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, with no separate tool per domain.
AI Powered Observability
One platform forevery layer of the stack.
XpediOo AI Observability consolidates infrastructure monitoring, application performance visibility, dependency intelligence, and incident response into a single operational surface. It is 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?
Eight capability areas
- Unified dashboards and operational visibilityCh. 01
- Infrastructure and cloud monitoringCh. 02
- Polyglot data layer monitoringCh. 03
- Network, application and synthetic assuranceCh. 04
- Discovery, dependency mapping and topology intelligenceCh. 05
- Intelligent alerting and incident responseCh. 06
- AI assisted operations intelligenceCh. 07
- Governance, multitenancy and extensibilityCh. 08
150+
AI features
25+
Monitored technologies
8
Capability domains
Overview
Every resource, one inventory
Modern environments span physical data centers, multicloud 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.
Eight capability areas
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.
Ch. 01
Unified dashboards and operational visibility
A single screen that answers “is everything okay?” in under five seconds: global health, custom dashboards, and exportable reporting.
- Global Overview Dashboard for ingestion, indexing, and satellite health
- Unlimited Custom Dashboards with persisted, shareable layouts
- Distributed Traces and deterministic Log Search & Exploration
- Explainable per resource Health Scores plus Export & Reporting
Ch. 02
Infrastructure and cloud monitoring
A live resource and satellite inventory spanning bare metal servers, Kubernetes clusters, and AWS, Azure, and GCP.
- Native Kubernetes cluster, workload, and pod telemetry
- Consolidated multicloud accounts, resources, cost, and alerts
- Cloud Auditors surfacing provider specific security findings
- Cloud Architects for architecture level review and recommendations
Ch. 03
Polyglot data layer monitoring
Purpose built monitoring for six data layer engine families, because data systems fail differently than application servers do.
- Relational databases, Redis, ElastiCache, and Memorystore
- ClickHouse parts, merges, mutations, and replication lag
- Elasticsearch / OpenSearch watermark aware alerting at 85 / 90 / 95%
- Snowflake, BigQuery, and Redshift workload and cost snapshots
Ch. 04
Network, application and synthetic assurance
Assurance for everything that serves traffic: network fabric, IP space, application runtimes, and outside in synthetic checks.
- Network device, interface, and traffic monitoring
- Full IP address, subnet, and DNS management (IPAM)
- Tomcat, WebLogic, WebSphere, JBoss, IIS, Node.js, and PHP-FPM
- Service SLO / error budget tracking and scheduled synthetic monitoring
Ch. 05
Discovery, dependency mapping and topology intelligence
Automatic discovery feeding a deterministic dependency graph, with AI inferred signal clearly separated from direct measurement.
- SNMP, ICMP, Cloud API, and Kubernetes discovery rules
- AI Auto Discovery for both infrastructure and applications
- Deterministic infrastructure and application dependency graphs
- Business Service Maps and interactive topology exploration
Ch. 06
Intelligent alerting and incident response
Reduce noise before it reaches a human, route what remains to the right channel, and resolve it automatically where policy allows.
- Grouping, deduplication, quiet hours, and maintenance windows
- Automated workflows with notify, escalate, script, and ticket steps
- Intelligent Routing Engine with full delivery history
- Approval gated Auto Remediation Engine with rollback
Ch. 07
AI assisted operations intelligence
An AI layer over the deterministic monitoring foundation: reviewable, editable output that augments operator judgment rather than replacing it.
- Root Cause Analysis and Capacity Forecasting
- Impact Analysis and baseline relative Drift Detection
- Cost Optimization with tracked, realized savings
- AI generated Runbooks, Daily Operations, and AI Search
Ch. 08
Governance, multitenancy and extensibility
Multi team isolation, an audit trail that survives scrutiny, reusable configuration standards, and public status communication.
- Native multitenant organizations with team management
- Audit & Compliance timeline with filterable governance records
- Reusable Monitoring Configuration Templates across OS, DB, cloud & more
- Tenant branded Public Status Pages with uptime history
Technical deep dive
Cloud, data and 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.
Multicloud 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 and 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
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, Ecommerce, 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 and compare
- Download the full generated architecture as JSON
- Save multiple designs locally and compare them side by side
- Rerun generation against a different provider without losing prior designs
Audit categories and 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 and 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
Fleet overview and 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 and 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 and 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 and 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
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 and alerting
- Add Storage Array configuration workflow for connecting new arrays
- Active alert tracking surfaced directly on the fleet overview
- Refresh control for on demand repolling of connected arrays
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 and 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 and 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
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 and 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 and 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
Appendix
Technologies and workflows at a glance
Appendix
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 and 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 and 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 and address management | Device/interface/traffic monitoring; VLANs, subnets, DHCP scopes, and DNS records |
| Storage | Capacity, performance, and lifecycle monitoring with dedicated maintenance planning |
At a glance
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 and 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 and search | What does the aggregate picture look like, and how do I find a specific entity fast? |
Why XpediOo AI Observability
Depth and breadth, without the tradeoff
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.
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 rather than acting as a black box.
Governed automation
Auto remediation, escalation workflows, and reconciliation rules are built around approval and rollback, not silent execution. That is automation operators can trust, because it stays inside explicit guardrails.
Built for real organizations
Native multitenancy, audit trails, reusable configuration templates, and public status communication fit how enterprise teams and their customers actually work.
Get started
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.