Select and qualify an AI solution provider
A convincing demonstration proves neither production quality nor provider resilience. Qualification must address a specific offer, defined use, dated evidence and realistic exit conditions.
The short answer
Freeze the need and test cases before vendor meetings. Compare the same offer on quality, data, operations, total cost and reversibility. Put material commitments into the contract, then pilot with limits and stop criteria.
- Qualify a specific offer
- Require dated evidence
- Negotiate exit before entry
Run a defensible selection
1. Freeze the scope
Describe users, volumes, data, integrations, service levels and required outcomes. Separate mandatory requirements from preferences so the demonstration does not redefine the need.
2. Identify the exact offer
Record the plan, region, model, options, connectors, administration and applicable terms. One brand may provide very different safeguards across plans.
3. Request evidence
Collect technical documentation, data terms, subprocessors, security, availability, change history, export and deletion information. Date every item and identify its owner.
4. Test the same cases
Use authorised data, normal and edge cases and one scoring grid. Measure outcomes, mistakes, latency, human rework, integration and stability.
5. Calculate cost and dependence
Project licences, usage, storage, services, integration, review and growth. Identify proprietary formats, quotas, non-exportable features and contractual dependencies.
6. Contract and pilot
Align contract, security, privacy, support, change and exit terms with the evidence. Run a bounded pilot with owners, thresholds, a decision log and rollback procedure.
Four files to compare
Product
Useful quality, integration, accessibility, stability and roadmap.
Data
Purposes, retention, regions, subprocessors, training, export and deletion.
Operations
Administration, monitoring, support, incidents, continuity and change.
Economics
Total cost, commitments, indexation, dependencies and exit cost.
Platforms and providers to compare
This selection offers starting points. Plans, regions, models, safeguards and prices change; document the version actually assessed and consult official sources.
NVIDIA NIM
NVIDIA · US
Visit official site
Codex
OpenAI · US
Visit official site
Dify
LangGenius / Dify
Visit official site
Microsoft Foundry
Microsoft · US
Visit official site
OpenAI Platform
OpenAI · US
Visit official site
LangGraph
LangChain · US
Visit official siteHow is this selection produced?
Active services are distributed across guide-related categories, then ordered by editorial highlighting and internal score. This does not assess security, compliance or performance on your use case. Methodology.
Frequently asked questions
How many vendors should be compared?
Three serious candidates are often sufficient after document screening. Deep testing on identical cases is more useful than many different demonstrations.
Can certifications be treated as proof?
They provide useful evidence for a defined scope, but do not prove that your configuration, use and obligations are covered.
What should an exit test cover?
Export useful data, settings and logs, recreate a case in an alternative solution and verify deletion timing and evidence.