment and scaling
- Advanced features (synonyms, stop words, ranking rules)
Mid-Level Search Developer (2-4 years)
What they can do:
- Design index schemas optimized for search patterns
- Configure custom ranking rules for business requirements
- Implement faceted search and filtering
- Set up multi-index search for complex applications
- Troubleshoot performance issues
- Handle data synchronization between primary database and Meilisearch
What distinguishes them:
- Understanding of when Meilisearch is (and isn't) the right tool
- Experience with production deployment and monitoring
- Ability to tune relevance based on user feedback
Senior Search Developer (4+ years)
What they can do:
- Architect search systems for high availability and scale
- Make technology decisions (Meilisearch vs Elasticsearch vs Algolia)
- Design data pipelines for real-time index updates
- Implement search analytics and A/B testing for relevance
- Mentor team on search best practices
- Optimize infrastructure costs for search workloads
What distinguishes them:
- Experience with search at scale (not necessarily Meilisearch specifically)
- Deep understanding of relevance and ranking algorithms
- Ability to translate business requirements into search architecture
The Modern Meilisearch Developer (2024-2026)
Meilisearch has evolved rapidly. Modern expertise includes understanding these recent developments:
Meilisearch Cloud
Meilisearch now offers a managed cloud service, reducing operational overhead:
- Automatic scaling and updates
- Global edge deployment for low latency
- Built-in monitoring and analytics
- Eliminates self-hosting complexity
Candidates don't need self-hosting experience if you use Meilisearch Cloud, but understanding deployment options shows architectural awareness.
AI-Powered Features
Recent versions include semantic and hybrid search:
- Vector search for similarity matching
- Hybrid search combining keyword and semantic
- Integration with embedding models
- Foundation for AI-powered recommendations
This is cutting-edge—don't require it, but it's a strong signal if candidates mention it.
Multi-Tenancy
For SaaS applications, multi-tenant patterns are essential:
- Tenant isolation strategies
- Index-per-tenant vs filtered single-index approaches
- API key management for tenant security
Senior candidates should discuss these tradeoffs even if they haven't used Meilisearch specifically.
Federation and Distributed Search
For larger deployments:
- Search across multiple indexes simultaneously
- Geographic distribution for global applications
- High availability configurations
Recruiter's Cheat Sheet: Spotting Great Candidates
Resume Signals That Matter
✅ Strong indicators:
- Search experience with any engine (Elasticsearch, Algolia, Solr, Typesense)
- Backend development with API integration experience
- Mentions of relevance tuning or search quality improvement
- Experience with data synchronization patterns
- Production deployment experience
🚫 Be skeptical of:
- "Meilisearch expert" without broader search context (it's not complex enough to specialize in)
- Only tutorial-level projects
- No backend development experience
- Listing Meilisearch among 30 technologies with no depth
Conversation Starters That Reveal Skill
| Question | Junior Answer | Senior Answer |
|---|---|---|
| "Why choose Meilisearch over Elasticsearch?" | "It's simpler" | "For our product search use case, we don't need Elasticsearch's aggregations. Meilisearch gives us typo tolerance, instant search, and simpler ops—all we need. But for our logging, we'd use something else." |
| "How do you keep search in sync with your database?" | "We update when records change" | "We use event-driven updates via webhooks. For bulk changes, we queue updates and batch index. We track indexing lag as a metric and have retry logic for failures." |
| "Your search results aren't relevant. How do you fix this?" | "Adjust the settings" | "First I'd analyze what users are searching for and what they expect. Then I'd check ranking rules, consider synonyms for terminology mismatches, and potentially adjust searchable attributes. Relevance is iterative—you measure, change, measure again." |
GitHub/Portfolio Signals
Positive indicators:
- Contributions to Meilisearch SDKs or documentation
- Projects showing data pipeline integration
- Blog posts comparing search engines
- Evidence of production search implementations
Common Hiring Mistakes
1. Requiring Meilisearch Specifically
Meilisearch is learnable in a day or two. Requiring it specifically eliminates candidates with Algolia, Elasticsearch, or Typesense experience who could be productive immediately. Search concepts transfer—index structure, relevance, query optimization.
Better approach: Require "search engine experience" or "full-text search implementation." Accept any search technology.
2. Overcomplicating the Role
Meilisearch is intentionally simple. If your job description reads like an Elasticsearch role (cluster management, complex aggregations, distributed systems expertise), candidates will wonder if you've chosen the right tool.
Better approach: Focus on the real work: API integration, relevance tuning, data synchronization. That's 90% of Meilisearch work.
3. Ignoring Backend Fundamentals
Meilisearch integration is backend work. A candidate who "knows Meilisearch" but struggles with APIs, database design, or deployment pipelines won't succeed. The search engine is the easy part.
Better approach: Prioritize backend fundamentals. Someone strong in backend development can learn Meilisearch in their first week.
4. Testing for Documentation Recall
Meilisearch's API is well-documented and simple. Asking "What's the endpoint to add documents?" tests nothing useful. Real work involves understanding when to use search, how to structure data, and how to measure success.
Better approach: Present scenarios: "Users search for 'Nike running shoes' but find casual shoes. How would you investigate and improve this?"
5. Expecting Enterprise Search Experience
Meilisearch is often chosen by smaller teams seeking simplicity. Candidates may not have massive-scale experience, and that's fine if your use case is product search for 100K items. Match expectations to reality.
Better approach: Clarify your scale in the job posting. "Search across 50K products" tells candidates what they're working with.