One Platform for
Every Layer of the Stack.
XpediOo AI Observability consolidates infrastructure monitoring, application performance visibility, dependency intelligence, and incident response into a single operational surface — built for teams tired of stitching together a dozen disconnected tools to answer one question: what is happening, why, and what do we do about it?
Every Resource, One Inventory
Modern environments span physical data centers, multi-cloud accounts, Kubernetes clusters, polyglot databases, message brokers, application servers, and business services that depend on all of the above. XpediOo AI Observability was built around a simple premise: operators should not need to context-switch between a cloud console, a database dashboard, a ticketing system, and a spreadsheet of dependencies to triage an incident.
Every resource type — from a bare-metal server to a Snowflake warehouse to a WebSphere cluster — is represented in the same inventory, monitored through the same alerting engine, and connected through the same dependency graph. Sitting on top of that deterministic foundation is an AI-assisted intelligence layer that applies large language models to the operational questions that traditionally required a senior engineer's judgment: what is the root cause, when will this resource run out of headroom, what happens if this dependency fails, and where is money being wasted.
This platform covers core observability, monitoring, and AI-assisted operations capabilities. AI-driven log analysis and log-based AIOps are a dedicated subsystem, documented separately.
From Everyday Visibility to AI-Assisted Intelligence
Moving from the day-to-day visibility surfaces operators use first, through specialized monitoring domains, into alerting and incident response, and finally into the AI-assisted intelligence layer.
Unified Dashboards & Operational Visibility
A single screen that answers “is everything okay?” in under five seconds — global health, custom dashboards, and exportable reporting.
Infrastructure & Cloud Monitoring
A live resource and satellite inventory spanning bare-metal servers, Kubernetes clusters, and AWS, Azure, and GCP.
Polyglot Data-Layer Monitoring
Purpose-built monitoring for six data-layer engine families, because data systems fail differently than application servers do.
Network, Application & Synthetic Assurance
Assurance for everything that serves traffic — network fabric, IP space, application runtimes, and outside-in synthetic checks.
Discovery, Dependency Mapping & Topology Intelligence
Automatic discovery feeding a deterministic dependency graph, with AI-inferred signal clearly separated from direct measurement.
Intelligent Alerting & Incident Response
Reduce noise before it reaches a human, route what remains to the right channel, and resolve it automatically where policy allows.
AI-Assisted Operations Intelligence
An AI layer over the deterministic monitoring foundation — reviewable, editable output that augments operator judgment rather than replacing it.
Governance, Multi-Tenancy & Extensibility
Multi-team isolation, an audit trail that survives scrutiny, reusable configuration standards, and public status communication.
Cloud, Data & Application Monitoring — In Depth
The seven areas below expand into full technical detail — every tab, metric, table, and workflow visible in the product, with AI-assisted features called out explicitly from deterministic monitoring. Select a section to expand it.
Cloud Monitoring
Unified multi-cloud inventory, cost visibility, and resource-level telemetry across AWS, Azure, GCP, Oracle Cloud, and OpenStack.
Cloud Monitoring
Unified multi-cloud inventory, cost visibility, and resource-level telemetry across AWS, Azure, GCP, Oracle Cloud, and OpenStack.
Multi-Cloud Overview
- Top-level KPI cards: Cloud Accounts, Total Resources, Active Alerts, and Monthly Spend
- Resources by Provider, Cost by Provider, Average CPU, Average Memory, and Resources by Type charts
- Cloud Accounts Status table — Account, Provider, Region, Resources, Monthly Spend, and Sync Status
- Active alert feed with inline acknowledge action
Provider Drill-Down
- Provider-scoped Accounts, Resources, Active Alerts, and Monthly Cost tiles
- Resource table — Name, Type, Region, Status, CPU, Memory, and Cost per Month
- Provider-scoped alert feed with a dedicated View action per alert
Resource Detail
- Location & Infrastructure and Specifications panels
- Kubernetes / Container metrics for containerized workloads
- Performance Metrics, Network Information, and Cost Information panels
Cost Visibility & Anomaly Detection
- Total Monthly Cost, Top Spending Provider, and Cost Records summary tiles
- Cost by Provider chart and a Top Services by Cost table
- Cost anomalies tracked through Active, Acknowledged, and Closed states
- AI-generated performance and resource-optimization recommendations
Enterprise Account Onboarding
- Supported providers: AWS, Azure, GCP, Oracle Cloud, and OpenStack
- Credential, region, and technical-contact capture per account
- Provider-specific IAM setup instructions shown inline during onboarding
AI Cloud Architects
Turns a written workload description into a reviewable, cost-estimated cloud architecture for AWS, Azure, GCP, and Oracle Cloud.
AI Cloud Architects
Turns a written workload description into a reviewable, cost-estimated cloud architecture for AWS, Azure, GCP, and Oracle Cloud.
Guided Architecture Design Wizard
- Step 1 — Workload Profile: provider, account/region, workload type, and environment
- Step 2 — Scale & Availability: expected scale and availability/SLA requirements
- Step 3 — Compliance & Data: compliance requirements and data-handling constraints
- Step 4 — Budget & Region: budget ceiling, target region, and networking/security/DR preferences
- Step 5 — Review & Generate: full input review before the AI generation call
- Six quick-start templates — 3-Tier Web App, Event-Driven Microservices, ML/AI Training Pipeline, E-Commerce, Real-Time Data Pipeline, and HIPAA Healthcare App
Generated Architecture Output
- Visual architecture diagram of the proposed environment
- Cost breakdown chart showing estimated monthly spend by component
- A Well-Architected-style scoring summary evaluating the design
- Collapsible sections for Compute, Network, Security, Storage, Database, Monitoring, and Cost
- Infrastructure-as-Code output and an implementation roadmap
Save, Export & Compare
- Download the full generated architecture as JSON
- Save multiple designs locally and compare them side by side
- Re-run generation against a different provider without losing prior designs
Cloud Auditors
Structured, framework-mapped compliance audits that use AI to turn raw findings into prioritized, explainable remediation guidance.
Cloud Auditors
Structured, framework-mapped compliance audits that use AI to turn raw findings into prioritized, explainable remediation guidance.
Audit Categories & Framework Mapping
- Core categories: IAM/Identity & Access, Compliance Frameworks, Network Security, Data Protection & Storage, Logging & Monitoring, and Cost & Governance
- Framework references: CIS Benchmarks, SOC 2, PCI-DSS, HIPAA, GDPR, and NIST 800-53
- GCP-specific coverage: VPC Service Controls, firewall rules, Cloud Armor, and Private Google Access
- Oracle-specific coverage: CIS Oracle Cloud Benchmarks
Configurable Audit Runs
- Audit Category scope selector (a specific category or All Categories)
- Resource Scope selector — Top 10, 25, 50, 100, or All Resources
- Explicit LLM Provider and Model selection for the AI-assisted remediation pass
- Live progress through initialization, evidence collection, analysis, and report generation
Cross-Cloud Comparison & Findings
- All-Providers overview with per-provider compliance-score cards and finding counts
- Compliance-score trend chart across successive audit runs, per provider
- Framework-compliance-by-provider matrix and a Top Critical Findings feed
- Per-audit result: score, severity breakdown, framework detail, and AI-generated remediation guidance
Database Monitoring
A single health, performance, and AI-assistance surface for PostgreSQL, MySQL, Microsoft SQL Server, Oracle, and MongoDB fleets.
Database Monitoring
A single health, performance, and AI-assistance surface for PostgreSQL, MySQL, Microsoft SQL Server, Oracle, and MongoDB fleets.
Fleet Overview & Per-Engine Detail
- Fleet KPIs: Total Databases, Healthy, Warning, and Critical/Offline counts
- Provider, deployment, and environment filtering across heterogeneous fleets
- Dedicated tabs per engine (PostgreSQL, MySQL, MS SQL, Oracle, MongoDB)
- Engine-specific detail: health score, sessions, wait-events, buffer/cache stats, WAL/replication
Query & Index Diagnostics
- Query Performance and Expensive Queries tables
- Index Health with missing-index recommendations and an Index Growth trend chart
- Lock Contention detection for blocking/blocked-session scenarios
- Trending Analysis across historical performance data
Maintenance & Auto-Remediation
- Maintenance task queue with status, scheduling, and per-task actions
- Auto-Remediation recommendations backed by real mutation/API calls
- Alert acknowledge/resolve workflow tied directly to remediation actions
AI Database Assistant & AI Tools Suite
- AI Assistant — natural-language chat scoped to the selected database connection
- 11 AI Tools: Query Optimizer, Anomaly Detection, Configuration Tuner, Performance Prediction, Automated Tuning, Root Cause Analysis, NL Query Builder, Schema Analysis, Relationship Mapping, Execution Plan Analysis, and Workload Analyzer
- Configuration recommendations with workload type, impact badge, current vs. recommended values, and rationale
Storage Monitoring
Unified enterprise storage-array monitoring across Dell EMC, HPE, IBM, NetApp, Pure Storage, and Hitachi platforms.
Storage Monitoring
Unified enterprise storage-array monitoring across Dell EMC, HPE, IBM, NetApp, Pure Storage, and Hitachi platforms.
Fleet Overview
- Top-level KPIs: Storage Arrays, Healthy array count, Total Capacity, Utilization %, and Active Alerts
- Capacity by Vendor chart comparing allocated and used capacity
- Array Status Distribution chart summarizing fleet-wide health
- Filter by Array control for scoping every downstream tab
Depth Across Eight Tabs
- Overview, Performance, Capacity, and Health
- Disks, Replication, and Controllers
- AI Intelligence — AI-generated capacity and health recommendations
Array Onboarding & Alerting
- Add Storage Array configuration workflow for connecting new arrays
- Active alert tracking surfaced directly on the fleet overview
- Refresh control for on-demand re-polling of connected arrays
App Server Monitoring
JVM and web-tier runtime monitoring across seven application-server engines that most infrastructure tools treat as a black box.
App Server Monitoring
JVM and web-tier runtime monitoring across seven application-server engines that most infrastructure tools treat as a black box.
Engine Coverage
- Apache Tomcat, Oracle WebLogic, IBM WebSphere, JBoss/WildFly, Microsoft IIS, Node.js, and PHP-FPM
- Real, engine-aware collectors with scheduled polling of registered instances
- Instance inventory with add/edit workflows and per-instance status
Runtime & Application Metrics
- CPU, memory, heap, garbage-collection, JVM, and process/thread metrics per instance
- Request throughput, error rate, and latency tracking
- Deployed-applications table — App, Context Path, Version, and Status
- Endpoint-level table — Endpoint, Method, Requests/sec, Error Rate, Avg Response, and p95 latency
Topology, Remediation & Alerting
- Topology and dependency view for each application-server instance
- Remediation execution history with an explicit approval step before actions run
- Per-instance “Poll Now” for on-demand metric refresh, alongside scheduled polling
Service Performance
Live golden-signal tracking for business-facing microservices, correlated to the underlying Kubernetes workloads.
Service Performance
Live golden-signal tracking for business-facing microservices, correlated to the underlying Kubernetes workloads.
Live Service Map
- Every tracked microservice rendered as a node in a live, auto-updating call graph
- Color-coded node status — healthy, degraded, and SLO breach
- Directed edges labeled by call type (HTTP, queue, cache, and similar)
- Click-to-inspect: selecting a node opens its trend and SLO history
Golden Signals & SLO Management
- Fleet KPIs: Services Tracked, Services Breaching SLO, Average p95 Latency, and Total Throughput
- Per-service golden signals — latency, throughput, errors, and saturation
- Error budget and burn-rate tracking per service
- SLO breach history over a rolling 24-hour window
Service Detail & Management
- Per-service detail: callers, p95 latency, requests/sec, error rate, and current status
- Add, edit, and remove tracked services and their dependency edges
- Add and edit SLO definitions per service directly from the management dialog
Supported Technology Families
| Domain | Coverage |
|---|---|
| Container Orchestration | Kubernetes — clusters, namespaces, workloads, pods, and container-level metrics |
| Public Cloud | AWS, Microsoft Azure, and Google Cloud Platform — accounts, resources, cost, and audit findings |
| Relational & General-Purpose Databases | PostgreSQL, MySQL, Microsoft SQL Server, Oracle, MongoDB, and other configured engines |
| In-Memory Data Stores | Redis (standalone, sentinel, cluster), Amazon ElastiCache, Google Cloud Memorystore |
| Analytical Databases | ClickHouse — parts/merges, mutations, ZooKeeper/Keeper coordination, replication |
| Search & Indexing | Elasticsearch and OpenSearch — nodes, clusters, indices, and watermark-based alerting |
| Cloud Data Warehouses | Snowflake, Google BigQuery, Amazon Redshift — compute, query activity, and cost snapshots |
| Application Servers | Apache Tomcat, Oracle WebLogic, IBM WebSphere, JBoss/WildFly, Microsoft IIS, Node.js, PHP-FPM |
| Network & Address Management | Device/interface/traffic monitoring; VLANs, subnets, DHCP scopes, and DNS records |
| Storage | Capacity, performance, and lifecycle monitoring with dedicated maintenance planning |
AI-Assisted Workflows
| Capability | Primary Question It Answers |
|---|---|
| Root Cause Analysis | Why did this incident happen, and what evidence supports that conclusion? |
| Capacity Forecasting | How long until this resource runs out of headroom? |
| Impact Analysis | What breaks downstream if this resource fails or changes? |
| Drift Detection & Baselines | Has this metric meaningfully deviated from its own historical normal? |
| Cost Optimization | Where can cloud spend be reduced without sacrificing capacity? |
| Alert Recommendations | Which alert rules are noisy, redundant, or mistuned? |
| Runbooks | What is the documented procedure for handling this scenario? |
| Auto-Remediation | Can this class of incident be resolved automatically, within approved guardrails? |
| AI Analytics & Search | What does the aggregate picture look like, and how do I find a specific entity fast? |
Depth and Breadth, Without the Trade-Off
Most observability platforms force a choice: broad but shallow coverage across every resource type, or deep, specialized monitoring for a handful of engines. XpediOo AI Observability pairs engine-specific depth across a dozen data and infrastructure domains with a single alerting, dependency, and AI-assistance layer that spans all of them.
Breadth Without Fragmentation
From bare-metal servers and Kubernetes clusters to Snowflake warehouses and WebSphere clusters, every resource type lives in the same inventory, dependency graph, and alert pipeline — no separate tool per domain.
AI That Assists, Not Replaces, Judgment
Root cause analysis, capacity forecasting, cost optimization, and runbook generation all surface reviewable, editable output with explicit AI configuration — augmenting operator decision-making, not acting as a black box.
Governed Automation
Auto-remediation, escalation workflows, and reconciliation rules are built around approval and rollback, not silent execution — automation operators can trust because it stays inside explicit guardrails.
Built for Real Organizations
Native multi-tenancy, audit trails, reusable configuration templates, and public status communication fit how enterprise teams and their customers actually work.
See Every Layer of Your Stack, in One Place
Walk through how XpediOo AI Observability unifies your infrastructure, cloud, data, and application monitoring — with AI-assisted operations on top.