AI and Machine Learning Azure Machine Learning premium

Endpoint traffic split

Endpoint traffic split is the percentage allocation that routes live Azure Machine Learning online endpoint requests across one or more deployments during rollout or testing. In plain English, it is the Azure concept teams use when they need to roll out model deployments gradually, mirror traffic, compare behavior, and reduce customer risk during real-time inference changes. It matters because the term connects the feature people discuss in meetings to the resource, setting, identity, or data object operators must actually check.

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Aliases
online endpoint traffic split, traffic allocation, blue green endpoint split
Difficulty
intermediate
CLI mappings
4
Last verified
2026-05-14

Microsoft Learn

Azure Machine Learning online endpoints can allocate percentages of live traffic across deployments and can mirror traffic to validate a new deployment before full rollout.

Microsoft Learn: Safe rollout for online endpoints2026-05-14

Technical context

Technically, Endpoint traffic split appears in managed online endpoint details, deployment traffic allocation, YAML endpoint definitions, Azure ML Studio, CLI update commands, and traffic logs. Configuration usually centers on endpoint name, deployment names, traffic percentages, mirror traffic setting, authentication, autoscale settings, diagnostics, and rollback target. It depends on Azure Machine Learning workspace, managed online endpoints, online deployments, model registry, Azure Monitor, Application Insights, and private endpoints, so scope matters before any command, portal change, or deployment update.

Why it matters

Endpoint traffic split matters because a shallow definition leads teams to troubleshoot the wrong layer, approve weak changes, or miss dependencies that explain the real incident. It affects model release governance, customer-impact reduction, canary deployment, A/B evaluation, and production inference reliability, which means architecture, security, operations, finance, and application teams all need the same vocabulary. When the term is documented well, operators know the exact scope, owner, identity, metric, log, and rollback evidence to inspect before they scale, rotate, reindex, deploy, grant access, or escalate. That shared evidence reduces incident time, improves audits, and makes production change safer. This keeps architecture, security, support, and finance teams working from the same production evidence.

Where you see it

Signals, screens, and Azure surfaces where this term usually becomes operational.

Signal 01

In Azure Machine Learning Studio, endpoint traffic split appears as percentages assigned to blue and green deployments for one online endpoint during production review and support triage.

Signal 02

In CLI or YAML, it appears as traffic values such as blue equals ninety and green equals ten during staged rollout during production review and support triage.

Signal 03

In monitoring, it appears through per-deployment request count, latency, errors, logs, and mirrored traffic results during canary validation during production review and support triage for release evidence and incident response.

When this becomes relevant

Specific situations where this term helps solve real Azure design, operations, migration, security, reliability, cost, or governance problems.

  • Send a small percentage of live inference traffic to a new model deployment.
  • Mirror traffic to validate a deployment without changing client responses.
  • Roll back traffic quickly when a deployment causes latency or error regressions.

Real-world case studies

Different enterprise-style examples that show the term being used to hit measurable objectives.

Case study 01

Endpoint traffic split in action for healthcare AI

Scenario, objectives, solution, measured impact, and takeaway.

Scenario

MedVista Imaging, a healthcare AI organization, needed to release a new image-triage model without risking all radiology support requests at once.

Business/Technical Objectives
  • Limit production exposure during model rollout
  • Compare latency and error rate by deployment
  • Keep rollback under ten minutes
  • Preserve clinical review evidence
Solution Using Endpoint traffic split

The ML team deployed the new model as the green deployment behind the existing managed online endpoint. They first mirrored ten percent of traffic to validate logs and latency without changing user responses. After clinical reviewers approved sample outputs, operators shifted traffic to ninety-ten, monitored per-deployment metrics, and kept the blue deployment ready for rollback. Endpoint logs, model version mapping, and approval records were linked to the release ticket. The team validated Endpoint traffic split in a lower environment, captured before-and-after evidence, and promoted the change through controlled release gates. Runbooks were updated so support engineers could find the correct scope, identity, dependency, telemetry signal, and approval record without relying on the original implementer. The final design connected governance with daily engineering work, making the change understandable to security, operations, finance, and application stakeholders.

Results & Business Impact
  • Green deployment reached the latency target before full rollout
  • Rollback was tested and documented under eight minutes
  • Clinical reviewers received traceable sample evidence
  • No full-service outage occurred during deployment
Key Takeaway for Glossary Readers

Endpoint traffic split gives ML teams a controlled path from validation to production exposure.

Case study 02

Endpoint traffic split in action for transportation

Scenario, objectives, solution, measured impact, and takeaway.

Scenario

VoltRide Mobility, a transportation organization, wanted to test a new demand-forecasting endpoint for vehicle dispatch during a regional launch.

Business/Technical Objectives
  • Canary the new model in one rollout window
  • Compare dispatch prediction quality
  • Protect customer booking flow
  • Measure real traffic behavior
Solution Using Endpoint traffic split

Engineers deployed the new forecasting model beside the stable deployment and used endpoint traffic split to send five percent of live requests to it. Dispatch decisions were logged with deployment name, prediction, latency, and region metadata. Operations monitored booking failures, request duration, and capacity saturation. Once the new model showed better forecast accuracy without latency regression, traffic was increased in stages and the old deployment remained available through the launch weekend. The team validated Endpoint traffic split in a lower environment, captured before-and-after evidence, and promoted the change through controlled release gates. Runbooks were updated so support engineers could find the correct scope, identity, dependency, telemetry signal, and approval record without relying on the original implementer. The final design connected governance with daily engineering work, making the change understandable to security, operations, finance, and application stakeholders.

Results & Business Impact
  • Forecast accuracy improved by 16 percent
  • Booking error rates stayed within the existing target
  • Traffic increases were approved with live evidence
  • The old deployment was retired after the launch window
Key Takeaway for Glossary Readers

Traffic splitting helps teams test model improvements against real workloads while keeping the business safe.

Case study 03

Endpoint traffic split in action for public sector

Scenario, objectives, solution, measured impact, and takeaway.

Scenario

SummitGov Analytics, a public sector organization, needed to update a citizen-service classifier used by multiple agencies without disrupting intake portals.

Business/Technical Objectives
  • Avoid broad outage during classifier update
  • Collect per-agency performance evidence
  • Support immediate rollback
  • Keep model release auditable
Solution Using Endpoint traffic split

The shared ML platform hosted blue and green classifier deployments under one online endpoint. The release team assigned a small traffic percentage to green, then reviewed endpoint traffic logs by agency, request type, and error category. Agencies with complex forms were watched separately before more traffic moved. Runbooks defined the exact CLI rollback command, approval path, and dashboard links. After two stable review periods, traffic shifted fully to green. The team validated Endpoint traffic split in a lower environment, captured before-and-after evidence, and promoted the change through controlled release gates. Runbooks were updated so support engineers could find the correct scope, identity, dependency, telemetry signal, and approval record without relying on the original implementer. The final design connected governance with daily engineering work, making the change understandable to security, operations, finance, and application stakeholders.

Results & Business Impact
  • Agency intake latency stayed below the agreed threshold
  • Rollback evidence was ready but not needed
  • Three form-specific errors were fixed before full exposure
  • Release documentation satisfied the shared-service governance board
Key Takeaway for Glossary Readers

Endpoint traffic split is especially useful when one model endpoint serves many business owners.

Why use Azure CLI for this?

CLI checks for Endpoint traffic split turn portal assumptions into repeatable evidence. Start with read-only commands that show scope, identity, deployment, endpoint, storage, policy, status, metrics, or access relationships. Attach sanitized output to incidents and change tickets. Run mutating, security-impacting, or cost-impacting commands only after approval because the wrong tenant, subscription, resource, deployment, or policy can affect customers and downstream teams.

CLI use cases

  • Confirm the live Azure scope and current configuration for Endpoint traffic split before a production change.
  • Collect troubleshooting evidence for incidents involving Endpoint traffic split without relying on screenshots alone.
  • Compare CLI or REST output with source-controlled intent, dashboards, graph connections, and owner runbooks.

Before you run CLI

  • Run az account show and confirm the tenant, subscription, resource group, and environment before collecting evidence or changing anything.
  • Use read-only commands first, save sanitized JSON output, and compare live state with source control, tickets, and approved architecture notes.
  • Confirm owner, data classification, private connectivity, identity, monitoring destination, cost center, and rollback path before production changes.

What output tells you

  • Whether the exact resource, deployment, identity, endpoint, cache, scope, domain, or access package exists in the expected Azure boundary.
  • Which configuration values, linked dependencies, identity settings, networking controls, status fields, and timestamps explain current production behavior.
  • Whether the next action is a safe read, a configuration fix, a rollout adjustment, an access review, a cost review, or escalation to service owners.

Mapped Azure CLI commands

Endpoint traffic split operational checks

direct
az ml online-endpoint show --name <endpoint> --workspace-name <workspace> --resource-group <resource-group>
az ml online-endpointdiscoverAI and Machine Learning
read-only Microsoft docs
az ml online-deployment list --endpoint-name <endpoint> --workspace-name <workspace> --resource-group <resource-group> --output table
az ml online-deploymentdiscoverAI and Machine Learning
read-only Microsoft docs
az ml online-endpoint update --name <endpoint> --workspace-name <workspace> --resource-group <resource-group> --traffic "blue=90 green=10"
az ml online-endpointconfigureAI and Machine Learning
mutating Microsoft docs
az ml online-deployment get-logs --name <deployment> --endpoint-name <endpoint> --workspace-name <workspace> --resource-group <resource-group>
az ml online-deploymentdiscoverAI and Machine Learning
read-only Microsoft docs

Architecture context

Endpoint traffic split belongs to AI and Machine Learning architecture where identity, monitoring, cost ownership, reliability, and support need shared evidence.

Security

Security for Endpoint traffic split starts with least privilege and clear evidence about who can configure, view, operate, or misuse it. Review endpoint authentication, workspace RBAC, deployment identity, private endpoint access, request logging, model approval, and sensitive payload handling before production approval. A common mistake is assuming that a successful deployment, healthy metric, or working application proves the configuration is safe. Use managed identity where possible, protect secrets and keys, prefer private connectivity for sensitive paths, restrict logs that contain business data, and keep exceptions ticketed and time-bounded. For regulated workloads, connect the term to classification, retention, break-glass access, and incident-response procedures.

Cost

Cost for Endpoint traffic split includes more than the visible Azure meter. Review running multiple deployments, mirrored traffic compute, over-provisioned replicas, failed canaries, monitoring ingestion, and extended rollback windows because weak design often creates hidden spend through repeated processing, failed retries, over-provisioned capacity, unused assignments, support labor, audit cleanup, or extra storage. Tag ownership, environment, application, and cost center so charges can be explained. Compare actual use with purchased capacity, retention, token volume, request count, and operational value. Do not scale or rebuild blindly before checking configuration mistakes, retry loops, stale data, access errors, and monitoring evidence. This keeps architecture, security, support, and finance teams working from the same production evidence.

Reliability

Reliability for Endpoint traffic split depends on known limits, tested dependencies, and recovery procedures that operators can run without guessing. Review rollback path, blue-green deployment health, autoscale readiness, error budget, mirrored traffic safety, deployment logs, and SLA metrics before depending on it for a customer-facing workflow. The important question is how it behaves during retries, scale events, region issues, model changes, key rotation, index rebuilds, approval delays, or operator mistakes. Capture baseline metrics, expected states, and failure modes before change. Alert on symptoms that prove user impact, not just configuration drift, and keep rollback steps visible in the runbook. This keeps architecture, security, support, and finance teams working from the same production evidence.

Performance

Performance for Endpoint traffic split depends on workload shape, platform limits, dependency health, and how evidence is interpreted. Review per-deployment latency, cold starts, replica count, request concurrency, model load time, autoscaling, and traffic distribution accuracy before blaming the service or adding capacity. Look for saturation, throttling, queueing, cold starts, slow dependencies, stale indexes, oversized payloads, weak filters, or inefficient application behavior. Measure before and after any change and keep baselines for normal, peak, and incident conditions. For shared services, identify noisy neighbors and per-resource limits. Performance tuning should not create new security gaps, reliability risk, or unexpected cost. This keeps architecture, security, support, and finance teams working from the same production evidence.

Operations

Operations for Endpoint traffic split should be repeatable enough that a different engineer can collect the same evidence and reach the same conclusion. Review release gates, traffic-change approvals, metric dashboards, rollback commands, deployment ownership, incident runbooks, and post-release comparison reviews during change management, incident response, onboarding, and access reviews. Start with read-only checks, confirm tenant and subscription context, and attach sanitized CLI, REST, log, or metric output to the ticket. Keep names, tags, owners, dashboards, runbooks, and graph connections current. After every change, verify expected behavior and record any exception so future operators know what breaks first. This keeps architecture, security, support, and finance teams working from the same production evidence.

Common mistakes

  • Assuming a matching display name proves the correct resource when tenants, subscriptions, regions, and service instances can contain similar names.
  • Running mutating commands before capturing read-only evidence, owner approval, monitoring baselines, rollback steps, and expected post-change signals.
  • Treating a portal screenshot as complete proof when CLI output, REST responses, Activity Logs, metrics, and source-controlled definitions are more repeatable.