Building log search looks easy. It isn't.

Most engineering teams don't set out to build enterprise log analytics. But one feature request leads to another, and suddenly you're maintaining an entire distributed system.

The Nine Hidden Complexities of DIY Log Search

What teams discover after committing to build their own

1. Systems Complexity

Log search isn't just search. It's distributed systems, information retrieval, data pipelines, relevance science, security, observability, and cost engineering—all at once.

2. Indexing Challenges

Continuous ingestion from multiple sources. Schema drift. Corrupted data reprocessing. Re-indexing without downtime. Each is a project unto itself.

3. Relevance is Research

Tuning ranking functions, boosting logic, and weighting never ends. You'll need human-labeled datasets and A/B testing infrastructure.

4. Non-Linear Scaling

Index growth outpaces data growth. Memory pressure increases unpredictably. Clusters require constant over-provisioning.

5. High Availability Demands

Replica strategies. Cross-zone resilience. Failover logic. Backward-compatible formats. Each failure mode needs handling.

6. Security Multiplication

Per-user permissions. Per-document ACLs. Field-level security. Audit logging. Compliance certification. Security is never "done."

7. Team Consumption

Search demands continuous attention: cluster tuning, memory incidents, slow query debugging, upgrade testing. It becomes someone's full-time job.

8. Scale Amplification

As log volume grows, every problem gets worse. What worked at 100GB/day fails at 1TB/day. Architecture decisions compound.

9. Opportunity Cost

Senior engineers maintaining search can't focus on product differentiation. Your best people become infrastructure operators.

The DIY Journey

How "simple log search" becomes a multi-year commitment

📦
Month 1
"Let's just spin up Elasticsearch. How hard can it be?"
🔧
Month 3
"We need to tune the heap. And add more shards. And fix the mapping."
🚨
Month 6
"The cluster went red at 3 AM. We need dedicated on-call for search."
💸
Month 12
"We have two full-time engineers just keeping search running."

The True Cost of DIY

What you're really paying for

Hidden DIY Costs
2 Senior Engineers (salary + benefits) $400k/year
Infrastructure (compute, storage, network) $150k/year
On-call burden (burnout, turnover) $50k/year
Opportunity cost (features not built) Priceless
Total Annual Cost $600k+
KalDB Alternative
S3 Storage (10TB/month) $3k/year
Query Compute (on-demand) $12k/year
Managed Support (optional) $20k/year
Engineering time (minimal) $0
Total Annual Cost $35k
Annual Savings: $565k+

What You Actually Need

KalDB provides the complete solution

Ingestion Pipeline

OpenSearch Bulk API compatible. Works with Logstash, Fluent Bit, and any existing pipeline.

Lucene Search

Full-text search with the same Lucene engine. Sub-second queries at petabyte scale.

S3 Durability

99.999999999% durability. No data loss. Unlimited retention at $0.023/GB.

Auto-Scaling

Query compute scales independently. No capacity planning. No over-provisioning.

Grafana Compatible

Use your existing dashboards. OpenSearch data source works out of the box.

Production-Ready

Battle-tested at Slack. No need to learn from your own failures.

Focus on What Matters

Your engineers should be building product features, not maintaining log infrastructure. KalDB handles the hard parts so you can focus on what differentiates your business.

Time to deploy KalDB:
5 minutes
vs. months for DIY

Ready to Make the Switch?

Try KalDB open source today or talk to us about production deployment

# Get started in minutes
git clone https://github.com/slackhq/kaldb
cd kaldb && docker-compose up
✓ Running on localhost:8080