Temporal Cloud Migration: How a Scale-Up Achieved 99.99% Reliability by Migrating Production Workflows to Temporal Cloud

Who This Story is For

Engineering leaders and CTOs who:

• Run production AI / LLM workflows where mid-execution failure is unacceptable
• Are operating self-hosted Temporal (on-prem or cloud) and feeling reliability or ops drag
• Want Temporal Cloud benefits but cannot risk downtime or state loss during migration

This case study shows how teams migrate safely while production keeps running.

Executive Summary

A fast-growing scale-up built sophisticated on-premises Temporal deployment to power AI workflows and business process orchestration for executive leadership and third-party integrations.

The self-hosted infrastructure quickly became a bottleneck, as chronic reliability issues, availability gaps, and mounting engineering overhead consumed resources that should have been building products.

Xgrid executed a zero-downtime migration to Temporal Cloud using feature flags and dual-run strategies, eliminating infrastructure management overhead while guaranteeing 99.99% availability.

The result: AI agent workflows that complete reliably, engineering teams focused on innovation instead of cluster babysitting, and operational costs that actually make sense.

“Temporal wasn’t the problem. Owning the infrastructure for it was.”

The Real Cost of “It Works on My Machine”

The platform ran self-hosted Temporal to orchestrate AI workflows—multi-step LLM processes, data transformations, and external API integrations. These workflows powered executive decision-making and third-party systems where failures resulted in lost state, wasted compute costs, and broken integrations.

Self-hosted Temporal provided critical value: reliable orchestration for sequences that couldn’t afford mid-execution failure. But the self-hosted deployment revealed problems as usage scaled.

• Availability gaps impacted users directly. No robust high availability configuration meant infrastructure issues translated to workflow interruptions. Executive users noticed. Partners complained.
• Scaling required manual intervention. Each growth phase demanded capacity planning, infrastructure provisioning, and careful rebalancing. Business velocity consistently outpaced infrastructure velocity.
• Disaster recovery was unproven. Backup procedures existed on paper but were never fully tested. Any major failure scenario required manual recovery with unclear data loss boundaries.

What Workflow Failures Actually Cost at Scale

• Wasted LLM API spend when workflows fail mid-execution
• Lost or inconsistent state across multi-step AI agents
• Broken downstream integrations and partner escalations
• Executive distrust in AI-powered dashboards and decisions
• Senior engineers pulled into incident response instead of shipping

As AI workflows scale, each failure compounds cost, not just error rates.

The Infrastructure Trap: Why Moving to Cloud Wasn’t Enough

The team migrated infrastructure to Oracle Cloud, expecting cloud-native benefits to resolve availability and scaling challenges.

Some hardware management burden disappeared. The operational problems didn’t.

• Infrastructure costs stayed high and unpredictable. Running self-hosted Temporal on cloud infrastructure meant database instances, compute capacity, load balancers, storage. Overprovisioning for peak load and high availability drove costs higher than expected.
• Engineering teams still owned the entire operational stack. Monitoring cluster health, applying version upgrades, executing database migrations, and troubleshooting performance degradation required specialized expertise and continuous attention.
• Disaster recovery remained manual. Cloud provider redundancy reduced risk, but failover logic and backup strategies still needed to be designed, implemented, and maintained. Failure scenarios continued to carry intervention requirements and data loss risk.

The infrastructure changed. The problems followed.

Why Cloud Infrastructure Didn’t Solve the Problem

Moving self-hosted Temporal to cloud infrastructure removed hardware headaches — but kept the operational burden:

• Teams still own upgrades, scaling, failover, and recovery
• Reliability still depends on internal runbooks and human response
• Engineering time remains the hidden tax

The hosting location changed. The responsibility model did not.

3 non-negotiables for production AI workflow migration

• No blind cutovers — every change must be reversible
• No in-flight state loss — workflows must drain cleanly
• No trust without telemetry — observability validates readiness

The migration strategy was designed around these constraints — not timelines.

The Solution: Zero-Downtime Migration to Temporal Cloud

Xgrid designed a migration strategy around two non-negotiable requirements: zero disruption to running workflows and full confidence in the new infrastructure before cutover. The approach leveraged feature flags at the API layer to enable controlled, gradual migration without modifying workflow code.

1. Feature Flag Architecture for Dual-Run Strategy

Implemented feature flag system at the API layer controlling which Temporal cluster (self-hosted or cloud) would handle new workflow executions. This allowed routing different workflows to different backends without code changes, enabling gradual validation and rollback capability.

The architectural decision was deliberate: controlling routing at the API layer rather than within workflow code meant the migration became a configuration change instead of a code deployment. Instant rollback if issues emerged. Zero risk of introducing bugs into battle-tested workflow logic.

2. Parallel Infrastructure Validation

Set up Temporal Cloud alongside existing self-hosted deployment, configuring namespaces, workflows, and activities to mirror the production environment. Validated connectivity, authentication, monitoring, and observability tooling before routing any production traffic.

This parallel run phase answered critical questions: Does authentication work correctly? Are monitoring integrations capturing the right metrics? Do workflows execute with comparable performance? Can we debug issues as easily as on self-hosted?

Only after affirmative answers to all questions did any production workflow touch cloud infrastructure.

3. Graceful Workflow Draining Process

Stopped routing new workflows to self-hosted cluster while monitoring existing workflows to completion. Implemented real-time tracking to ensure zero workflows remained in-flight on old infrastructure before decommissioning, preventing state loss or incomplete executions.

For AI workflows where mid-execution failure meant wasted LLM API costs and corrupted state, careful draining was non-negotiable. Each in-flight workflow was tracked: when it started, current execution state, projected completion time. Decommissioning happened only after the last workflow completed successfully.

4. Staged Cutover with Rollback Safety

Gradually shifted workflow types to Temporal Cloud in controlled batches, starting with lower-risk workflows and monitoring for issues. Maintained ability to instantly route traffic back to self-hosted infrastructure if any problems appeared, ensuring complete safety throughout migration.

The rollout sequence was risk-based: background jobs and internal tools migrated first, customer-facing workflows next, mission-critical executive dashboards last. Each cohort ran on cloud infrastructure for a validation period before the next cohort migrated. Feature flags allowed instant reversion at any stage.

5. Infrastructure Cleanup and Cost Optimization

Once all workflows successfully completed on self-hosted cluster and Temporal Cloud proved stable under full production load, removed self-hosted cluster support from API codebase and decommissioned Oracle infrastructure, immediately reducing operational costs and engineering maintenance burden.

The cleanup was methodical: verify zero in-flight workflows on old infrastructure, remove feature flag routing logic from codebase, decommission Oracle database instances, terminate compute resources, validate cost reduction in next billing cycle. Only then was the migration considered complete.

Implementation at a Glance

Request a workflow review

How Teams Usually Run Migrations like These

Teams often execute migrations with a Temporal-certified Forward-Deployed Engineer (FDE) who:

• Works directly inside existing codebases and APIs
• Designs feature-flag and dual-run strategies
• Validates observability and rollback paths
• Ships alongside internal engineers, not in parallel silos

This keeps ownership internal while removing execution risk.

Results: From Infrastructure Burden to Engineering Leverage

The operational shift happened immediately. Workflows that previously required constant infrastructure attention now run reliably on Temporal Cloud without intervention.

Operational Reliability → Zero Incidents, Zero Firefighting

• Zero workflow disruptions during migration: Complete cutover executed without a single workflow failure, timeout, or state loss across all AI agent workflows and third-party integrations.
• 99.99% availability SLA eliminated reliability gaps: Temporal Cloud’s managed infrastructure provides automatic failover with no maintenance windows impacting users.
• Built-in disaster recovery: Automatic backups, point-in-time recovery, and multi-region redundancy handled entirely by Temporal Cloud without engineering effort.

Process Efficiency → Engineering Time Freed to Ship Features

• Infrastructure management eliminated: Engineering team no longer spends time managing Temporal clusters, applying upgrades, troubleshooting database issues, or handling operational incidents.
• Automatic scaling without intervention: AI workflow volume grows without capacity planning or manual infrastructure changes. Temporal Cloud automatically handles load increases.
• Faster time to market: New AI workflows and integrations deploy without concerns about cluster capacity or operational readiness.

Technical Performance → Reliable Execution Even at Peak Load

• Consistent workflow execution: AI agent workflows with multiple LLM calls and external integrations complete reliably without timeout issues that occurred on self-hosted infrastructure.
• Improved observability: Temporal Cloud’s built-in monitoring and metrics provide better visibility into workflow execution, making debugging and optimization significantly easier.
• Seamless third-party integration: External agents calling workflow endpoints experience consistent performance without availability gaps that previously caused integration failures.

Cost Impact → Predictable Costs, No Hidden Engineering Tax

• Lower total cost of ownership: Eliminating infrastructure costs and engineering overhead resulted in significant savings compared to self-hosted deployment, especially when factoring in hidden costs of operational time and incident response.
• Predictable pricing: Usage-based Temporal Cloud pricing replaced unpredictable infrastructure costs and the need to overprovision for peak capacity.

What Was Learned (And What Architecture Should Reflect)

These are six strategic decisions that separate successful migrations from disaster stories:

• 1. Feature flags aren’t optional for critical migrations. They enable gradual rollout, instant rollback capability, and confidence to migrate without risking production stability. Big-bang cutover is gambling with production
• 2. Workflow draining requires patience. Waiting for all in-flight workflows to complete on old infrastructure prevents state loss and ensures clean cutover. Rushing to decommission guarantees problems.
  
• 3. Self-hosted costs hide in engineering time. Infrastructure bills are visible. Engineering hours spent on maintenance, upgrades, incident response, and capacity planning are invisible and opportunity cost often exceeds infrastructure spend.
• 4. AI workflows demand reliable orchestration. Multi-step LLM processes cost money and carry state. Losing execution progress halfway through means wasted API spend and inconsistent results. Reliability is non-negotiable..
• 5. Proactive migration beats reactive firefighting. Moving infrastructure while systems are stable is orders of magnitude easier than migrating during a production crisis.
• 6. Observability enables confidence. New infrastructure cannot be validated without baseline metrics from old infrastructure. Measurement turns migration from gut-feel to engineering discipline.

Advanced Patterns

The platform now builds on stable infrastructure:

• Expanded AI workflow coverage without infrastructure concerns limiting feature development.
• Multi-region deployment leveraging Temporal Cloud’s global infrastructure to reduce latency for distributed users.
• Advanced workflow patterns like sagas, compensation logic, and human-in-the-loop approvals implemented without operational overhead.
• Enhanced monitoring and analytics building on Temporal Cloud’s observability to track workflow performance and usage trends.
• Scaled third-party ecosystem with confidence that workflow infrastructure handles increased volume automatically.

The Hidden Cost of Staying on Self-hosted Temporal

• Infrastructure spend grows linearly — engineering overhead grows faster
• Reliability incidents erode trust in AI systems
• Senior engineers become operators instead of builders
• Disaster recovery remains “theoretical” until it’s too late

Over time, inaction becomes the most expensive option.

Conclusion: Infrastructure Is a Cost Center—Treat It Like One

Engineering teams join to build AI products—not to manage databases.

Workflow timeouts increase with scale. Scaling strategies rely on adding infrastructure reactively. Disaster recovery remains untested. Senior engineers spend time firefighting instead of shipping features.

The economics are unforgiving. Each dollar spent on self-hosted infrastructure multiplies into engineering overhead. Each workflow failure erodes trust. Each scaling incident delays delivery.

Production-grade Temporal is not about technical capability alone—it is about choosing which problems are worth solving internally.

Managed services, feature flags, and observability turn migrations into predictable engineering exercises. Skipping them turns infrastructure into an executive escalation.

The question is not whether migration is affordable. The question is whether staying put is.

Considering a Temporal Cloud migration?

We run short workflow migration reviews where we:

• Identify which workflows are safe to migrate first
• Flag risks unique to AI / LLM orchestration
• Share proven zero-downtime migration patterns