LLM Application Migration and Rollout Validation

Manages safe deployment of AI application changes, including migrating self-managed SageMaker LLM serving to Amazon Bedrock and validating production retrieval updates for relevance, latency, resource usage, uptime, and output quality before or during rollout.

Business Blueprint

GROUNDED

Safely migrate and roll out LLM application changes by shifting traffic gradually, validating reliability and performance, and keeping rollback and governance controls in place.

The Problem

Slack needed to serve LLMs at enterprise scale with the security, reliability, and performance its customers expected, while avoiding the risk of changing a live backend in production.

Slack AI engineering and platform teams

They faced heavy operational overhead from SageMaker serving, including scaling latency and hardware scarcity that made instantaneous scaling difficult.

Slack AI users and enterprise customers

As Slack AI scaled to millions of users, customers still expected strong security, reliability, and performance from AI features.

Legal, Security, and FedRAMP reviewers

They had to approve the change before production traffic could be rerouted while maintaining Slack’s data privacy bar.

Cost of Inaction

Remaining on the prior approach would keep the business exposed to scaling latency, GPU scarcity, regional outage risk, and the production risk of switching a backend while serving live traffic.

Process Fit

Software development & delivery

As-Is

AI application releases depended on self-managed LLM serving capacity, regional infrastructure, and manual readiness work before production traffic could move. Scaling and infrastructure constraints made live changes risky.

To-Be

The release process uses a controlled AI platform layer: teams validate governance approvals, shift traffic gradually with feature flags, monitor availability and performance, and keep instant rollback and fallback paths ready before or during rollout.

Human Checkpoints

  • Legal, Security, and FedRAMP sign-off before rerouting production traffic — Legal, Security, and compliance approvers
  • Approval of gradual traffic shifts and rollback readiness — Engineering release owner
  • Selection of dedicated or on-demand capacity by workload type — AI platform owner

Systems Touched

AWS SageMakerAmazon Bedrockfeature flag and rollback controlsmulti-region LLM serving infrastructureAI platform abstraction

Business Cycle

Upstream

  • A compliant target serving platform with the required security posture must be available before migration.
  • Legal, Security, and FedRAMP approvals are needed before production traffic is rerouted.
  • Workloads need capacity policies that distinguish latency-sensitive interactive features from bursty scheduled workloads.

Downstream

  • Production traffic can be shifted gradually with instant rollback, preserving availability during the switch.
  • Requests can be rerouted in real time when a model or region is underperforming or hitting limits.
  • Bursty scheduled workloads can use on-demand infrastructure to eliminate costs for idle compute.

Value Evidence

  • Production availability during backend switchIMPROVED

    100% availability during the switch

  • AI feature scale supportedINCREASED

    millions of users

  • Idle compute cost for bursty scheduled workloadsREDUCED
  • Model availability and resilience when a model or region degradesIMPROVED

Adoption Journey

  1. LEVEL 1 — QUICK WIN

    Gate: Prove value on one AI feature with controlled capacity and clear latency expectations.

    Outcome: A low-risk starting point for validating whether the target serving platform can support the feature experience users expect.

  2. LEVEL 2 — STANDARD

    Gate: Prove production readiness with security approvals, gradual traffic shift, monitoring, and rollback.

    Outcome: The business can reroute live traffic without making the migration an all-or-nothing cutover.

  3. LEVEL 3 — ADVANCED

    Gate: Prove the rollout works across regions, workload types, and demand spikes.

    Outcome: The platform can sustain broader AI usage with spillover capacity and regional failover rather than relying on a single serving path.

  4. LEVEL 4 — ENTERPRISE

    Gate: Prove a shared AI platform can choose fallback models and healthy endpoints automatically.

    Outcome: AI releases become a platform capability with resilience policies that protect multiple features from model or regional degradation.

Detailed per-level builds in the solution spectrum below

Risk & Governance

  • Data privacy and regulated deployment approval

    Posture: Require Legal, Security, and FedRAMP sign-offs before production traffic is rerouted.

  • Production outage during backend migration

    Posture: Use gradual traffic shifts through feature flags and keep instant rollback available.

  • Capacity shortage or traffic surge

    Posture: Use provisioned capacity for latency-sensitive workloads and spill excess requests to on-demand endpoints.

  • Model or regional degradation

    Posture: Maintain model hierarchy and reroute requests to another healthy endpoint when needed.

Operating Intelligence

How it works

AI runs the operating engine in real time.

Humans govern policy and overrides.

Measured outcomes feed the optimization loop.

Confidence82%
ArchetypeOptimize & Orchestrate
Shape6-step circular
Human gates1
Autonomy
67%AI controls 4 of 6 steps

Who is in control at each step

Each column marks the operating owner for that step. AI-led actions sit above the divider, human decisions and feedback loops sit below it.

Loop shapecircular

Step 1

Sense

Step 2

Optimize

Step 3

Coordinate

Step 4

Govern

Step 5

Execute

Step 6

Measure

AI lead

Autonomous execution

1AI
2AI
3AI
5AI
gate

Human lead

Approval, override, feedback

4Human
6↺ Loop
AI-led step
Human-controlled step
Feedback loop
TL;DR

AI senses, optimizes, and coordinates in real time. Humans set policy and override when needed. Measurements close the loop.

The Loop

6 steps

1 operating angles mapped

Operational Depth

Technologies

Technologies commonly used in LLM Application Migration and Rollout Validation implementations:

Amazon BedrockInference
4 mentions
AWS SageMakerOther
4 mentions
Load testingOther
4 mentions
A/B evaluation frameworkOther
3 mentions
Feature flagsOther
3 mentions
+1 more technologies(sign up to see all)

Key Players

Companies actively working on LLM Application Migration and Rollout Validation solutions:

Amazon BedrockAWS SageMaker

Real-World Use Cases

Safe production rollout validation for retrieval application changes

Before a retrieval change goes live, Hornet checks whether the new version still returns the right documents, ranks them well, and uses acceptable resources, then supports safe rollout or reversal.

Evaluation-driven release management for AI retrieval systemsdescribed platform capability under construction; source states hornet verifies changes between application versions and supports safe production updates.
10.0

Zero-incident migration of Slack AI from SageMaker to Amazon Bedrock

Slack moved its AI backend from one AWS service to another slowly and safely, testing both systems side by side before shifting users over.

LLM serving platform migration with quality, latency, and capacity parity validationcompleted production migration with zero customer-facing incidents.
10.0

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