# Why Migrate to Valohai? **TL;DR:** Migrate to Valohai in phases starting with one high-impact project. Most organizations see 50% faster experimentation within weeks and achieve full adoption in 3-6 months. Zero vendor lock-in and your code stays yours. ## Why Teams Migrate to Valohai Your ML teams are likely battling at least one of these challenges: ### Infrastructure Overhead Is Killing Productivity * Data scientists spending 40% of time on DevOps tasks * "Works on my machine" blocking production deployments * Each team maintaining their own MLOps stack * Cloud costs spiraling due to idle resources ### Experiments Aren't Reproducible * Can't recreate that model from 6 months ago * Missing dependency versions breaking reruns * Audit requirements forcing manual documentation * Teams re-running identical experiments unknowingly ### Scaling Hits a Wall * Single-machine limits blocking larger experiments * Manual provisioning creating bottlenecks * No resource sharing between teams * Every new project starts from zero ### Teams Can't Collaborate Effectively * Models trapped in individual laptops * No standard deployment pipeline * Knowledge lost when people leave * Integration nightmares between team tools ## The Strategic Migration Path
Phase 1: Prove Value Fast (Weeks 1-4) Start with one team's biggest pain point. Get a working project in hours, not weeks. **Choose Your Pilot:** * High-impact project with clear metrics * 3-5 person team eager for better tools * Existing code that runs today **Success Looks Like:** * First execution running within 2 hours * 50% reduction in experiment setup time * Zero infrastructure debugging by data scientists * Team asking "can we migrate more projects?" **Deliverables:** * Working project with automated tracking * Before/after metrics showing time saved * Initial cost analysis * Team testimonial for internal buy-in
Phase 2: Standardize Your Workflows (Months 2-3) Scale what works. Move from individual wins to team transformation. **Expand Scope:** * Full project lifecycle (data → training → deployment) * Advanced features (pipelines, hyperparameter optimization) * Team-wide best practices **Success Metrics:** * All team projects using Valohai * 70% reduction in "plumbing" work * Models deploying in hours, not weeks * 30% infrastructure cost reduction **Deliverables:** * Reusable templates for common workflows * Automated CI/CD pipelines * Team playbook documented * Quarterly cost savings report
Phase 3: Scale Across the Organization (Months 4-6) Transform ML from cost center to innovation engine. **Organization-Wide Impact:** * All ML teams onboarded * Cross-team model sharing * Enterprise governance active * Executive visibility enabled **Success Metrics:** * 60% faster model delivery to production * 90% experiment reproducibility * Full compliance audit trail * 50% reduction in total ML infrastructure costs
## Common Migration Scenarios
"We Need to Replace Our Existing Platform" Your current platform promised the world but delivered complexity. **Migration Strategy:** * Run Valohai parallel to existing platform * Migrate your most painful workflows first * Compare metrics side-by-side * Sunset old platform once value proven **Timeline:** 2-3 months for complete transition
"We're Drowning in Tool Sprawl" Different teams, different tools, zero standardization. **Migration Strategy:** * Map current tool landscape and overlaps * Identify common workflows across teams * Replace tool-by-tool with unified platform * Calculate maintenance hours saved **Timeline:** 3-4 months to consolidate
"We Can't Get Models to Production" Research breakthroughs dying in deployment purgatory. **Migration Strategy:** * Start with research workflow * Add deployment in same platform * No handoffs between teams * Measure time-to-production improvement **Timeline:** 1-2 months for first production model
"We're Starting Our ML Journey" Green field opportunity to build it right. **Migration Strategy:** * Implement best practices from day one * Avoid accumulating technical debt * Scale gradually as team grows * Learn from others' mistakes **Timeline:** Immediate value, scales with growth
## Addressing Executive Concerns
"What About Lock-In?" **Reality Check:** * Your code runs unchanged—no Valohai SDK required * Standard Docker, Git, and YAML throughout * Full API access to export everything * Migration is so pattern-based that [AI coding agents can do it](/migration-strategy/migrate-with-ai-skills.md) Your migration path out is as easy as your path in.
"How Do We Justify the Investment?" **Measurable Returns:** **Month 1:** * 50% reduction in experiment setup time * Infrastructure debugging eliminated * First cost optimizations visible **Months 2-3:** * 30-50% infrastructure cost reduction * 2x faster development cycles * Tool consolidation savings **Months 4+:** * 60% faster time-to-market * 5x improvement in asset reuse * Complete compliance coverage
"Will Our Teams Actually Adopt This?" **Why Teams Love Valohai:** * Keep using familiar tools (Python, notebooks, Git) * No new languages or frameworks * Less time on plumbing, more on ML * [AI coding agent skills](/migration-strategy/migrate-with-ai-skills.md) handle the migration steps automatically * Success spreads organically Early adopters become internal champions.
"What If Something Goes Wrong?" **Risk Mitigation Built In:** * Enterprise SLAs with 24/7 support (by separate agreement) * Gradual migration reduces risk * Your code remains portable * Professional services available
## Making the Decision ### Key Decision Factors **Cost of Delay:** Every month without proper ML infrastructure costs you: * Lost innovation opportunities * Accumulated technical debt * Growing compliance risk * Widening competitive gap **Why Valohai Over Alternatives:** * Only true bring-your-own-code platform * Managed service = zero maintenance * Proven with enterprises like yours * Scales from startup to enterprise ### Next Steps 1. **Technical Validation** * Review the [technical migration guide](/migration-strategy.md) * Run proof-of-concept with pilot team * Benchmark against current setup 2. **Business Case Development** * Calculate current infrastructure + maintenance costs * Estimate productivity gains from metrics above * Factor in compliance and risk reduction 3. **Get Expert Input** * Schedule migration assessment with Valohai * Connect with similar organizations who've migrated * Review security and compliance requirements *** **Ready to transform your ML operations?** Start with our [technical migration guide](/migration-strategy.md) or contact our team at for a migration assessment. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://docs.valohai.com/migration-strategy/why-migrate-to-valohai.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.