Azure Log Analytics
Azure Log Analytics is a service that helps you collect and analyze data generated by resources in your cloud and on-premises environments, providing query, management, and data collection APIs for monitoring and analytics.
Reference-quality API operations across every facet — a rich contract, published governance, transparent operations, and machine-readable commercial terms.
API Evangelist profiles Azure Log Analytics the way a machine reads it — 75 machine-readable artifacts across 5 APIs, pulled from the provider's own public surface and indexed so a developer, an analyst, or an AI agent can evaluate it against every other provider on the network.
Every provider in the network is reduced to the same set of machine-readable artifacts — OpenAPI contracts, event specifications, GraphQL schemas, runnable collections, pricing and rate-limit signals, security posture, OAuth scopes, and the agent surfaces (MCP servers and skills) that let software drive the API on its own. We profile them because the interface is the part of a company you can actually inspect: it is a truer signal of what a provider does than any marketing page. From those artifacts we compute the Kin Score — Azure Log Analytics scores 75.4/100 (exemplar), with a separate agent-readiness read of 48/100 (agent ready). The full breakdown is below, followed by every artifact we hold — each card links through to its machine-readable definition on apis.io.
Kin Score
This is the API Evangelist rating — a single, repeatable read computed from the artifacts on this page. Green fill is points earned; the red track is points possible, so every bar shows earned-versus-possible at a glance.
Kin Score
How this is scored →scored 2026-07-27 · rubric v0.5
How we profile Azure Log Analytics
Each block below is one kind of artifact we hold for Azure Log Analytics. For each we say what it is and why it earns a place in the profile, then list every one we've indexed — capped at two rows, scroll within the panel for the rest.
APIs 5
Each API is captured as its own OpenAPI definition — every operation, parameter, and response. This is the single most useful machine-readable description of what an API does, and it's what lets us score, lint, mock, and generate against it without asking the provider for anything.
Individual APIs this provider publishes, each with its own machine-readable definition.
Azure Log Analytics Ingestion API
Send log data to Log Analytics workspaces
Azure Log Analytics Query API
Execute KQL queries against Log Analytics workspaces
Azure Log Analytics Saved Searches API
Manage saved KQL queries
Azure Log Analytics Tables API
Manage workspace tables
Azure Log Analytics Workspaces API
Manage Log Analytics workspaces
Postman Collections 3
A runnable collection turns the contract into something a developer can execute in seconds. We profile them because the fastest way to trust an API is to make a real call against it.
Ready-to-run Postman collections for exercising this provider's APIs.
Arazzo Workflows 14
Real integrations are rarely a single call. Arazzo describes the multi-step sequences — auth, then create, then confirm — so both a human and an agent can follow the choreography, not just the endpoints.
Multi-step API workflows described with the Arazzo specification.
Azure Log Analytics Audit and Clean Up a Saved Search
List saved searches, inspect one, then delete it if it is uncategorized.
ARAZZOAzure Log Analytics Create Workspace and Baseline Custom Table
Create a workspace, add a baseline custom table, then read the table back.
ARAZZOAzure Log Analytics Cross-Workspace Query
Discover subscription workspaces, then run one KQL query spanning several of them.
ARAZZOAzure Log Analytics Discover and Query Workspace
Find a workspace in a subscription, confirm it, then run a KQL query against it.
ARAZZOAzure Log Analytics Ingest Logs and Verify
Confirm a target table exists, upload logs via a DCR, then query to verify.
ARAZZOAzure Log Analytics Browse Saved Searches and Run One
List a workspace's saved searches, fetch one's KQL, then execute it.
ARAZZOAzure Log Analytics Inspect Table Schema then Query
List a workspace's tables, inspect one table's schema, then query that table.
ARAZZOAzure Log Analytics Validate then Save a KQL Query
Run a KQL query to validate it, then persist it as a saved search.
ARAZZOAzure Log Analytics Provision Custom Table then Ingest and Verify
Create a custom table, upload logs through a DCR, then query the table to verify.
ARAZZOAzure Log Analytics Query Workspace by Name (GET)
Confirm a workspace exists, then run a KQL query via the GET query endpoint.
ARAZZOAzure Log Analytics Resolve Workspace by Resource Group and Run KQL
Narrow workspaces to a resource group, resolve one, then run a KQL query.
ARAZZOAzure Log Analytics Run a Saved Search
Fetch a saved search's KQL definition, then execute it against the workspace.
ARAZZOAzure Log Analytics Update Workspace Retention and Verify
Read a workspace's current retention, patch it, then read it back to confirm.
ARAZZOAzure Log Analytics Workspace Inventory Report
Resolve a workspace, then list its tables and its saved searches together.
ARAZZOScroll within the panel for all 14 ·
Pricing Plans 1
Pricing is part of the interface. Machine-readable plans tell you what a tier costs and includes before you commit — one of the six things the Kin Score reads for commercial clarity.
Published pricing tiers and plan structures.
Rate Limits 1
Rate limits are the difference between a demo that works and a production integration that doesn't fall over. Publishing them is an operational-transparency signal — and a hard requirement for any agent that plans its own throughput.
Documented rate limits and quota policies.
Azure Log Analytics Rate Limits
RATE LIMITSFinOps 1
Cost, billing, and metering signals let a buyer model the financial operations of an API before it's live. We profile them for the same reason we profile pricing: the money is part of the contract.
Cost, billing, and metering signals for API financial operations.
Features 10
The notable capabilities this provider advertises, captured as structured features so they can be searched and compared instead of read one landing page at a time.
Notable capabilities this provider offers.
Kusto Query Language
Full KQL query language support for complex log analytics and data exploration across cloud and on-premises resources.
Custom Log Ingestion
Send custom log data from any source using the Logs Ingestion API with data collection rules and transformations.
Workspace Management
Create, configure, and manage Log Analytics workspaces including data sources, retention policies, and access control.
Saved Searches
Save and reuse KQL queries across workspace sessions for consistent monitoring and reporting.
Data Collection Rules
Define data collection pipelines with transformations that shape incoming data before it reaches the workspace.
Cross-Workspace Queries
Query data across multiple Log Analytics workspaces in a single query for centralized analysis.
Simple Mode Queries
Point-and-click spreadsheet-like query experience for users who do not need full KQL knowledge.
Alert Rule Integration
Create alert rules directly from log queries to enable proactive monitoring and automated responses.
Workspace Failover
Activate and deactivate failover for workspace disaster recovery and high availability.
Data Export
Export query results to Excel, CSV, Power BI, and Grafana dashboards for external analysis.
Scroll within the panel for all 10 ·
Semantic Vocabularies 3
JSON-LD contexts give the data shared meaning across APIs. We profile them because semantics are what let a machine reconcile 'customer' here with 'customer' somewhere else.
JSON-LD contexts and semantic vocabularies used across these APIs.
Spectral Rules 2
Governance rulesets we run against this provider's specs — the automated checks behind parts of the score. Profiling them makes the quality bar explicit and re-runnable, not a matter of opinion.
Azure Log Analytics API Rules
SPECTRALAzure Log Analytics API Rules
SPECTRALJSON Schema 5
Standalone JSON Schema definitions describe the data models behind the API. We profile them so the shapes are validatable on their own — useful long after a single request is forgotten.
Standalone JSON Schema definitions for this provider's data models.
LogEntry
JSON SCHEMASavedSearch
JSON SCHEMAWorkspace
JSON SCHEMAQueryBody
JSON SCHEMAQueryResults
JSON SCHEMAJSON Structure 5
JSON Structure captures the data shapes in a form built for tooling — a complement to JSON Schema that keeps the model machine-legible.
JSON Structure definitions describing this provider's data shapes.
Ingestion Api Log Entry Structure
JSON STRUCTUREManagement Api Saved Search Structure
JSON STRUCTUREManagement Api Workspace Structure
JSON STRUCTUREQuery Api Query Body Structure
JSON STRUCTUREQuery Api Query Results Structure
JSON STRUCTUREExamples 5
Real request and response payloads are what turn a spec from abstract into obvious — and they're one of the twelve things an agent needs to call an API correctly on the first try.
Example request and response payloads for these APIs.
Security Posture 2
Authentication, domain security, vulnerability disclosure, and trust-center signals — the evidence that a provider takes security seriously enough to document it. We profile it because you can't govern what you can't see.
Authentication, domain security, vulnerability disclosure, and trust-center signals.
Scopes 1
OAuth scopes are the vocabulary of least-privilege access. Profiling them shows exactly what an integration — or an agent acting on a user's behalf — is allowed to do.
OAuth scopes governing access to this provider's APIs.
Agentic Access 1
An x-agentic-access contract marks which operations are safe for an agent to run on its own and which need a human in the loop. It is the difference between an API an agent can use and one it can use safely.
Recommended x-agentic-access execution contracts for AI agents.
Use Cases 6
What developers actually build with this provider — captured so the catalogue answers 'what is this for', not just 'what does this expose'.
What developers build with this provider.
Infrastructure Monitoring
Collect and analyze logs from virtual machines, containers, and network resources to monitor infrastructure health.
Security Investigation
Query security events and audit logs to investigate incidents and detect threats across Azure resources.
Application Performance Monitoring
Analyze application logs and telemetry to identify performance bottlenecks and errors.
Compliance Auditing
Collect and retain audit logs to meet regulatory compliance requirements and generate compliance reports.
Custom Data Integration
Ingest custom log data from third-party systems and on-premises resources using the Logs Ingestion API.
Cost Optimization
Analyze resource usage patterns and log data to identify cost-saving opportunities across Azure deployments.
Integrations 10
Pre-built integrations with other platforms tell you where this provider already fits in a stack.
Pre-built integrations with other platforms and tools.
Azure Monitor
Core integration with Azure Monitor for unified observability across metrics, logs, and traces.
Microsoft Sentinel
Feed log data into Microsoft Sentinel for SIEM and SOAR capabilities.
Azure Data Explorer
Built on Azure Data Explorer engine, supports the same KQL query language for advanced analytics.
Power BI
Export and visualize log query results in Power BI dashboards for business intelligence reporting.
Grafana
Connect Azure Monitor Logs as a data source in managed Grafana dashboards for visualization.
Azure Workbooks
Create interactive visual reports using log query results within Azure Workbooks.
Azure Automation
Trigger automation runbooks based on log query results and alert rules.
Azure Logic Apps
Integrate log analytics alerts with Logic Apps workflows for automated incident response.
Application Insights
Combine application telemetry from Application Insights with infrastructure logs for full-stack observability.
Azure Resource Manager
Manage Log Analytics resources programmatically through Azure Resource Manager REST APIs.
Scroll within the panel for all 10 ·
Resources
Every other property we hold for Azure Log Analytics — documentation, portals, status pages, policies, and corporate surface — grouped by the job it does, following the integrator's arc from getting started to running in production.
Get Started 2
Portal, sign-up, and the first successful call
Documentation 1
Reference material describing how the API behaves
Agent Surfaces 1
MCP servers, agent skills, and machine-readable catalogs
Design & Contract 16
Pagination, idempotency, versioning, errors, and events
Scroll within the panel for all 16 ·
Build 9
SDKs, sample code, and the tooling you integrate with
Scroll within the panel for all 9 ·
Access & Security 3
Authentication, authorization, and security posture
Operate 3
Status, limits, changes, and where to get help
Commercial 3
Pricing, plans, and the legal terms of use
Company 1
The organization behind the API
← All providers · Data indexed from github.com/api-evangelist/azure-log-analytics · machine-readable index on apis.io