The conversation around enterprise AI has shifted from "does it work?" to "can it scale economically?" As companies move generative AI from prototypes to production, they're discovering that platform choices made during experimentation often become liabilities at scale like vendor lock-in, unpredictable costs, and limited flexibility start constraining growth rather than enabling it.

This technical blog presents a case study of migrating InterWiz, an AI-powered recruitment platform, from OpenAI to Amazon Bedrock. The results: 90% cost reduction, 55% faster response times, and 99.9% uptime through multi-provider architecture. More importantly, we'll detail the exact seven-phase framework used to execute this migration with minimal risk.

If you're evaluating whether your current AI platform can scale with your business, this blueprint will show you how to systematically assess, plan, and execute a migration that transforms your AI infrastructure into a competitive advantage.

Challenges with Single-Provider AI Platforms

The Hidden Costs of Vendor Lock-In

When enterprises initially adopt platforms like OpenAI, the focus is on capabilities: can the model handle our use case? But as workloads scale from thousands to millions of API calls, a different set of challenges emerges—challenges that can fundamentally limit your ability to grow.

1. Vendor Lock-In and Limited Flexibility

Building your entire AI strategy around a single model family means you're dependent on one provider's pricing decisions, availability, and product roadmap. If that provider changes their API, adjusts pricing, or experiences service disruptions, you have no alternatives. Your engineering team has optimized everything for one model's behavior, making it costly and risky to switch.

2. Data Privacy and Compliance Concerns

For enterprises, where your data goes matters as much as what the AI can do with it. CISOs need answers: Is the data used for model training? Does it leave specific geographic regions? Does the platform meet SOC 2, HIPAA, or other compliance requirements? For many organizations, these aren't negotiable requirements—they're table stakes. Platforms that can't provide clear compliance certifications become non-starters.

3. Cost Unpredictability at Scale

Early pilots might cost $500/month. But when you scale to production with real user traffic, those costs can explode to $50,000/month or more—often with little warning. Without features like prompt caching or the ability to select cheaper models for simpler tasks, your unit economics can deteriorate rapidly. For InterWiz, OpenAI costs were consuming 40% of their cost per interview, making it impossible to scale profitably.

4. Performance and Latency Issues

User adoption drops immediately when AI responses are slow. If your platform can't deliver sub-second response times consistently, features that look impressive in demos become frustrating in production. High latency isn't just a technical metric—it's a business constraint that limits which use cases are viable.

5. Integration and Customization Limitations

Enterprise systems don't exist in isolation. Your AI platform needs to integrate seamlessly with existing AWS infrastructure, security policies, monitoring tools, and data pipelines. Platforms that require custom integrations or don't support enterprise features like VPC endpoints, private networks, or fine-grained access controls add significant operational overhead.

Why Amazon Bedrock?

Amazon Bedrock isn't just another GenAI platform—it's designed specifically to address the limitations enterprises face when scaling AI workloads. Here's how it solves the challenges outlined above.

Multi-Model Flexibility Through a Single API

Bedrock provides access to multiple frontier models—Claude (Anthropic), LLaMA (Meta), Cohere, Amazon Titan, and Amazon Nova—all through a unified API. This means you can benchmark different models, assign them to different use cases based on their strengths, and switch between them without re-architecting your application. Need advanced reasoning? Use Claude. Need maximum cost efficiency? Use LLaMA. The flexibility is built in from day one.

AWS-Native Integration

Because Bedrock is part of the AWS ecosystem, integration with existing infrastructure is seamless. If you're already using AWS services—Lambda, ECS, VPCs, IAM, CloudWatch—Bedrock plugs in natively. There's no need to manage external API keys, configure complex authentication flows, or build custom monitoring solutions. For teams already on AWS, this cuts deployment time from weeks to days.

Enterprise-Grade Compliance and Security

Bedrock comes with the compliance certifications enterprises require: SOC 2, HIPAA, GDPR compliance, and more. Data stays within your AWS environment and can be restricted to specific regions. You get fine-grained IAM controls, VPC endpoint support, and the ability to run models in private subnets. Your data never leaves your control, and you can prove it to auditors.

Cost Optimization at Scale

Bedrock includes features specifically designed to reduce costs at scale:

Prompt caching can reduce costs by up to 90% and improve latency by 85% for repeated context

Model selection flexibility lets you route simple tasks to cheaper models while reserving expensive models for complex reasoning

Transparent pricing with no surprise bills—you know exactly what you're paying per token

Production-Ready from Day One

Unlike platforms optimized for experimentation, Bedrock is built for production workloads. You get enterprise SLAs, built-in monitoring through CloudWatch, and the reliability of AWS infrastructure. When you're ready to scale, the platform scales with you—no need to rebuild for production standards.

The InterWiz Migration Case Study

About InterWiz

InterWiz is an AI-powered recruitment platform that transforms how companies conduct candidate screenings. Instead of recruiters spending hours on repetitive initial interviews, InterWiz conducts structured, consistent interviews at scale—asking follow-up questions, probing for deeper insights, and generating comprehensive candidate evaluations.

For recruiters, this means reducing time-to-hire while improving hiring quality through standardized, unbiased assessments. For candidates, it means flexible interview scheduling and a consistent experience regardless of which recruiter they're assigned to.

The Problem: Cost, Latency, and Scale

InterWiz's platform was built entirely on Azure OpenAI GPT-4 Turbo, which handled three critical functions:

Question generation tailored to specific roles and seniority levels

Dynamic follow-ups based on candidate responses

Interview evaluation with scoring and detailed feedback

The system worked functionally, but faced three critical constraints:

Unsustainable costs: GPT-4 Turbo was consuming 40% of the cost per interview—approximately $0.25 per interview. At prototype scale, this was manageable. But as InterWiz scaled to thousands of interviews monthly, these costs became prohibitive and made aggressive growth economically unviable.

Latency bottlenecks: As a real-time interview platform, response time directly impacts user experience. With GPT-4 Turbo averaging ~850ms per response, candidates experienced noticeable delays during conversations. In a live interview scenario, even a one-second pause feels unnatural and damages the conversational flow that makes AI interviews effective.

Limited optimization flexibility: Being locked into a single model meant InterWiz couldn't optimize different interview modules independently. They needed the ability to use faster, cheaper models for simpler tasks while reserving more powerful models for complex reasoning.

The Migration Challenge

The goal was clear but demanding: reduce costs by at least 60%, improve response times to under 500ms, and maintain or improve interview quality—all while conducting thousands of live interviews without disruption.

This wasn't about marginal optimization. InterWiz needed a platform that could deliver the same quality at a fraction of the cost and latency, while providing flexibility to continuously optimize their interview pipeline as new models became available.

The 7-Phase Migration Framework

This is where theory meets execution. Here's the exact framework we used to migrate InterWiz from OpenAI to Bedrock, broken down into seven systematic phases. Each phase builds on the previous one, reducing risk while maintaining service quality.

The diagram above illustrates the complete migration journey. Let's walk through each phase in detail.

Phase 1: Collect Information & Define Success

Before writing a single line of code, you need complete clarity on three things: what's broken, what you have, and what success looks like.

Document Pain Points

For InterWiz, we identified three critical issues:

Cost: GPT-4 Turbo consuming 40% of per-interview costs ($0.25/interview)

Latency: Average 850ms response time impacting real-time interview experience

Customization limits: Single model preventing role-specific optimization

Unit economics: Unsustainable cost structure blocking platform scaling

Establish Current Baseline

We documented InterWiz's existing architecture:

Platform: Azure OpenAI GPT-4 Turbo for all AI functions

Three core use cases:

Question generation based on job role and candidate background

Dynamic follow-up questions during live interviews

```
Post-interview evaluation and scoring
```

Current metrics:

```
Average response time: ~850ms
```
```
Cost per interview: $0.25
```

Monthly interview volume: ~8,000 interviews

Define Clear Success Criteria

Success wasn't subjective. We established measurable targets:

Cost: Reduce AI expense ratio from 40% to under 10% (minimum 60% reduction)

Latency: Achieve sub-500ms average response time

Quality: Maintain or improve interview quality scores and candidate feedback

Reliability: Zero downtime during migration

Why This Matters: Without clear baseline metrics and success criteria, you can't objectively evaluate whether the migration worked. These numbers become your decision framework throughout the entire process.

Phase 2: Select Candidate Models

With requirements documented, the next step is identifying which Bedrock models are worth testing. This isn't about picking one "best" model—it's about finding models that match your specific use case requirements.

Selection Criteria for InterWiz

We evaluated models across three dimensions critical to interview applications:

1. Instruction Following (Critical for Interview Structure)

InterWiz interviews follow specific formats with role-based question sequences. Models need to adhere strictly to instructions without deviating from the interview flow.

From benchmark analysis:

LLaMA 3.3 70B: 92.1% on IFEval (instruction following)

Claude 3.5 Sonnet: 90.2% on Tool Use performance

```
GPT-4: 84.6% baseline
```

2. Conversational AI & Reasoning

Interviews require natural conversation flow and the ability to generate contextually appropriate follow-up questions.

Claude 3.5 Sonnet: 69.4% on reasoning benchmarks + 90.20% tool use for structured outputs

LLaMA 3.3 70B: 92.1% instruction adherence for structured interview formats

3. Cost Optimization

Both candidate models were significantly more cost-effective than GPT-4:

LLaMA 3.3 70B: Best price-performance ratio

Claude 3.5 Sonnet: More expensive than LLaMA but still much cheaper than GPT-4

The Decision: Test Both Models

Rather than picking one model, we decided to test both for different InterWiz modules:

Claude 3.5 Sonnet: For complex reasoning tasks requiring nuanced follow-ups

LLaMA 3.3 70B: For structured question generation and evaluation where cost efficiency matters most

This multi-model approach is one of Bedrock's key advantages—you're not forced to optimize everything for a single model's strengths and weaknesses.

Phase 3: Prompt Optimization

Here's a critical lesson: prompts are not portable between models. A prompt that works perfectly for GPT-4 will often produce poor results with Claude or LLaMA. Each model has been trained differently and responds to different prompting patterns.

Step 1: Bedrock Prompt Optimization Tool

AWS provides a prompt optimization interface within the Bedrock console that lets you:

Test multiple prompt variants side-by-side

```
Compare outputs across different models
```

Use test variables to simulate real scenarios

For InterWiz's interview question generation, we started with the original GPT-4 prompt:

You are an interviewer.
Your role is to ask candidates questions about their [module_name] in previous jobs in a soft conversational manner.
Here are steps you have to follow:
1. Use a professional tone.
2. Don't ask too many questions at once, ask one by one.
3. Ask [#amount_of_questions] questions in total and cover everything. Don't make the interview section very big.
4. Think before asking a question.
5. Keep transitions brief and natural. Remove greetings, role mentions, and specific context references.

This prompt worked for GPT-4 but needed refinement for Bedrock models.

Step 2: LLM-Guided Enhancement

Rather than manually iterating on prompts, we used an LLM-guided optimization approach:

Fed the AWS-optimized prompt along with official prompt engineering guides to each model:

Claude: Anthropic's prompt engineering best practices

LLaMA: Meta's LLaMA-specific optimization techniques

Asked each model to self-improve its own prompt based on its documentation:
"Based on these best practices, how can this prompt be improved for optimal performance with your model?"

The models generated refined prompts incorporating model-specific techniques

Example Enhancement for Claude:

The optimized Claude prompt included structured XML tags and clearer role definitions:

<task>
You are a professional interviewer conducting a focused interview about a candidate's experience with <module> in their previous roles.
</task>


<interview_guidelines>
- Maintain a professional yet approachable tone that incorporates occasional wit to keep the conversation engaging
- Ask exactly <#questions> questions in total
- Ask one question at a time, focusing each question on different aspects of the candidate's experience with <module>
- Focus each question on different aspects without unnecessary elaboration
- Keep the interview concise and focused—avoid unnecessary elaboration
- Structure questions to progress logically from general experience to specific technical details
- Skip traditional interview formalities (no introductions, greetings, or exit statements between questions)
</interview_guidelines>


<question_types>
1. Experience-based questions (e.g., "How did you implement [module_name] in your previous role?")
2. Achievement-based questions (e.g., "What improvements did you make to [module_name] processes?")
3. Technical knowledge questions (e.g., "How would you describe to [module_name]?")
4. Problem-solving questions (e.g., "What was the most difficult aspect of working with [module_name]?")
</question_types>

Begin the interview directly with your first question about <module>. After receiving a response, continue with subsequent questions until you've asked exactly <#questions> questions. Maintain a conversational flow throughout the interview.

The enhanced version uses Claude's preferred XML structure and provides more explicit guidance.

Results of Prompt Optimization

After optimization:

LLaMA 3.3: Instruction following improved to near-perfect adherence to interview structure

Claude 3.5: Generated more natural follow-up questions with better contextual awareness

Both models now matched or exceeded GPT-4's conversational quality

Key Takeaway: Investing time in model-specific prompt optimization pays massive dividends. Don't assume prompts will work across models—test and refine for each one.

Phase 4: Comprehensive Model Evaluation

With optimized prompts in hand, it's time for rigorous evaluation across three dimensions: latency, quality, and cost.

Latency Testing

We measured average response times across 100+ test requests for each model:

Model	Average Response Time	vs GPT-4
LLaMA 3.3	~450ms	55% faster
OpenAI GPT-4	~850ms	Baseline
Bedrock Claude	~2000ms	Slower

Key Finding: LLaMA 3.3 delivered the fastest response times, making it ideal for real-time interview scenarios where every millisecond of latency impacts user experience.

Quality Evaluation: Using AWS Bedrock Model Evaluation

This is where AWS Bedrock's evaluation capabilities become invaluable. Rather than manually reviewing outputs, we used Bedrock's automated evaluation framework.

Step 1: Generate Evaluation Dataset

We created a representative test dataset consisting of:

50 real interview scenarios covering different roles (software engineer, product manager, data scientist)

Diverse candidate backgrounds (junior, mid-level, senior)

Various interview modules (technical skills, leadership, problem-solving)

Each scenario included:

Candidate profile (role, experience level)

```
Interview module being tested
```
```
Expected interview flow
```
```
Sample candidate responses
```

Step 2: Configure Bedrock Evaluation Metrics

AWS Bedrock provides built-in evaluation metrics for assessing model outputs. We configured evaluations across multiple dimensions:

Quality Metrics:

Correctness (0-1 scale): Does the output match expected interview structure?

Professional style and tone (0-1 scale): Is the language appropriate for professional interviews?

Following instructions (0-1 scale): Does the model adhere to the specified interview guidelines?

Responsible AI Metrics:

Harmfulness (0-1 scale): Does the output contain harmful content?

Refusal (0-1 scale): Does the model inappropriately refuse valid requests?

Stereotyping (0-1 scale): Does the output contain biased or stereotypical language?

Step 3: Run Automated Evaluations

We ran the evaluation dataset through both candidate models using Bedrock's evaluation jobs:

LLaMA 3.3 70B Evaluation Results:

Quality Metrics:

Correctness: 0.70 (Good adherence to expected outputs)

Professional style and tone: 0.95 (Excellent professional communication)

Following instructions: 1.00 (Perfect instruction adherence)

Responsible AI Metrics:

```
Harmfulness: 0.00 (No harmful content)
```

Refusal: 0.00 (No inappropriate refusals)

Stereotyping: 0.00 (No stereotypical language)

Claude 3.5 Sonnet Evaluation Results:

Quality Metrics:

Correctness: 0.40 (Lower factual accuracy in initial tests)

Professional style and tone: 0.95 (Excellent professional communication)

Following instructions: 1.00 (Perfect instruction adherence)

Responsible AI Metrics:

```
Harmfulness: 0.00 (No harmful content)
```

Refusal: 0.00 (No inappropriate refusals)

Stereotyping: 0.00 (No stereotypical language)

Analysis: Both models scored perfectly on instruction following and responsible AI metrics, which were critical for InterWiz. LLaMA 3.3 had higher correctness scores, making it particularly well-suited for structured interview generation. Claude excelled in professional tone and could be valuable for more complex reasoning tasks.

Cost Analysis

We calculated per-interview costs based on typical token usage (30,000 input + 5,000 output tokens per complete interview):

Model	Cost per Interview	vs GPT-4
Meta LLaMA 3.3	$0.025	10x cheaper
Claude 3.7 Sonnet	$0.165	1.5x cheaper
OpenAI GPT-4	$0.25	Baseline

Key Finding: LLaMA 3.3 offered the most significant cost reduction for InterWiz's high-volume interview workloads.

Phase 5: Compare & Select Models

With comprehensive evaluation data, we created a decision matrix matching models to specific InterWiz modules:

Decision Framework:

Use Case	Priority	Selected Model	Rationale
Question Generation	Cost + Structure	LLaMA 3.3	Perfect instruction following, 10x cost savings, high volume
Follow-up Questions	Reasoning + Speed	LLaMA 3.3	Fast response time critical for real-time interviews
Interview Evaluation	Quality + Cost	LLaMA 3.3	Strong evaluation accuracy, cost-effective for bulk processing

Final Decision: Deploy LLaMA 3.3 70B as the primary model for all InterWiz modules, with Claude 3.5 Sonnet available as a fallback option for future complex reasoning requirements.

Phase 6: Migration Execution

This is where architectural decisions matter. The goal isn't just to swap API endpoints—it's to build a resilient, multi-provider system that can adapt as new models emerge.

Key Implementation Challenges

1. API Differences: System Message Formatting

OpenAI and Bedrock handle system messages differently:

# OpenAI format
messages = [{
    "role": "system",
    "content": "You are an interviewer named Ava..."
}]

# Bedrock Claude format
messages = [{
    "role": "system",
    "content": [{"text": "You are an interviewer named Ava..."}]
}]

2. Function/Tool Calling Syntax

Tool calling structures differ significantly:

# OpenAI
functions = [{
    "type": "function",
    "function": {
        "name": "end_chat",
        "description": "...",
        "parameters": {...}
    }
}]

# Bedrock
tools = {
    "tools": [{
        "toolSpec": {
            "name": "end_chat",
            "description": "...",
            "inputSchema": {"json": {...}}
        }
    }]
}

3. API Response Structure

Response parsing requires provider-specific handling:

# OpenAI
response.choices[0].message.content
response.choices[0].message.tool_calls

# Bedrock
response['output']['message']['content']
response['output']['message']['toolUse']

Solution: Build an Abstraction Layer

Rather than scattering provider-specific code throughout the application, we built a unified abstraction layer:

class LLMProvider:
    """Abstract interface for LLM providers"""
    
    def format_messages(self, messages):
        """Convert to provider-specific format"""
        raise NotImplementedError
    
    def format_tools(self, tools):
        """Convert tool definitions to provider format"""
        raise NotImplementedError
    
    def call(self, messages, tools=None):
        """Make provider-specific API call"""
        raise NotImplementedError
    
    def parse_response(self, response):
        """Parse provider-specific response"""
        raise NotImplementedError


class BedrockProvider(LLMProvider):
    """Bedrock-specific implementation"""
    # Implementation details...


class OpenAIProvider(LLMProvider):
    """OpenAI-specific implementation"""
    # Implementation details...

Smart Fallback Architecture

To achieve 99.9% uptime, we implemented multi-provider redundancy:

class LLMOrchestrator:
    def __init__(self):
        self.primary = BedrockProvider()
        self.fallback = OpenAIProvider()
    
    def call_with_fallback(self, messages, tools=None):
        try:
            return self.primary.call(messages, tools)
        except Exception as e:
            logger.warning(f"Primary provider failed: {e}")
            logger.info("Switching to fallback provider")
            return self.fallback.call(messages, tools)

This architecture ensures that if Bedrock experiences issues, the system automatically falls back to OpenAI without service disruption.

Migration Rollout Strategy

We didn't flip a switch and migrate everything at once. InterWiz's platform consists of multiple modules (interview execution, evaluation, question generation), each with several sub-modules handling different use cases. We took a progressive, module-by-module approach:

Phase 1: Identify the Low-Risk Starting Point

Analyzed all modules to identify the one with the least complexity and lowest user-facing risk

Selected a single sub-module as the initial migration target

This gave us a controlled environment to validate the entire migration process

Phase 2: Module-by-Module Rollout

Week 1: Migrate first sub-module to Bedrock (100% traffic for that sub-module only, all others remain on OpenAI)

Week 2: Monitor metrics closely—cost, latency, quality, error rates

Week 3: If successful, migrate next sub-module; if issues arise, fix before proceeding

Weeks 4-8: Continue progressive rollout across remaining sub-modules

Phase 3: Full Migration with Fallback

Once all sub-modules migrated, maintain OpenAI as fallback provider for reliability

This ensures 99.9% uptime even if primary provider experiences issues

This granular approach minimized risk by isolating potential issues to individual sub-modules rather than impacting the entire platform. It also allowed us to build confidence incrementally and make adjustments based on real production data before expanding the migration.

Phase 7: Post-Migration Optimization

Migration isn't finished when the code deploys. Continuous monitoring and optimization ensure you capture the full value.

Real-Time Monitoring Dashboard

We implemented comprehensive monitoring across:

```
Cost per interview (tracked daily)
```

Average response latency (p50, p95, p99)

```
Error rates by provider
```
```
Fallback activation frequency
```

Quality metrics from automated evaluations

Continuous Improvement Cycle

Post-migration optimization focused on:

1. Prompt Refinement: As we observed real interview patterns, we continued tuning prompts to improve conversation flow

2. Token Optimization: Analyzed token usage patterns to reduce unnecessary context while maintaining quality

3. Model Updates: AWS regularly releases new models and versions. Our abstraction layer made it easy to test new options (e.g., upgrading to newer LLaMA versions)

4. Cost Analysis: Weekly cost reviews identified opportunities for further optimization, such as using prompt caching for repeated interview templates

Business Outcomes & Results

The migration delivered measurable improvements across every key metric. Here's what changed:

Quantifiable Impact

90% Cost Reduction

Before: $0.25 per interview (GPT-4 Turbo)

```
After: $0.025 per interview (LLaMA 3.3)
```

Annual savings at 100K interviews: ~$22,500

This transformed InterWiz's unit economics from barely sustainable to highly profitable, enabling aggressive growth and market expansion.

55% Latency Improvement

Before: ~850ms average response time (GPT-4)

After: ~450ms average response time (LLaMA 3.3)

Sub-500ms responses made interviews feel more natural and conversational. Candidate feedback improved noticeably, with fewer complaints about awkward pauses during conversations.

99.9% Uptime Achievement

Multi-provider architecture with automatic fallback

```
Zero downtime during migration
```

Resilience against single-provider outages

The abstraction layer meant that if Bedrock experienced issues, the system seamlessly fell back to OpenAI without interrupting active interviews.

Strategic Business Impact

Beyond the immediate metrics, the migration unlocked capabilities that weren't economically viable before:

New Product Features: With costs reduced by 90%, InterWiz could finally invest in role-specific interview customization—tailoring question styles, follow-up patterns, and evaluation criteria for different positions without worrying about cost explosion.

Market Expansion Opportunities: The new cost structure made it feasible to enter price-sensitive markets that were previously off-limits. InterWiz could now profitably serve smaller companies and startups that couldn't afford premium pricing.

Competitive Moat: Multi-model flexibility means InterWiz can adopt new models as they're released without re-architecting the entire platform. As AWS adds models to Bedrock, InterWiz can immediately test and integrate them—staying ahead of competitors locked into single providers.

Improved Developer Velocity: The abstraction layer didn't just enable migration—it accelerated future development. Engineers can now experiment with different models for different features without worrying about tight coupling to specific APIs.

Lessons Learned & Best Practices

Every migration teaches lessons that aren't obvious until you're deep in execution. Here are the critical insights we gained from the InterWiz migration—lessons that will save you time, money, and headaches if you're planning a similar transition.

1. The Most Expensive Model Isn't Always the Best Model

The Critical Insight: GPT-4 Turbo was overkill for InterWiz's use case. We achieved 90% cost savings without sacrificing quality—simply by taking the time to properly evaluate which model actually fit their needs.

The Pattern We See Everywhere: Teams default to the most powerful, expensive model without asking whether their specific use case actually requires that capability.

The Reality: Most production AI use cases don't need the absolute cutting edge. InterWiz needed:

```
Reliable instruction following
```
```
Consistent structured outputs
```
```
Professional conversational tone
```
```
Fast response times
```

LLaMA 3.3 delivered all of this at 10% of the cost. The "best" model isn't the one that tops benchmarks—it's the one that meets your requirements at the lowest cost.

The Practice: Before defaulting to GPT-4 or Claude Sonnet, ask:

What specific capabilities does my use case actually require?

Which models meet those requirements based on real testing?

Am I paying for reasoning capabilities I'm not using?

Run comprehensive evaluations with your real prompts and data. Let objective metrics—not model popularity—drive your selection. This single insight delivered more value than any other optimization in the entire migration.

2. Build the Abstraction Layer from Day One

The Mistake: Most teams start by tightly coupling their application logic to a specific provider's API. This makes sense early on—you're moving fast, and abstractions feel like over-engineering.

The Reality: Without an abstraction layer, you're not just locked into a provider—you're locked into their API structure, response formats, and update cycles. When you eventually need to migrate, you'll be rewriting code across your entire codebase.

The Practice: Create a thin abstraction layer even if you're only using one provider initially. It costs maybe 2-3 days of upfront work but saves weeks or months during migration. Your application code should never directly call OpenAI, Bedrock, or any provider's API—it should call your abstraction layer.

Practical Implementation:

# Bad: Direct provider coupling
response = openai.ChatCompletion.create(...)

# Good: Provider-agnostic abstraction
response = llm_service.generate(messages, config)

This single architectural decision is the difference between a 2-week migration and a 6-month rewrite.

3. Plan for Failures: Multi-Provider Redundancy is Essential

The Mistake: Assuming your primary provider will always be available. Even the best platforms experience outages, rate limiting, or regional issues.

The Reality: Production AI systems need the same redundancy as databases or other critical infrastructure. If your interview platform goes down because OpenAI has an outage, you're losing revenue and damaging customer trust.

The Practice: Design your architecture with automatic failover from day one. Your abstraction layer should support multiple providers, with graceful degradation if the primary fails.

When Bedrock experiences issues, InterWiz automatically falls back to OpenAI. Users never notice—interviews continue uninterrupted. This isn't optional for production systems; it's mandatory.

4. Prompts Are Not Portable—Budget Time for Model-Specific Optimization

The Mistake: Assuming you can copy-paste prompts from GPT-4 to Claude or LLaMA and get similar results.

The Reality: Each model family has been trained differently and responds to different prompting patterns. GPT-4 works well with conversational instructions, Claude prefers structured XML tags, and LLaMA excels with clear step-by-step formatting.

The Practice: Allocate 20-30% of your migration timeline to prompt optimization. Don't just test—systematically optimize for each model using:

Official prompt engineering guides from each provider

Side-by-side testing with real use cases

LLM-guided enhancement (have each model optimize its own prompts)

For InterWiz, prompt optimization made the difference between mediocre results and production-ready quality. A poorly optimized prompt can make a superior model perform worse than an inferior one.

5. Monitor Everything in Real-Time

The Mistake: Migrating based on benchmark tests, then assuming production performance will match.

The Reality: Real-world usage patterns differ from test scenarios. Costs can spike unexpectedly, latency can degrade under load, and quality issues might only appear with specific user inputs.

The Practice: Implement comprehensive monitoring before migration:

Cost tracking: Per-request costs, daily spend, cost per business unit (e.g., per interview)

Latency metrics: p50, p95, p99 response times

Quality signals: Error rates, fallback activation, user feedback

Provider health: Track which provider is being used and why

Set up alerts for anomalies. If your daily AI spend suddenly doubles, you need to know within hours, not weeks.

6. Start Small, Validate, Then Scale

The Lesson: We migrated module-by-module, starting with the lowest-risk sub-module. This incremental approach meant we could validate the entire process—prompts, costs, latency, quality—before expanding.

The Practice:

Identify your least critical, lowest-complexity module

```
Migrate just that module to production
```
```
Monitor for 1-2 weeks
```

Only proceed to the next module after validating success

This might feel slower, but it's actually faster than migrating everything at once and then spending months debugging issues across your entire platform.

7. Migration is Architectural, Not Just Technical

The Final Lesson: The goal isn't just to swap APIs—it's to build a resilient, adaptable AI architecture that can evolve as new models emerge.

The abstraction layer, multi-provider support, and monitoring infrastructure we built for InterWiz aren't just migration tools. They're the foundation for continuously optimizing their AI stack as better models become available.

When AWS releases a new model or Meta ships LLaMA 4, InterWiz can test it in production within days, not months. That architectural flexibility is the real competitive advantage.

Conclusion

When Should You Consider Migration?

Not every organization needs to migrate from OpenAI to Bedrock. But if you're experiencing any of these indicators, it's time to seriously evaluate your options:

Cost is becoming prohibitive: AI expenses are consuming 30%+ of your product costs, making unit economics unsustainable

Scale is limited by a single provider: You can't experiment with different models for different use cases, forcing suboptimal compromises

Compliance requirements aren't met: You need specific data residency, enterprise certifications, or security controls your current provider doesn't offer

Latency is constraining product experience: Response times are too slow for your real-time use cases

Vendor lock-in creates strategic risk: Your entire AI roadmap depends on one provider's pricing and availability decisions

If any of these sound familiar, the migration framework we've outlined provides a systematic, low-risk approach to transition.

The Path Forward

The InterWiz case study demonstrates that migration isn't just about changing APIs—it's about building a resilient AI architecture that can adapt as new models emerge and business requirements evolve. The 90% cost reduction and 55% latency improvement are impressive, but the real value is the strategic flexibility to continuously optimize without being locked into a single provider's roadmap.