COMPARE
AI SRE and Operational Intelligence Platforms: what should teams compare?
The AI-for-operations market now includes several different product categories that are often grouped together under “AI SRE.” Understanding their architecture and operating model matters more than comparing feature checklists alone.
PRODUCT CATEGORIES
“AI SRE” now describes several different products
Incident-management and AI SRE
Example: Rootly
- Alerts
- Incident response
- On-call
- Coordination
- Root cause analysis
- Retrospectives
Telemetry-native AI SRE
Example: Edge Delta
- Logs, metrics and traces
- Telemetry pipeline
- Issue detection
- AI investigation
- Action and recommendation
Agent-driven production operations
Example: Resolve AI
- Production agents
- On-call agents
- Incident agents
- Background operational tasks
- Governed actions
Operational Intelligence Layer
Example: Opsphere
- Existing source systems
- Broad tool interrogation
- Operational Context
- Evidence-backed investigations
- MCP access
- Read-only by default
EVALUATION
10 questions to ask before choosing an AI SRE platform
1. Who owns the operational data?
- Does it query existing systems?
- Does it pull telemetry into its own store?
- Does it require its own observability pipeline?
2. What is the primary workflow?
- Incident response?
- Observability?
- Autonomous agents?
- Broad operational intelligence?
3. Does it work outside incidents?
- Configuration questions
- Infrastructure state
- Deployment differences
- DNS and TLS behavior
- Repository relationships
4. How does it establish root cause?
- Evidence
- Hypotheses
- Confidence
- Contradictions
- Verification
5. Does it retain context?
- Knowledge graph
- Service relationships
- Incident history
- Investigation memory
6. What can it change?
- Read-only
- Recommendations
- Approval-required writes
- Autonomous actions
7. How does it integrate with AI clients?
- Proprietary UI
- MCP
- API
- Cursor, Codex, Claude
- Agent frameworks
8. Does it replace an existing category?
- PagerDuty and incident management?
- Datadog and observability?
- Other existing tools?
- Or does it work alongside them?
9. How is tenant and security scope handled?
- Tenant
- Account
- Environment
- Approvals
- Auditability
10. How is value measured?
- Time to context
- Investigation speed
- Evidence quality
- On-call reduction
- Tool-call efficiency
CATEGORY MATRIX
Four architectural categories, side by side
Opsphere
- Primary public category
- Operational Intelligence Layer
- Data / architecture starting point
- Query and correlate existing source systems
- Investigation
- Evidence-backed structured investigations
- Persistent context
- Knowledge Graph plus Investigation Memory
- Action model
- Read-only by default
Resolve AI
- Primary public category
- AI for production / production agents
- Data / architecture starting point
- Integrations plus an agent platform plus production context
- Investigation
- Agent teams and incidents
- Persistent context
- Queryable graph and learning
- Action model
- Governed actions
Rootly
- Primary public category
- Incident management and AI SRE
- Data / architecture starting point
- Incident and on-call platform plus integrated sources
- Investigation
- Evidence-backed AI SRE
- Persistent context
- Incident and service context
- Action model
- Human sign-off oriented
Edge Delta
- Primary public category
- Telemetry-native AI SRE
- Data / architecture starting point
- Telemetry pipeline architecture
- Investigation
- AI teammates and issues
- Persistent context
- Issue, history and environment context
- Action model
- Approval-based actions
This matrix describes each platform's public category and architecture. It is not a scorecard, and it is re-verified against each vendor's official documentation before review.
WHERE OPSPHERE FITS
Opsphere's approach: operational intelligence without replacing source systems
Opsphere is designed for teams that already have specialized operational systems and want an intelligence layer across them.
- Querying source systems
- Evidence-backed investigations
- Reusable Operational Context
- MCP access
- Read-only by default
- Broad operational questions beyond the incident lifecycle
CHOOSING
There is no single best AI SRE
The right architecture depends on what a team wants to consolidate. Choose based on whether your priority is the incident lifecycle, telemetry pipeline consolidation, autonomous production agents, or operational intelligence across an existing stack.
The most important comparison is not “which platform has AI?” — it is “where does the operational truth live, and what is the AI allowed to do with it?”
DIRECT ANSWERS
Key facts about AI SRE and Operational Intelligence Platforms: what should teams compare?
A concise, verifiable summary based on the capabilities and operating boundaries described on this page.
- What distinguishes operational intelligence platform categories?
- The AI-for-operations market now includes several different product categories that are often grouped together under “AI SRE.” Understanding their architecture and operating model matters more than comparing feature checklists alone.
- How should teams evaluate this category?
- The right architecture depends on what a team wants to consolidate. Choose based on whether your priority is the incident lifecycle, telemetry pipeline consolidation, autonomous production agents, or operational intelligence across an existing stack.
- Does Opsphere change production systems?
- Opsphere is read-only by default. It queries connected source systems, correlates evidence and recommends next steps; source systems remain authoritative.