How many vendors should I evaluate?

Three to five in depth. Fewer than three does not give you enough comparison data. More than five makes the evaluation take too long and delays your decision. Start with a broad scan, then narrow to 3-5 for detailed evaluation.

Should I evaluate open-source models alongside commercial APIs?

Yes, if self-hosting is a realistic option for your team. Include at least one open-source model in your evaluation as a baseline. Even if you choose a commercial API, understanding the open-source alternative gives you negotiating leverage and a fallback option.

How often should I re-evaluate vendors?

Annually, or whenever a major new model is released that could significantly impact your use case. The AI market evolves quickly, and the best vendor today may not be the best choice in twelve months.

What if no vendor meets all my requirements?

Rank your requirements by priority and accept trade-offs on lower-priority items. If no vendor meets your hard requirements, consider whether your requirements are realistic or whether you need a hybrid approach (e.g., different vendors for different use cases). ---

AI Vendor Comparison Template

What This Template Does

Choosing an AI vendor, model, or API is one of the highest-leverage decisions in AI product development. The wrong choice locks you into a provider that may not meet your performance, cost, or compliance requirements -- and switching providers later is expensive because prompts, evaluation suites, and integration code are often vendor-specific. Yet most teams make this decision based on hype, a single benchmark, or whichever model the engineering team tried first.

This template provides a structured framework for comparing AI vendors across the dimensions that actually matter for production use: task-specific performance, total cost of ownership, reliability and uptime, data privacy and compliance, integration complexity, and long-term viability. It includes evaluation matrices, scoring rubrics, and a decision framework that forces you to weight your priorities before comparing options.

Direct Answer

An AI Vendor Comparison is a structured evaluation of AI providers, models, and APIs across multiple dimensions including performance on your specific tasks, total cost, reliability, compliance, and integration effort. This template provides the complete framework to evaluate vendors objectively and make a defensible selection decision.

Template Structure

1. Requirements Definition

Purpose: Before comparing vendors, define what you need. This prevents the evaluation from being biased toward whichever vendor demos best.

## Requirements

### Use Case Summary
- **Product**: [Product name]
- **AI capability needed**: [What the AI will do in 1-2 sentences]
- **Input type**: [Text / Images / Audio / Structured data / Multi-modal]
- **Output type**: [Text generation / Classification / Extraction / Embeddings / Other]
- **Volume**: [Expected requests per day/month]
- **Growth projection**: [Expected volume in 6 and 12 months]

### Priority-Weighted Requirements
Assign a weight (1-5) to each requirement based on importance for your use case.

| Requirement | Weight (1-5) | Minimum Threshold | Ideal Target |
|-------------|-------------|-------------------|-------------|
| Task accuracy | [Weight] | [e.g., > 85%] | [e.g., > 95%] |
| Latency (p95) | [Weight] | [e.g., < 5s] | [e.g., < 1s] |
| Cost per request | [Weight] | [e.g., < $0.10] | [e.g., < $0.01] |
| Uptime SLA | [Weight] | [e.g., 99.5%] | [e.g., 99.9%] |
| Data privacy | [Weight] | [e.g., No training on our data] | [e.g., SOC 2 + GDPR] |
| Context window | [Weight] | [e.g., 32K tokens] | [e.g., 128K tokens] |
| Fine-tuning support | [Weight] | [e.g., Available] | [e.g., Managed fine-tuning] |
| Multi-language support | [Weight] | [e.g., English + Spanish] | [e.g., 20+ languages] |
| Integration complexity | [Weight] | [e.g., REST API] | [e.g., SDK + docs + support] |

### Hard Requirements (Pass/Fail)
These are non-negotiable. Any vendor that fails these is eliminated regardless of other scores.

- [ ] [e.g., Data must not be used for training]
- [ ] [e.g., SOC 2 Type II certified]
- [ ] [e.g., Data residency in US/EU]
- [ ] [e.g., Supports our required programming languages]
- [ ] [e.g., Has been in production for > 1 year]

2. Vendor Shortlist

Purpose: Identify 3-5 vendors to evaluate in depth. Do not evaluate more than 5 -- the comparison becomes unwieldy and delays the decision.

## Vendor Shortlist

| Vendor | Model/Product | Why Included | Initial Concerns |
|--------|--------------|-------------|-----------------|
| [Vendor 1] | [Model name] | [Reason for considering] | [Known risks] |
| [Vendor 2] | [Model name] | [Reason for considering] | [Known risks] |
| [Vendor 3] | [Model name] | [Reason for considering] | [Known risks] |
| [Vendor 4] | [Model name] | [Reason for considering] | [Known risks] |

### Hard Requirements Check
| Requirement | Vendor 1 | Vendor 2 | Vendor 3 | Vendor 4 |
|-------------|----------|----------|----------|----------|
| [Requirement 1] | Pass/Fail | Pass/Fail | Pass/Fail | Pass/Fail |
| [Requirement 2] | Pass/Fail | Pass/Fail | Pass/Fail | Pass/Fail |
| [Requirement 3] | Pass/Fail | Pass/Fail | Pass/Fail | Pass/Fail |

**Vendors eliminated by hard requirements**: [List any vendors that fail hard requirements]

3. Performance Evaluation

Purpose: Test each vendor on your actual use case, not generic benchmarks. Public benchmarks tell you how a model performs on academic tasks, not on your specific inputs and quality requirements.

## Performance Evaluation

### Test Dataset
- **Dataset size**: [N test cases]
- **Categories covered**: [List categories that represent your use case]
- **Quality labels**: [How ground truth was established -- human labels, expert review, etc.]
- **Edge cases included**: [N edge cases, representing X% of test set]

### Results

| Metric | Vendor 1 | Vendor 2 | Vendor 3 | Vendor 4 |
|--------|----------|----------|----------|----------|
| Overall accuracy | [%] | [%] | [%] | [%] |
| Accuracy on easy cases | [%] | [%] | [%] | [%] |
| Accuracy on hard cases | [%] | [%] | [%] | [%] |
| Accuracy on edge cases | [%] | [%] | [%] | [%] |
| Latency (p50) | [ms] | [ms] | [ms] | [ms] |
| Latency (p95) | [ms] | [ms] | [ms] | [ms] |
| Latency (p99) | [ms] | [ms] | [ms] | [ms] |
| Throughput (max req/s) | [N] | [N] | [N] | [N] |
| Output quality (human eval) | [1-5] | [1-5] | [1-5] | [1-5] |
| Hallucination rate | [%] | [%] | [%] | [%] |
| Refusal rate (false positives) | [%] | [%] | [%] | [%] |

### Qualitative Assessment
For each vendor, note observations about output quality that metrics do not capture:

**Vendor 1**: [Notes on tone, style, consistency, edge case handling]
**Vendor 2**: [Notes]
**Vendor 3**: [Notes]
**Vendor 4**: [Notes]

4. Cost Analysis

Purpose: Calculate the total cost of ownership, not just the per-request price. Include integration costs, ongoing operational costs, and scaling costs.

## Cost Analysis

### Per-Request Pricing
| Cost Component | Vendor 1 | Vendor 2 | Vendor 3 | Vendor 4 |
|---------------|----------|----------|----------|----------|
| Input tokens (per 1K) | [$] | [$] | [$] | [$] |
| Output tokens (per 1K) | [$] | [$] | [$] | [$] |
| Average cost per request | [$] | [$] | [$] | [$] |
| Fine-tuning cost (if applicable) | [$] | [$] | [$] | [$] |
| Embedding cost (per 1K tokens) | [$] | [$] | [$] | [$] |

### Monthly Cost Projection
| Volume Tier | Vendor 1 | Vendor 2 | Vendor 3 | Vendor 4 |
|------------|----------|----------|----------|----------|
| Current ([N] req/month) | [$] | [$] | [$] | [$] |
| 6 months ([N] req/month) | [$] | [$] | [$] | [$] |
| 12 months ([N] req/month) | [$] | [$] | [$] | [$] |
| Volume discounts available? | [Yes/No] | [Yes/No] | [Yes/No] | [Yes/No] |

### Total Cost of Ownership (12 months)
| Cost Category | Vendor 1 | Vendor 2 | Vendor 3 | Vendor 4 |
|--------------|----------|----------|----------|----------|
| API costs | [$] | [$] | [$] | [$] |
| Integration engineering | [$] | [$] | [$] | [$] |
| Ongoing maintenance | [$] | [$] | [$] | [$] |
| Prompt engineering | [$] | [$] | [$] | [$] |
| Evaluation and monitoring | [$] | [$] | [$] | [$] |
| **Total 12-month TCO** | **[$]** | **[$]** | **[$]** | **[$]** |

5. Reliability and Support

Purpose: Evaluate how reliable each vendor is in production and what support you can expect when things go wrong.

## Reliability and Support

| Dimension | Vendor 1 | Vendor 2 | Vendor 3 | Vendor 4 |
|-----------|----------|----------|----------|----------|
| Published uptime SLA | [%] | [%] | [%] | [%] |
| Historical uptime (last 6 months) | [%] | [%] | [%] | [%] |
| Status page available | [Y/N + URL] | [Y/N + URL] | [Y/N + URL] | [Y/N + URL] |
| Incident communication quality | [1-5] | [1-5] | [1-5] | [1-5] |
| Rate limit headroom | [X vs. your needs] | [X] | [X] | [X] |
| Support tier included | [Community/Email/Dedicated] | | | |
| Support response time (SLA) | [Hours] | [Hours] | [Hours] | [Hours] |
| Dedicated account manager | [Y/N] | [Y/N] | [Y/N] | [Y/N] |
| Migration support offered | [Y/N] | [Y/N] | [Y/N] | [Y/N] |

6. Compliance and Data Privacy

Purpose: Verify that each vendor meets your data privacy and compliance requirements.

## Compliance and Data Privacy

| Requirement | Vendor 1 | Vendor 2 | Vendor 3 | Vendor 4 |
|-------------|----------|----------|----------|----------|
| SOC 2 Type II | [Y/N] | [Y/N] | [Y/N] | [Y/N] |
| GDPR compliant | [Y/N] | [Y/N] | [Y/N] | [Y/N] |
| CCPA compliant | [Y/N] | [Y/N] | [Y/N] | [Y/N] |
| HIPAA compliant (if needed) | [Y/N/NA] | [Y/N/NA] | [Y/N/NA] | [Y/N/NA] |
| Data not used for training | [Y/N] | [Y/N] | [Y/N] | [Y/N] |
| Data residency options | [Regions] | [Regions] | [Regions] | [Regions] |
| Data retention policy | [Duration] | [Duration] | [Duration] | [Duration] |
| DPA available | [Y/N] | [Y/N] | [Y/N] | [Y/N] |
| Sub-processor list published | [Y/N] | [Y/N] | [Y/N] | [Y/N] |
| Right to deletion supported | [Y/N] | [Y/N] | [Y/N] | [Y/N] |

7. Scoring and Decision

Purpose: Apply your priority weights to the evaluation results and make a final decision.

## Weighted Scoring

### Score Each Vendor (1-5 per criterion)
| Criterion | Weight | Vendor 1 | Vendor 2 | Vendor 3 | Vendor 4 |
|-----------|--------|----------|----------|----------|----------|
| Task accuracy | [W] | [1-5] | [1-5] | [1-5] | [1-5] |
| Latency | [W] | [1-5] | [1-5] | [1-5] | [1-5] |
| Cost | [W] | [1-5] | [1-5] | [1-5] | [1-5] |
| Reliability | [W] | [1-5] | [1-5] | [1-5] | [1-5] |
| Compliance | [W] | [1-5] | [1-5] | [1-5] | [1-5] |
| Integration ease | [W] | [1-5] | [1-5] | [1-5] | [1-5] |
| Support quality | [W] | [1-5] | [1-5] | [1-5] | [1-5] |
| Long-term viability | [W] | [1-5] | [1-5] | [1-5] | [1-5] |
| **Weighted Total** | | **[Score]** | **[Score]** | **[Score]** | **[Score]** |

### Decision
- **Recommended vendor**: [Name]
- **Runner-up**: [Name]
- **Rationale**: [2-3 sentences explaining the recommendation]
- **Key risks with recommended vendor**: [List top 2-3 risks]
- **Mitigation plan**: [How to address the key risks]
- **Decision maker**: [Name and role]
- **Decision date**: [Date]

How to Use This Template

Define requirements before looking at vendors. Fill out Section 1 with your product team and engineering lead. Weight the priorities honestly -- if cost is your primary concern, weight it highest. Do not weight everything equally.

Select 3-5 vendors for the shortlist. Include the obvious market leaders and at least one non-obvious option. Eliminate vendors that fail hard requirements immediately.

Run performance evaluation on your actual data. Create a test dataset of 50-200 examples that represent your real use case. Do not rely on public benchmarks, which measure academic tasks that may not reflect your needs.

Calculate total cost of ownership, not just API price. A cheaper API that requires more prompt engineering, has worse accuracy (requiring human review), or has reliability issues may cost more in total.

Verify compliance requirements with your legal team. Do not take vendor marketing at face value. Request their SOC 2 report, read their DPA, and confirm data handling practices directly.

Score vendors using the weighted rubric and make the decision. The highest score should win unless there are qualitative factors that the scoring does not capture. Document those factors explicitly.

Tips for Best Results

Test with real data, not toy examples. Vendor demos use cherry-picked inputs that make their model look good. Use your actual production data -- including messy, edge-case inputs -- for the evaluation.

Negotiate pricing before committing. Most AI vendors offer volume discounts, committed-use pricing, or startup credits. Get quotes from at least two vendors and use them as leverage.

Design for portability from the start. Abstract your AI calls behind an interface so you can switch vendors without rewriting your application. The AI market is moving fast, and today's best vendor may not be best in six months.

Check the vendor's model update policy. Some vendors update models without notice, which can change behavior and break your prompts. Understand how you will be notified and how long you have to migrate.

Talk to other customers. Ask the vendor for customer references in your industry or at your scale. Peer feedback reveals reliability and support quality that evaluations cannot.

Evaluate the vendor's financial stability. AI startups can run out of funding. If you are building critical infrastructure on a vendor, assess their runway, funding, and business model sustainability.

Key Takeaways

Define and weight your requirements before evaluating vendors to avoid bias

Test on your actual data and use cases, not vendor demos or public benchmarks

Calculate total cost of ownership including integration, maintenance, and prompt engineering

Design for portability so you can switch vendors as the market evolves

Verify compliance claims directly -- do not rely on vendor marketing materials

About This Template

Created by: Tim Adair

Last Updated: 2/9/2026

Version: 1.0.0

License: Free for personal and commercial use