Your AI is only as good as your search, and Elasticsearch is holding you back.
Lucenia gives you the power of Elasticsearch without the operational nightmare — so your AI can reason over all your data, not just what you can afford to index.
Trusted by platform teams who need search that scales with AI
What engineers are actually saying about Elasticsearch
Elasticsearch often breaks in production in ways that cost you engineering time, customer trust, and money. Below are the most common, proven problems people report.
Constant heap tuning and JVM optimization
Teams spend days tuning JVM settings, only to have production behave differently than staging.
View discussionShard management nightmares at scale
Too many shards kill performance. Too few limit scalability. Finding the right balance is a full-time job.
View discussionIndex lifecycle policies that break silently
ILM policies fail without warning, leaving stale indices consuming resources and budget.
View discussionMemory pressure alerts at 3 AM
Circuit breakers trip during peak traffic, causing cascading failures across your cluster.
View discussionCluster state synchronization failures
Master node elections during high load cause cluster-wide indexing pauses and query timeouts.
View discussionSplit-brain scenarios during network partitions
Network issues lead to multiple masters, data corruption, and manual intervention requirements.
View discussionTroubleshooting & visibility gaps
When issues occur, debugging Elasticsearch requires deep expertise and often external consultants.
View discussionEven Their Paid Self-Hosted Offering Won't Save You
Running Elastic Cloud Enterprise (ECE) or Elastic Cloud on Kubernetes (ECK)? These tools handle orchestration — spinning up nodes, rolling restarts, coordinating upgrades across a cluster. But every operational decision remains yours: JVM heap sizing, shard allocation strategy, index lifecycle policies, capacity planning, and monitoring for cluster health. When your nodes hit memory pressure at 3 AM, you're the one remediating it. Elastic's own shared responsibility model makes this clear: "Customers are responsible for monitoring, alerting, and remediating cluster issues."
Additional reported issues
Direct from Elastic Discuss Forum
These are real excerpts from community discussions — click to view the full threads.
"JVM heap size issue. Elasticsearch stops sometimes due to this error... CircuitBreakingException: Data too large"
— Elastic Discuss Forum"ILM is failing to delete closed indices... retry attempt [20077]"
— Elastic Discuss Forum"Cluster red, unassigned shards, no response on writes... tried enlarging heap, restarting many times, with no success"
— Elastic Discuss Forum"When relocating 3 or more shards, ES is overloaded, REST API is very slow, Kibana and es-exporter cannot reach ES"
— Elastic Discuss ForumThe Architecture That Holds Everyone Back
Elasticsearch was built as a fast text search engine. Over time, teams layered on analytics, logging, observability, security, vectors, ML, RAG, semantic search, and hybrid pipelines. What you're left with today is a system doing far more than it was ever designed for.
Built for text search, forced into analytics
Elasticsearch was designed to index and search text documents. The analytics, aggregations, and dashboards were bolted on later — never core to the architecture.
View sourceJVM architecture was a 2010 decision
The choice of Java and the JVM made sense for a search library. For a distributed data platform handling billions of events? It creates hard ceilings and operational nightmares.
View sourceEvery new feature adds complexity, not capability
Vectors, ML, security, observability — each addition increases the surface area for bugs, conflicts, and performance degradation. The architecture wasn't designed to absorb them.
View sourceNo clean separation between storage and compute
Modern architectures separate these concerns for independent scaling. Elasticsearch couples them tightly, limiting flexibility and driving up infrastructure costs.
View sourceArchitectural mismatch for AI workloads
Built for keyword + structured search first. Vector and hybrid search were bolted on later — not optimized for feeding everything to AI and reasoning over it.
View sourceTechnical debt compounds with every release
Each version patches old problems while introducing new ones. The codebase has grown so complex that even the original maintainers have moved on.
View source"Elasticsearch — You know, for search"
"Elastic — You know, for cybersecurity, observability, analytics, AI, vector, and search"
Search went from being the product to being an afterthought. Every new feature adds complexity, slows innovation on the core, and creates attack surfaces you never asked for. Most teams use Elasticsearch for search — yet they're forced to pay for (and secure) capabilities they'll never touch.
From Elasticsearch Pain to Lucenia Gain
Every Elasticsearch headache has a Lucenia solution. Here's how we address the seven biggest pain points.
JVM heap tuning
- No JVM — native performance without memory ceilings
- Predictable resource usage at any scale
Stop tuning, start building
Shard management complexity
- Autoscaling that adapts to your workload automatically
- 7x less infrastructure to manage
Focus on your product, not your search cluster
Silent ILM failures
- Simplified operations with fewer moving parts
- Cross-platform deployment in just a few clicks
Reliable operations without the complexity
3 AM memory alerts
- 99.999% uptime with resilient architecture
- Built to handle peak traffic gracefully
Sleep through the night with confidence
Cluster synchronization issues
- AI-native architecture for modern workloads
- Designed for vectors, hybrid search, and RAG from the start
Future-proof search that grows with AI
Split-brain scenarios
- 3-5x faster query performance
- Consistent operations even during high load
Speed and reliability without tradeoffs
Troubleshooting blind spots
- Simplified deployment and operations
- Less complexity means fewer things to debug
Debug issues in minutes, not days
Elasticsearch Problems vs Lucenia Solutions
A direct comparison of what breaks in Elasticsearch and how Lucenia fixes it.
Efficient architecture
40% lower infrastructure costs with predictable performance
Autoscaling
Automatically adapts to workload changes
Simple deployment
Cross-platform deployment in a few clicks
AI-native architecture
Built for vectors, hybrid search, and RAG from the start
Lean infrastructure
7x less infrastructure to manage
Speed + reliability
3-5x faster with 99.999% uptime
Search Infrastructure Built for the AI Era
Lucenia is what Elasticsearch would be if it were built today, for today's AI-native applications.
Built for AI Workloads
Native vector search, hybrid queries, and semantic capabilities designed for modern AI applications—not bolted on as an afterthought.
Predictable Costs
Know what you'll pay before you scale. No surprise bills when your AI features succeed and data volumes grow.
Elasticsearch Compatible
Drop-in API compatibility means your existing code works. Migration is measured in hours, not months.
Truly Open Source
Apache 2.0 license with no usage restrictions. Build without worrying about license changes or compliance audits.
Ready to Replace Elasticsearch?
Join engineering teams who have migrated to a stable, secure, and scalable search platform.