Ashutosh “Ash” Kulkarni is not a new CEO in 2026. Elastic appointed him in January 2022, after one year as its chief product officer and after co-founder Shay Banon led the company. The appointment made a product-and-cloud executive responsible for turning Elasticsearch from a widely used search component into a managed platform spanning search, observability, security and artificial-intelligence workloads.
Four years of results now provide a way to judge that strategy. Elastic Cloud revenue grew faster than total company revenue in fiscal 2025, while subscription and large-customer metrics continued to expand in fiscal 2026. Those figures support a meaningful cloud shift, but they do not settle questions about usage-based costs, competition, AI spending or the future of self-managed deployments.
What happened in January 2022
Elastic promoted Kulkarni to CEO in January 2022, succeeding Banon. The board cited his success growing Elastic’s cloud business and his customer relationships. Kulkarni had joined Elastic in January 2021 as chief product officer, following senior product and management roles at McAfee, Akamai, Informatica and Sun Microsystems. Elastic’s announcement described him as a leader shaping the company’s cloud focus and its observability, security and enterprise-search businesses.
Elastic’s leadership page still lists Kulkarni as CEO as of August 18, 2026. The original announcement is available at Elastic’s CEO announcement, and his current role is listed on the leadership page.
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Why a product executive was the logical choice
The succession was more than a sales-management change. Elastic needed to make cloud easier to buy and operate while broadening the product beyond a single search and logging engine. A product leader is positioned to connect those decisions: simplify deployment, improve the user experience, package multiple workloads and decide which capabilities belong in a managed service.
That approach treats Elastic as a platform. Search can lead an account, then observability, security, vector retrieval or AI features can expand the relationship. Cloud delivery also gives Elastic a consistent way to ship upgrades, snapshots, security controls and service operations rather than leaving every customer to assemble them independently.
What “cloud front and center” means in practice
Elastic Cloud Hosted
Elastic Cloud Hosted provides managed Elasticsearch and Kibana deployments operated by Elastic. It reduces the need for customers to design clusters, apply routine upgrades and manage many infrastructure tasks, while customers still make decisions about data, access, retention and workload configuration.
Elastic Cloud Serverless
Elastic Cloud Serverless abstracts more of the deployment and infrastructure work. It can lower operational effort for teams that prefer a service-oriented experience, but it does not remove responsibility for architecture, security, retention or cost control.
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Multiple deployment choices
“Cloud first” does not mean “cloud only.” Elastic continues to support customer-operated deployments in public clouds, private clouds, hybrid environments and on premises. Its current cloud feature matrix lists AWS, Microsoft Azure and Google Cloud, along with capabilities such as zone-level high availability, private connectivity, SSO, role-based access control and marketplace billing. The matrix also lists a 99.95% monthly uptime SLA; availability and eligibility vary by plan, product, region and deployment model. See the Elastic cloud subscriptions matrix for the applicable details.
Marketplace procurement
Buying through AWS, Azure or Google Cloud marketplaces can let an enterprise use existing cloud commitments and procurement processes. That is commercially important: the service is easier to adopt when it fits the buyer’s established vendor, billing and governance model.
From hosted Elasticsearch to a multi-workload platform
Elastic’s product direction now combines several workloads that traditionally came from different tools:
- Search: full-text, enterprise and application search.
- Observability: logs, metrics, traces, application-performance monitoring and OpenTelemetry intake.
- Security: SIEM, XDR, endpoint and cloud-security capabilities.
- AI retrieval: vector search, hybrid search and reciprocal-rank fusion for applications that combine semantic and lexical results.
- AI operations: AI Assistant features, retrieval-augmented generation use cases and large-language-model observability.
These features are not identical across every subscription level. Some are beta or limited by region and deployment type, so buyers should use the feature matrix rather than assume that every Elastic Cloud plan includes the full platform.
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The financial scorecard
Elastic’s reported figures show that cloud and recurring subscriptions became increasingly important. The measures below come from the company’s fiscal-year results; fiscal years are Elastic’s reporting periods, not calendar years.
| Period | Metric | Reported result |
|---|---|---|
| Fiscal 2025 | Total revenue | $1.483 billion, up 17% year over year |
| Fiscal 2025 | Elastic Cloud revenue | $688 million, up 26% |
| Fourth quarter, fiscal 2025 | Elastic Cloud revenue | $182 million, up 23% |
| Fiscal 2026 | Total revenue | $1.739 billion, up 17% |
| Fiscal 2026 | Subscription revenue | $1.634 billion, up 18% |
| Fiscal 2026 | Sales-led subscription revenue | $1.438 billion, up 20%; excludes Monthly Elastic Cloud |
| Fiscal 2026 | Customers above $100,000 annual contract value | More than 1,720 |
Sources: Elastic fiscal 2025 results and Elastic fiscal 2026 results.
Fiscal 2025 cloud revenue grew nine percentage points faster than total revenue, a sign that the managed offering was gaining weight. Fiscal 2026 reporting uses “sales-led subscription revenue” and separately identifies Monthly Elastic Cloud, so that $1.438 billion measure should not be treated as the same series as the $688 million Elastic Cloud figure.
Why cloud matters to Elastic’s business model
Recurring and consumption revenue
Subscriptions renew more predictably than one-time software purchases, while usage can expand as customers ingest more data, add replicas, retain information longer or deploy additional workloads. That creates an opportunity for account expansion, but it also makes the customer’s bill sensitive to architecture and data volume.
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Lower adoption friction
A managed service lets a team start without building and staffing an Elasticsearch operating practice. For smaller platform teams, that can be more valuable than maximum infrastructure control.
Cross-selling
A search deployment can become an entry point for observability, security, vector search or AI tooling. The economic case depends on Elastic proving that one platform is simpler and more useful than several specialized services.
AI distribution
Cloud is a natural delivery channel for vector indexes, retrieval-augmented generation, AI assistants and LLM observability. Elastic’s investor communications position the platform as infrastructure for search, security, observability and AI; those are company strategy claims, not guarantees of future performance.
When Elastic Cloud is a strong fit
- The team wants managed Elasticsearch rather than cluster operations.
- Workload demand is growing or difficult to forecast.
- Search, observability, security and AI retrieval need to share a platform.
- Managed upgrades, snapshots, monitoring and support are worth the service premium.
- The organization already buys AWS, Azure or Google Cloud and values marketplace procurement.
- Managed vector search is needed alongside conventional search.
When self-managed Elastic may be better
- Air-gapped, residency-sensitive or otherwise isolated environments are mandatory.
- An experienced platform team already operates Kubernetes or private-cloud infrastructure.
- Deep control over plugins, node types, topology and tuning is required.
- Utilization is stable enough that operating infrastructure may cost less than managed consumption.
- Regulatory or security rules prohibit the relevant managed service.
The trade-offs buyers should model
Usage-based cost
Managed operations can reduce staffing and maintenance work, but the bill can rise with ingestion, retention, replicas, compute, storage tiers, high availability and cross-region transfer. A cost review should model those variables rather than compare only a headline service rate.
Best Value
Portability
Before committing, document export and restore procedures, index templates, mappings, managed-only features, marketplace constraints and the practical path to self-managed Elastic or another search service.
Control versus simplicity
Serverless and hosted modes reduce infrastructure decisions, but they also limit some low-level tuning. A workload with unusual latency, storage or topology requirements may benefit from more control.
Plan fragmentation
Security, compliance, AI, observability and management features can depend on subscription tier. Confirm entitlements, regional availability and service-level terms in the contract.
The competitive test
Elastic competes with AWS, Microsoft and Google cloud-native services; OpenSearch and managed OpenSearch; observability platforms such as Datadog, Dynatrace, New Relic, Grafana and Splunk; specialized vector databases; and customer-built open-source systems. The right comparison is workload-specific.
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- Amazon OpenSearch Service: relevant for AWS-centric procurement and OpenSearch compatibility; see AWS OpenSearch Service.
- CloudWatch: a natural AWS-native monitoring choice, but narrower than Elastic’s search-centered platform; see Amazon CloudWatch.
- Datadog or Grafana Cloud: strong SaaS observability alternatives when monitoring is the primary need; see Datadog and Grafana Cloud.
- Splunk or Dynatrace: established enterprise observability and security options; Splunk’s observability offering is at Splunk Observability.
- Self-managed Elastic: the control-oriented option, available through Elastic downloads.
What the appointment means now
Kulkarni’s promotion made cloud a central organizing principle for Elastic’s product and commercial strategy. The subsequent growth in Elastic Cloud and subscriptions indicates that the move was commercially significant, not merely a change in hosting format.
The unresolved question is durability. Elastic must make managed search economically compelling while defending against hyperscalers, OpenSearch and specialized observability and AI vendors. It also has to show that customers can adopt cloud without losing the control, portability or deployment flexibility that made self-managed Elastic attractive.
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