Observability should be simple, not an infrastructure project.

The Infrastructure Project Problem

A search engine adapted for observability, not built for it.

• Cluster management: node sizing, shard allocation, index lifecycle policies, hot-warm-cold-frozen tier configuration.
• Operational overhead before you investigate your first production issue. You are running an infrastructure project.
• Observability should be a tool you use, not a system you operate.

What If Observability Just Worked?

No clusters. No shards. No ILM policies. Just your system.

• Managed platform: Point your OpenTelemetry Collector at our OTLP endpoint and your system appears as a spatial topology.
• Services, dependencies, and telemetry in a single 3D environment. No infrastructure to manage.
• Your engineers focus on their systems. We handle the observability platform.
• Phantoms: when a call goes out and nothing ever reports back from the other side, or a consumer goes quiet while messages keep arriving, DeepCube draws the missing side where it should be, so what is missing shows up in the space instead of as a quiet gap on a dashboard.

Architecture: How We Differ

OpenTelemetry exclusive vs Elastic Agent.

DeepCube is built exclusively for observability. No cluster management, no shard allocation, no ILM policies to configure. Your engineers spend time understanding their systems, not operating the platform that monitors them.

Elastic has 550+ integrations (source, verified 2026-08-19), unmatched search and query capabilities for log analysis, and a flexible data platform that spans observability, security, and enterprise search. ES|QL and Elasticsearch's full-text, semantic, and vector search are genuinely world-class for ad hoc log investigation. For teams deeply invested in the Elastic ecosystem, the breadth of the platform is a genuine advantage.

Elastic is also candid about what its OpenTelemetry-native path gives up against its own classic stack, and it is worth reading before you count integrations. Their compatibility page states that "Many prebuilt Elastic integrations and dashboards are designed for ECS-formatted data and may not work as expected with the OpenTelemetry semantic conventions without customization", that its OTel-native browser instrumentation, EDOT Browser, is "in technical preview", so "continue using the classic Elastic APM browser agent for production RUM", that Universal profiling "is currently only supported in the classic stack", and that "Elastic OpenTelemetry does not provide managed TBS" (source, verified 2026-10-10).

AI: Tessa vs Elastic Agent Builder

Human on the loop.

Tessa connects the telemetry of the environment you are viewing to your connected codebase. Add your repositories as workspaces, and Tessa can trace a production issue from the 3D topology straight to the source file. Tessa accesses your connected codebase workspace, diagnoses from 3D topology, makes the fix. You review, you own it. Human on the loop.

Elastic's AI Assistant is query-focused, and Elastic scopes it that way itself: "The AI Assistant is an integration with a large language model (LLM) that helps you understand, analyze, and interact with your Elastic data", and the visualization it offers is "Create time-series charts and distribution graphs from your Elasticsearch data" (source, verified 2026-08-19). It is also now deprecated, with the Elastic AI Agent as its stated replacement. Valuable capabilities, but on self-managed and Elastic Cloud Hosted subscriptions they are gated to the Enterprise tier ($184/mo; entry pricing starts at $99/mo Standard) (tier gating: elastic.co/subscriptions; price: elastic.co/pricing/cloud-hosted; both verified 2026-10-10). Elastic Cloud Serverless tiers differently: there the Elastic Managed LLM is scoped to Observability Complete projects and metered per token. Elastic's route to your code runs through a coding agent you bring (see Code Fixes). AI that works in your code is common now, and Tessa does it too; the difference is where Tessa starts, the spatial view of your running system, and that it is in the per-node price.

Note: Elastic Agent Builder, generally available since 2026-01-22 ("Agent Builder is now GA", Elastic Search Labs, verified 2026-10-10), is now the strategic direction; the original Elastic AI Assistant is now deprecated, and Elastic's own docs banner reads "The Elastic AI Agent is now the default chat experience"; both are Enterprise-gated. Agent Builder and Workflows are metered on Serverless: 10,000 executions free, then as low as $0.025 and $0.0108 per execution respectively, and both are available only on Observability Complete projects (source, pricing, verified 2026-08-19).

Pricing: The Full Picture

Scenario: 40 nodes, 10 engineers.

Elastic's pricing model combines resource-based cloud pricing with tier-gated features. Here is what a realistic deployment looks like when you need AI and full observability:

Run Both. Migrate Gradually.

Same telemetry, two destinations, zero re-instrumentation.

• Dual-destination: Your OTel Collector fans out telemetry to both DeepCube and Elastic simultaneously.
• No code changes: If you already use OTel SDKs, your instrumentation does not change.
• If you use Elastic Agent: Keep Elastic Agent for legacy services. New services start with OTel + DeepCube. Migrate at your own pace.
• Keep Elastic for logs: Elasticsearch is excellent for log analysis. Use DeepCube for APM and spatial visualization while keeping Elastic for deep log search.
• Exit guarantee: If DeepCube is not right for you, change one endpoint URL. Your instrumentation stays exactly the same.