AI News
26 Jan 2026
Read 10 min
Multicloud AI workload migration guide: How to migrate fast
Migrate multicloud AI workload faster with AI code conversion, waived transfer fees and open standards.
Move AI apps faster across clouds with this multicloud AI workload migration guide. Use AI tools to rewrite code, open standards to connect agents, and high-speed links to move data. Follow clear steps to plan, test, and cut over with low risk. Reduce costs and keep options open.
AI and cloud choice now go hand in hand. Prices keep dropping, new providers enter the market, and moving data gets easier. AWS waives data-transfer charges when you leave, and new tools help you switch quickly. This multicloud AI workload migration guide gives you a simple path to plan, move, and run AI workloads across providers without lock-in.
Kiro (AWS dev assistant) or AWS Transform to convert code and infra.
Claude or ChatGPT to rewrite service calls and config files.
Create unit tests first, then let AI refactor to meet those tests.
Wire these tools into CI so changes run automatically.
Prefer S3-compatible storage and open SDKs to keep options open.
Adopt MCP and A2A so your agents talk to apps and other agents across clouds.
Containerize inference services to move them as one unit.
Bulk transfer a snapshot, then stream deltas until cutover.
Compress and chunk large files; validate with checksums.
Stage data near compute to reduce cold-start times.
Pick the smallest GPU that meets SLA, then scale horizontally.
Use spot/interruptible instances for batch jobs.
Cache embeddings and model outputs to cut repeat costs.
Write service adapters so app code stays stable.
Use contract tests to confirm identical behavior across clouds.
Pin model versions and drivers; record hashes in CI.
Connectivity: AWS Interconnect for multicloud links and published APIs.
AI dev: Kiro, AWS Transform, Claude, ChatGPT for code and config translation.
Models and agents: Amazon Bedrock and AgentCore; Strands Agents for multi-agent orchestration.
Portability: S3-compatible storage, open SDKs, and standardized APIs.
Operations: Terraform, OpenTelemetry, centralized logging, and cost dashboards.
Your multicloud AI workload migration guide in 10 steps
1) Set clear goals and success metrics
Define why you are moving: lower cost, better GPUs, data residency, or new services. Pick 3–5 metrics (latency, cost per inference, uptime). Set a deadline and agree on rollback triggers.2) Inventory workloads and data
List models, datasets, APIs, and batch jobs. Tag each by sensitivity, size, and uptime needs. Prioritize low-risk moves first. Keep a single source of truth in a spreadsheet or CMDB.3) Choose target clouds and regions
Match workloads to strengths: model hosting, vector DBs, GPUs, or compliance. Keep data near users. Spread risk across at least two regions. Note service equivalents you will use on each cloud.4) Build network and identity foundations
Use high-speed links between clouds. AWS Interconnect simplifies multicloud connectivity and publishes API specs; it is in preview with Google Cloud and will add Microsoft Azure later in 2026. Standardize identity and access. Use SSO and consistent roles across clouds.5) Plan data movement and storage
Exploit waived AWS data-transfer fees to move out at no charge. For storage, target S3-compatible endpoints to reuse tools and skills. Use snapshots and parallel uploads. Schedule transfers off-peak.6) Make models and agents portable
Use Amazon Bedrock to access 100+ foundation models, then abstract model calls in your code. With Amazon Bedrock AgentCore, build agents that can run on any cloud. Adopt open agent protocols: Anthropic’s MCP for app connections and Google’s A2A for agent-to-agent links.7) Accelerate code migration with AI
Use AI to translate services and SDKs fast:8) Stand up a multicloud landing zone
Use Infrastructure as Code (Terraform or CDK/CloudFormation) for repeatable builds. Apply guardrails for IAM, encryption, and tagging. Centralize logging and metrics. Use OpenTelemetry to ship traces from every cloud to one dashboard.9) Harden security and compliance
Encrypt data at rest and in transit. Manage keys per region. Log admin actions. Validate data residency policies before go-live. Run vulnerability scans and patch images automatically.10) Cut over safely and measure
Use blue/green or canary releases. Mirror traffic and compare latency and quality. Set autoscaling rules. Track cost per 1,000 tokens or per training hour. Keep a tested rollback path.Fast paths for AI migration
Use open interfaces to avoid rewrites
Move data once, stream updates
Right-size GPUs and save
Automate compatibility checks
Common pitfalls and how to avoid them
Underestimating egress and sync time
Even with waived AWS egress charges, bandwidth limits remain. Start transfers early, parallelize, and plan cutover windows.Feature drift between services
One cloud’s feature may not match another’s. Build a minimal, shared baseline and layer extras behind adapters.Observability gaps
If logs and traces live in silos, you fly blind. Standardize formats and centralize views before you move traffic.Security policy mismatch
Different defaults cause surprises. Compare IAM, network ACLs, and encryption policies line by line. Test with real attack simulations.Tooling checklist for speed
Costs, competition, and why now
Cloud competition is strong and prices have fallen over time. New providers offer fresh GPU options and global reach. AI is also speeding code conversion and reducing friction between environments. These shifts make migration faster and cheaper than before, while keeping your options open for future moves. A practical way to move today is simple: pick one priority workload, follow this multicloud AI workload migration guide, measure the results, and then scale the pattern to the rest of your stack. Move with intent, validate often, and keep control of cost and quality. In the end, the best plan is the one you can execute this quarter. Start small, automate early, and use standards and AI tools to do the heavy lifting. With this multicloud AI workload migration guide, you can ship faster, cut risk, and keep freedom of choice. (Source: https://www.aboutamazon.com/news/policy-news-views/aws-customer-choice-multicloud-ai-tools) For more news: Click HereFAQ
Q: What is the multicloud AI workload migration guide and who should use it?
A: The multicloud AI workload migration guide outlines a practical, step-by-step path to plan, move, and run AI workloads across cloud providers while avoiding lock-in. It is aimed at teams moving AI applications, models, and data between clouds to reduce risk, control costs, and retain flexibility.
Q: What high-level process does the guide recommend for migrating AI workloads?
A: The guide presents a 10-step process that includes setting goals and success metrics, inventorying workloads and data, choosing target clouds and regions, building network and identity foundations, planning data movement and storage, making models and agents portable, accelerating code migration with AI, standing up a multicloud landing zone, hardening security, and cutting over safely while measuring results. These steps are designed to reduce migration risk and keep options open during the move.
Q: How does the guide advise handling data movement and storage during migration?
A: The guide advises exploiting waived AWS data-transfer fees to move data out at no charge and targeting S3-compatible storage endpoints to reuse tools and skills. It also recommends using snapshots and parallel uploads for bulk transfer, compressing and chunking large files, streaming deltas after an initial snapshot, and scheduling transfers off-peak to reduce sync time and risk.
Q: Which AI tools does the guide recommend to accelerate code and infrastructure translation?
A: The guide recommends AI development assistants such as Kiro and AWS Transform and models like Claude or ChatGPT to translate service calls, SDKs, and configuration files. It advises creating unit tests first, letting AI refactor code to meet those tests, and wiring these tools into CI so changes run automatically.
Q: How can I make models and agents portable across different clouds according to the guide?
A: Use Amazon Bedrock to access over 100 foundational models and abstract model calls in your application so the backend can change without large rewrites, and employ Amazon Bedrock AgentCore to build agents that can run on any cloud. The guide also recommends adopting open agent protocols such as Anthropic’s MCP for app connections and Google’s A2A for agent-to-agent communication to improve interoperability.
Q: What networking and identity foundations are recommended for multicloud connectivity?
A: The guide recommends establishing high-speed links between clouds and standardizing identity and access across providers, using SSO and consistent roles to reduce friction. It also highlights AWS Interconnect as a capability that simplifies multicloud connectivity and publishes API specifications, noting it is in preview with Google Cloud and will add Microsoft Azure later in 2026.
Q: What common pitfalls should teams avoid during an AI workload migration and how can they be mitigated?
A: Teams should avoid underestimating egress and synchronization time even when data-transfer charges are waived, so the guide advises starting transfers early and parallelizing where possible. It also warns about feature drift between services, observability gaps, and security policy mismatches, recommending minimal shared baselines with adapters, standardized logging and traces, and comparing IAM/network/encryption policies with real attack simulations to validate controls.
Q: How should teams cut over to a new cloud environment and measure success after migration?
A: The guide recommends blue/green or canary releases, mirroring traffic to compare latency and model quality, and setting autoscaling rules while maintaining a tested rollback path. It also suggests tracking operational costs such as cost per 1,000 tokens or per training hour to measure the economic impact of the cutover.
Contents
