Use Cases

Build and scale Elasticsearch and OpenSearch search that's fast, relevant, and doesn't fall over under load:

• Full-text search across apps, docs, and catalogs
• Faceted search, filters, and relevance tuning
• Autocomplete, suggestions, did-you-mean
• Index design and mappings that match how the data gets searched
• Multi-tenant and multi-index search architectures

E-commerce, content platforms, internal tools — search that users actually trust.

Centralized logs, metrics, and traces — with cost control baked in, not bolted on later:

• Log aggregation, retention, and analysis
• APM, RUM, and application performance visibility
• Alerting, anomaly detection, and on-call workflows that don't burn people out
• Cutting ingestion and storage costs without losing the signal you actually need
• Drawing the boundaries between Elastic, OpenSearch, Prometheus, Grafana, Datadog, Fluent Bit, and OpenTelemetry

Observability that's still maintainable a year from now, not just a year from when it shipped.

Most deep Elasticsearch and OpenSearch problems start with SQL-shaped thinking, mappings nobody owns, and indexes that no longer match how the system gets queried:

• Mapping explosion from accidental dynamic fields
• Field type mistakes that make search or aggregation expensive
• Oversharding, undersharding, and index sprawl
• Template drift across environments and versions
• Data modeling that needs denormalized, query-shaped documents — not relational normalization

The fix is almost always schema and query shape, not more hardware.

Lock the cluster down without making it unusable:

• Authentication (native, SAML, OIDC, LDAP)
• Role-based access control and field-level security
• Encryption in transit and at rest
• Audit logging and compliance (SOC2, HIPAA, etc.)
• Hardening and least-privilege design

Compliance requirements met without the security model becoming the new operational nightmare.

Version upgrades and platform migrations done without downtime surprises:

• Elasticsearch version upgrades (7.x → 8.x, 8.x → 9.x) with minimal downtime
• Elastic ↔ OpenSearch migration assessment and execution
• Amazon OpenSearch Service planning and honest managed-service tradeoffs
• Cluster-to-cluster and cross-cloud migrations
• Reindexing strategies and data validation
• Post-migration tuning

We have done a lot of these without causing an outage.

Clusters that grow without degrading:

• Query and aggregation optimization
• Shard sizing, index design, and capacity planning
• JVM, thread pools, and resource tuning
• Finding the actual bottleneck — not just the obvious one
• Horizontal vs vertical scaling decisions, made honestly

We design for the workload you have today and the one you'll have in 12–24 months.

Cut spend without losing capability:

• Right-sizing nodes and storage
• Cutting log and metric ingestion (sampling, filtering, retention)
• High-cardinality and duplicate log reduction
• Storage tiers and lifecycle policies (ILM)
• The honest tradeoff: visibility vs. cost

Most cost audits we run pay for themselves inside a billing cycle or two.