Choosing an AI vendor is not about picking the fanciest model. It’s about matching your actual product needs with the right technology and business trade-offs.
Product teams often rush into AI vendor selection based on hype or superficial metrics. The uncomfortable reality is this: the best AI model on the market is not always the best choice for your product. Your actual job is to evaluate vendors on a comprehensive set of criteria — technical, business, risk, and integration — that align with your product’s needs and Indian context.
This lesson walks you through a practical, hands-on approach to vendor evaluation, technology testing, and integration planning. It is grounded in the real frameworks and exercises we use at Pragmatic Leaders with companies like 91mobiles.
Vendor evaluation is a multi-dimensional trade-off
When evaluating AI vendors, the trap is to focus solely on one dimension — model quality or pricing. But the truth is that vendor selection is a multi-dimensional decision. You must balance:
- Model quality: accuracy, coherence, and relevance of output
- API reliability: uptime, error handling, and rate limits
- Performance: response times and throughput under load
- Scalability: ability to handle your volume and peak traffic
- Security and privacy: compliance with data protection laws
- Developer experience: quality of SDKs, documentation, and tooling
- Customization: fine-tuning and prompt optimization capabilities
- Pricing model: cost predictability and value for money
- Contract terms: flexibility, lock-in risk, and support SLAs
- Roadmap alignment: vendor’s future capabilities matching your needs
- Financial stability and geographic coverage: vendor viability and latency in India
- Partnership potential: strategic value beyond just technology
No vendor is perfect on all dimensions. Your job is to weigh these based on your product’s priorities and risk tolerance.
Tier 1 LLM Providers for Indian Mobile Commerce
The leading enterprise-ready LLM vendors today are:
| Vendor | Models | Notes |
|---|---|---|
| OpenAI | GPT-4-turbo, GPT-4o, GPT-3.5-turbo | Industry leader with broad adoption and integration ecosystem |
| Anthropic | Claude-3-sonnet, Claude-3-haiku, Claude-3-opus | Focus on safety and alignment, emerging enterprise traction |
| Gemini Pro, Gemini Ultra, PaLM 2 | Strong enterprise presence, deep integration with Google Cloud |
These vendors offer production-grade APIs, SLAs, and global infrastructure. India availability, rate limits, and pricing vary and must be evaluated carefully.
Tier 2 Emerging and Specialized Vendors
| Vendor | Focus | 91mobiles Fit Status |
|---|---|---|
| Cohere | Enterprise text generation and embeddings | Evaluate / Monitor / Exclude |
| Together AI | Open source model hosting | Evaluate / Monitor / Exclude |
| Mistral AI | European AI with strong reasoning | Evaluate / Monitor / Exclude |
These vendors may offer niche advantages such as cost benefits, open-source flexibility, or regional data compliance. Monitor their maturity before committing.
The Vendor Evaluation Matrix: a structured approach
Use a weighted scoring matrix to evaluate each vendor on key criteria. This forces you to quantify trade-offs clearly.
| Criterion | Weight | Description |
|---|---|---|
| Model Quality | 25% | Accuracy, coherence, domain fit |
| API Reliability | 20% | Uptime, consistency, error handling |
| Performance | 15% | Response time, throughput |
| Scalability | 15% | Rate limits, volume handling |
| Security & Privacy | 10% | Data protection, compliance |
| Documentation | 5% | API docs, examples, support |
| Developer Experience | 5% | SDKs, tooling, ease of use |
| Model Customization | 5% | Fine-tuning, prompt optimization |
Each criterion is scored 1-10 per vendor, multiplied by weight, and summed for a total technical score.
Similarly, evaluate business criteria:
| Criterion | Weight | Description |
|---|---|---|
| Pricing Model | 25% | Cost predictability, value |
| Contract Terms | 20% | Flexibility, exit clauses, lock-in |
| Support Quality | 15% | Responsiveness, expertise |
| Roadmap Alignment | 15% | Future capabilities matching needs |
| Financial Stability | 10% | Vendor viability |
| Geographic Coverage | 10% | India presence, latency |
| Partnership Potential | 5% | Strategic relationship value |
Vendor evaluation meeting at 91mobiles
You (PM): “We’ve completed our scoring on technical and business factors. OpenAI leads on quality but lags on pricing predictability.”
Rahul (Product Lead): “Anthropic scores well on support but has regulatory uncertainties for India.”
Neha (Engineering): “Google’s India data center gives them a latency edge, which matters for real-time features.”
You (PM): “Let’s also assess risks before finalizing.”
Balancing vendor strengths against risks and integration complexity
Risk assessment is non-negotiable
Every vendor carries risks that impact your product delivery.
| Risk Type | Description |
|---|---|
| Technology Risk | Model quality degradation, API instability |
| Business Risk | Vendor financial health, acquisition risk |
| Regulatory Risk | Compliance with Indian data privacy and AI regulations |
| Competitive Risk | Vendor’s market position and threat from new entrants |
You score each risk 1 (low) to 10 (high) per vendor and document key risk factors. This informs mitigation planning.
Practical technology comparison: test with your use cases
Theoretical scores only go so far. You must run hands-on tests with your core scenarios.
Content generation quality test
For example, generate a mobile comparison article:
"iPhone 15 Pro vs Galaxy S24 Ultra for content creators"
Evaluate outputs on:
- Technical accuracy (1-5)
- Brand voice consistency (1-5)
- Content structure (1-5)
- SEO optimization (1-5)
Rank overall quality out of 20.
Performance benchmarking
Measure response times with multiple test runs. Target sub-3-second latency for 95% of requests.
Token usage and cost analysis
Track input/output tokens and calculate cost per article. This feeds into your pricing model evaluation.
Integration complexity: beyond the API call
Evaluate the ease of integrating each vendor’s APIs into your platform:
- SDK quality and maturity
- Documentation clarity
- Error handling mechanisms
- Rate limiting policies
- Authentication flows
- Estimated development effort (in days)
These factors directly affect your engineering timeline and operational risk.
Multi-provider strategies: hedging your bets
Using a primary vendor with fallback providers increases reliability but adds complexity and cost.
Options include:
- Primary + fallback with automatic failover
- Splitting traffic across providers for load balancing
- A/B testing vendors to compare outcomes
Each approach demands custom routing logic and monitoring.
Pricing model comparison and hidden costs
Compare volume-based pricing tiers across vendors for your projected usage.
Also account for:
- Infrastructure: monitoring, caching, load balancing
- Operational: model management, quality control, vendor management
- Risk mitigation: multi-provider setup, legal compliance
These hidden costs can significantly impact your total cost of ownership.
Negotiation levers and contract terms
Successful vendor negotiations optimize:
- Volume discounts and committed usage rates
- Service level agreements (uptime, response times, support)
- Flexibility for rate limit increases and model upgrades
- Exit clauses and data retention policies
Strong contracts reduce lock-in risk and operational surprises.
- Select three AI vendors relevant to your product domain.
- Fill out the technical and business evaluation matrices with publicly available data and your team’s research.
- Conduct a hands-on content generation test with a core user scenario.
- Estimate integration effort and hidden operational costs.
- Draft a risk assessment for each vendor.
- Prepare a recommendation with primary and fallback options, supported by your scoring.
Real-world example: 91mobiles AI vendor evaluation
At 91mobiles, the PM team applied this framework to select their AI partner for content generation.
They found:
- OpenAI delivered the highest content quality and fastest response times but was costlier.
- Anthropic scored well on safety features and support but had limited API rate limits.
- Google offered competitive pricing and better latency in India thanks to local data centers.
A multi-provider fallback strategy was recommended to balance cost, quality, and reliability.
Test yourself: Vendor selection scenario
You are the PM at a Series B Indian mobile commerce startup. Your team must select an AI vendor to power product descriptions and user reviews. You have shortlisted OpenAI, Anthropic, and Google. You have data on model quality, pricing, integration complexity, and risk profiles.
The call: Which vendor do you choose as primary, which as fallback, and how do you justify your selection to leadership?
Your reasoning:
You are the PM at a Series B Indian mobile commerce startup. Your team must select an AI vendor to power product descriptions and user reviews. You have shortlisted OpenAI, Anthropic, and Google. You have data on model quality, pricing, integration complexity, and risk profiles.
Your task: Which vendor do you choose as primary, which as fallback, and how do you justify your selection to leadership?
your reasoning:
Where to go next
- If you want to master AI product strategy: AI Product Strategy
- If you want frameworks for user research in AI: User Research Methods
- If you want to deepen your PM technical skills: Technical Product Management Fundamentals
- If you want to refine your negotiation skills: Contract Negotiation for PMs
PL alumni now work at Flipkart, Razorpay, Swiggy, PhonePe, and 30+ other companies.