We design, tune, and operate Amazon OpenSearch Service clusters — sized for the query patterns you actually have, and priced so the bill stops growing faster than the data.
OpenSearch is straightforward to launch and unforgiving at scale. Shard counts chosen early become expensive later, hot nodes get sized for peak and paid for continuously, and ingestion pipelines built for one workload buckle when a second one lands on them.
We treat cluster design as a capacity problem rather than a defaults problem: index and shard strategy built around real query patterns, tiered storage so cold data stops paying hot-node prices, and ingestion that degrades predictably instead of falling over.
Index, shard, and node strategy built from your query and retention patterns — with headroom that is deliberate rather than accidental.
Moves from self-managed Elasticsearch or legacy OpenSearch domains, including version upgrades planned around your indexing windows.
UltraWarm and cold tiering, reserved capacity planning, and query-side work to cut the spend that comes from over-provisioning for peak.
Most OpenSearch problems arrive through the ingestion path. We build pipelines — OpenSearch Ingestion, Kinesis Data Firehose, or Lambda-based, depending on what fits — with buffering and backpressure designed in, so a burst in one data source does not take down search for everything else. Index lifecycle policies get defined up front, because retention decided later is retention that never happens.
A cluster diagram does not keep a cluster healthy. We instrument the things that predict trouble — shard skew, JVM memory pressure, queue depth, slow-query patterns — and set alerting thresholds against your workload rather than generic defaults. When OpenSearch is part of a managed engagement, that monitoring runs under the same operations model as the rest of your AWS environment.
Fine-grained access control, encryption in transit and at rest, VPC-only access, and audit logging that satisfies the control set you are held to. For GovCon and Life Sciences workloads, cluster configuration is captured as evidence alongside the rest of the environment rather than documented separately.
Workload discovery, migration planning and sequencing, database and data movement, and cutover with validation.
Multi-account AWS environments for federal, state, and local work, designed against FedRAMP, NIST, and CMMC control sets with evidence collection built in from the start.
Infrastructure as code, continuous compliance monitoring, and automated qualification evidence for regulated Life Sciences workloads on AWS.
We'll review your cluster configuration, index strategy, and spend, and show you what can be recovered without a redesign.
Request a Cluster Review