Migration Assistant

Migration planning helps you evaluate, plan, and execute transitions between AI vendors or LLM providers. Whether you’re moving from GPT-4 to Claude, migrating from proprietary systems to open-source, or switching cloud providers, TruthVouch guides you through every step.

Why Migrate?

Common reasons to switch providers:

Cost Optimization

Reduce API costs by 30-40% with better pricing or rate limits
Optimize token usage with more efficient models
Consolidate multiple vendors into one

Performance Improvement

Newer models with better accuracy or reasoning
Better performance on specific domains (code, reasoning, creative tasks)
Lower latency with alternative providers

Governance & Risk

Better data privacy (on-premise or regional models)
Compliance with regulations (GDPR, data residency)
Vendor lock-in reduction
Security audit results require change

Availability & Reliability

Reduce dependency on single vendor
Alternative models with higher uptime SLA
Local/self-hosted options for critical workloads

Migration Planning Process

Phase 1: Readiness Assessment (1-2 weeks)

Define your constraints:

Current state — What models, providers, and volumes are you using?
Requirements — Cost targets, performance minimums, compliance needs?
Risk tolerance — How much disruption can you absorb?
Timeline — How quickly must migration complete?

Key metrics to benchmark:

Current monthly API costs by provider and model
Average latency and error rates
Compliance gaps (if any)
Vendor SLA and uptime history

Phase 2: Target Evaluation (2-4 weeks)

Evaluate candidates:

Create comparison matrix (Cost, Performance, Compliance, Support):

Provider	Model	Cost/1M tokens	Latency	Compliance	Support
OpenAI	GPT-4o	$15	50ms	SOC 2	Email + API
Anthropic	Claude 3	$20	80ms	SOC 2 + HIPAA	Email + API
Google	Gemini Pro	$7	120ms	SOC 2	Email + API
Self-hosted	Llama 2	$0 (infra)	200ms	Custom	Internal

Run parallel tests:

Deploy candidates in sandbox (test environment only)
Run your top 50 use-case queries through each
Measure accuracy, latency, cost
Gather user feedback

Phase 3: Migration Strategy (1 week)

Choose your approach:

Option A: Big Bang (All at once)

Pros: Quick, simple
Cons: Riskier; harder to troubleshoot
Best for: Non-critical workloads, low-complexity migrations

Option B: Phased (By module/use case)

Pros: Lower risk; iterate feedback
Cons: Longer timeline; complexity managing multiple providers
Best for: Production systems; high-impact use cases

Option C: Parallel (Both providers running)

Pros: Safest; 100% rollback capability
Cons: Most costly; requires comparison logic
Best for: Critical systems; high-value migrations

Recommended: Phased — Balance risk and speed.

Phase 4: Testing & Validation (2-4 weeks)

Pre-migration checklist:

New provider SDK integrated in staging environment
API keys and credentials secured in vault
Monitoring alerts configured (latency, errors, costs)
Fallback mechanism tested (auto-revert to old provider)
Team training completed

Validation tests:

Run full test suite through new provider
Measure quality against baseline (accuracy, tone, safety)
Run load test (ramp-up usage gradually)
User acceptance testing (stakeholders try it)

Go/no-go gate:

Does new provider meet performance targets?
Cost savings within expected range?
Compliance and security validated?
Team confident in stability?

Phase 5: Cutover (1-2 days)

Execution:

Announce — Notify users of planned migration
Enable — Flip traffic to new provider (start at 10% → 50% → 100%)
Monitor — Watch error rates, latency, costs in real-time
Support — On-call team ready to troubleshoot
Rollback plan — If issues occur, revert to previous provider

Rollback criteria:

Error rate exceeds 5%
Average latency >2x baseline
Cost >10% over forecast
Quality degradation >10%

Phase 6: Optimization & Cleanup (1-2 weeks)

After successful cutover:

Monitor production for stability (7-14 days)
Decommission old provider credentials
Document lessons learned
Fine-tune new provider settings (temperature, max tokens, etc.)
Update team documentation and runbooks

Cost Impact Analysis

Calculate True Cost Savings

Not just API prices:

Factor	Impact	Calculation
API Cost	Primary	Monthly token usage × $/1M tokens
Infrastructure	Secondary	Hosting, monitoring, redundancy
Team Time	Often overlooked	Hours × hourly rate (migrations are expensive in labor)
Error Handling	Quality cost	Cost of errors × error rate delta
Learning Curve	Temporary	Slower responses while team learns new provider

Example Migration ROI:

Current cost: $50K/month (OpenAI)
Target cost: $20K/month (Anthropic) = $360K/year savings
Migration effort: 200 hours × $150/hr = $30K
Payback period: 1 month
12-month ROI: $360K - $30K = $330K saved

Risk Management

Technical Risks

Risk	Mitigation
Model accuracy drops	Phased rollout; side-by-side comparison; fast rollback
API incompatibility	Test SDK thoroughly; abstract provider logic in code
Latency increases	Pre-test under load; have fallback provider; adjust timeouts
Cost overruns	Set hard limits in API keys; alert on threshold breach

Organizational Risks

Risk	Mitigation
User disruption	Communicate changes; gather feedback; iterate
Knowledge loss	Document new provider quirks; train team; create runbooks
Vendor lock-in again	Build abstraction layer; plan multi-vendor from start
Compliance issues	Audit new provider before cutover; update data agreements

Vendor-Specific Considerations

OpenAI → Anthropic

API is similar (chat, embeddings, vision)
Expect 10-20% speed difference (Anthropic slower on code, better on reasoning)
Test on your specific use cases before committing

Commercial → Self-Hosted

Higher upfront infrastructure cost, but lower per-token cost at scale
Requires DevOps expertise (hosting, scaling, monitoring)
Best for: High-volume workloads (>100M tokens/month) or compliance-critical

Commercial → Open-Source (Llama, Mistral, etc.)

Much cheaper but quality gap narrows yearly
Requires fine-tuning for domain-specific tasks
Best for: Specialized workloads (code gen, domain tasks); cost-constrained projects

Monitoring Post-Migration

Key Metrics to Track (4-week window)

Quality/Accuracy
- Error rate (vs baseline)
- Customer satisfaction (surveys or NPS)
- Domain-specific metrics (code correctness, reasoning quality)
Performance
- API latency (p50, p95, p99)
- Availability/uptime
- Cost per inference
User Behavior
- Adoption rate (% using new provider)
- Fallback rate (% reverting to old provider)
- User complaints or issues
Business Impact
- Total cost (including infrastructure)
- ROI vs projection
- Team productivity changes

Alerting Rules

Set alerts for:

Error rate >5% above baseline
Latency spike >1.5x average
Cost >10% over forecast
Customer complaints spike

Rollback Strategy

When to Rollback

Quality issues persist after 24-48 hours
Unrecoverable technical failures
Cost significantly exceeds forecast
Customer complaints indicate major problems

How to Rollback

Detection — Automated alerts trigger (or manual decision)
Decision — Incident commander approves rollback
Execution — Flip traffic back to previous provider (instant)
Validation — Verify system stability within 30 minutes
Communication — Notify users of issue and resolution

Post-Rollback

Root cause analysis (why did migration fail?)
Retry only after addressing root cause
Consider intermediate steps (more testing, longer phased rollout)

Vendor Evaluation & Scoring — Evaluate which providers to consider
Blueprints — Implement migrations using templates
AI Spend Dashboard — Monitor costs during and after migration

Next Steps

Map current state — What providers and models are you using?
Define requirements — Cost, performance, compliance targets
Identify candidates — Which alternatives meet your needs?
Run proof-of-concept — Test top 2-3 candidates in sandbox
Create migration plan — Timeline, team, go/no-go gates
Execute phased rollout — Start small, expand gradually
Monitor and optimize — Track costs, quality, and performance