Choosing a generative AI partner is one of the most consequential technology decisions you'll make—and one of the most confusing, given the crowded vendor landscape and rapid feature evolution. You need a partner who understands your specific use case, can integrate reliably with your existing systems, and won't become obsolete in six months. This checklist will help you separate qualified vendors from hype.
Model Capability & Customization
Start by assessing what models the vendor actually offers access to. Are they reselling OpenAI, Anthropic, or Meta models, or do they have proprietary models? Ask them directly: which LLM versions do they support, and how often do they update? A vendor stuck on GPT-3.5 in 2024 signals they're not investing in keeping pace.
For your use case, you also need to know if they offer fine-tuning or retrieval-augmented generation (RAG). Fine-tuning lets you adapt a model on your domain-specific data—valuable for legal document analysis, medical coding, or financial forecasting—but costs $1,000–$10,000+ per model depending on data volume. RAG is cheaper and faster (often $500–$3,000 to implement) but requires robust knowledge base management on their end.
Ask for a demo on a representative sample of your actual data, not sanitized examples. If they won't do this, move on.
Integration Architecture & Technical Debt
How does their platform integrate with your tech stack? Look for vendors offering:
- API-first design with clear REST or GraphQL endpoints (not just UI wrappers)
- Native connectors for your CRM, ERP, or data warehouse (Salesforce, SAP, Snowflake, etc.)
- Webhook support for real-time workflows
- SDKs in Python, JavaScript, or Go—whatever your team uses
- Documented rate limits and SLAs (uptime guarantees, response latency)
Integration timelines typically range from 2–8 weeks depending on complexity. A vendor claiming they can integrate your legacy system in one week is either overselling or building a fragile solution. Ask for a technical architecture diagram and a reference customer with a similar stack to yours.
Data Privacy, Compliance & Governance
This is non-negotiable. Confirm:
- Data residency options: Can logs and processed data stay on-premises or in your preferred cloud region?
- SOC 2 Type II certification: Mandatory for enterprise deals. If they don't have it, ask for an audit roadmap.
- HIPAA, GDPR, FedRAMP readiness: If you're in healthcare, finance, or government, these aren't optional.
- Data retention policies: Will they keep your prompts and outputs indefinitely? Demand clear deletion policies in writing.
- IP indemnification: Who owns outputs? Many vendors now clarify that you retain IP rights to generated content.
Get these commitments in a Data Processing Addendum (DPA), not just marketing collateral. Budget 4–6 weeks for legal review if this is your first AI vendor relationship.
Pricing Model & Hidden Costs
LLM pricing varies wildly. Understand what you're paying for:
- Per-token pricing: Most vendors charge by input and output tokens. A typical page of text = 250–500 tokens. GPT-4-class models range from $0.03–$0.15 per 1K tokens (input) and $0.06–$0.30 per 1K tokens (output).
- Seat licenses vs. consumption: Some charge monthly per user ($100–$500/user); others are pure pay-as-you-go.
- Infrastructure costs: Who hosts? If they're reselling OpenAI, costs are transparent. If they're self-hosting on your cloud, you might pay infrastructure fees on top.
Ask for a 90-day cost forecast on realistic usage volumes. A vendor refusing to estimate is a red flag.
Support & Roadmap Clarity
Evaluate their support tier availability. You typically need:
- Technical support: Response time (4-hour SLA is standard), escalation process
- Product roadmap transparency: What's coming in the next 6 months? Are they addressing your pain points?
- Reference customers: At least 2–3 in your industry, not just big-name logos
Frequently Asked Questions
Q: What's the difference between hiring a generative AI vendor versus building in-house with open-source models? In-house takes 6–12 months, $200K–$1M+ in engineering labor, and ongoing maintenance; vendor solutions deploy in weeks but lock you into their platform and pricing. Choose vendor if speed and managed reliability matter; choose in-house if you have specialized security needs or want to avoid vendor lock-in.
Q: How do I evaluate if a vendor's model is accurate enough for my use case? Run a blind test: give them 100–500 real examples from your data and measure precision, recall, or F1 score against your ground truth. Benchmark against competing vendors on the same dataset. Accuracy claims without testing numbers are marketing.
Q: What happens if a vendor goes out of business or discontinues my model? Always negotiate exit clauses and data export rights in your contract. Request 90-day notice for model deprecation. Prefer vendors backed by larger companies or with clear acquisition plans.
Mercoly helps you compare and find trusted generative AI and LLM integration providers side-by-side, so you can evaluate multiple vendors against this checklist in one place.
Start your vendor evaluation today by documenting your specific integration needs and running this checklist against your top three candidates.