· Denzil Correa, CEO AIReview · 4 min read
How to Evaluate an AI Vendor Before Signing a Contract
A practical checklist for evaluating AI vendors before committing engineering time or signing expensive contracts.
Why AI Vendor Evaluation Matters
Many companies sign AI vendor contracts before fully understanding the technical architecture, evaluation methodology, and long-term operational costs.
The result is often predictable:
- vendor lock-in
- expensive infrastructure costs
- systems that perform well in demos but fail in production
- engineering teams forced to redesign the system later
In some cases, organizations commit tens of millions of euros before realizing the system cannot deliver the expected value.
A structured technical evaluation before signing a contract can prevent these mistakes.
This article outlines a practical framework for evaluating AI vendors before committing engineering budget or infrastructure investment.
Step 1: Understand What Problem AI Is Actually Solving
The first question is not about models or infrastructure.
It is about the problem itself.
Many vendor proposals assume AI is required even when a simpler solution would work better.
Before evaluating a vendor, ask:
- What exact problem are we solving?
- What metric defines success?
- Could a simpler system solve this problem?
In many cases, rule-based systems, search systems, or deterministic software can outperform machine learning systems in reliability, cost, and latency.
AI should be introduced only when it clearly provides value.
Step 2: Examine the Proposed Architecture
Every AI system has an architecture.
A vendor should be able to clearly explain:
- data flow
- model components
- retrieval systems
- evaluation pipeline
- infrastructure requirements
For modern AI systems using large language models, the architecture often includes:
- data ingestion
- embeddings generation
- vector database retrieval
- prompt construction
- model inference
- output evaluation
If a vendor cannot explain the architecture clearly, that is an immediate red flag.
A good architecture review should answer:
- Why this architecture?
- What alternatives were considered?
- What happens when the system fails?
Step 3: Ask How the System Is Evaluated
One of the most common weaknesses in AI vendor proposals is lack of evaluation methodology.
A demo is not an evaluation.
Vendors should be able to explain:
- how the system is tested
- what evaluation datasets are used
- what metrics determine success
- how performance is monitored after deployment
Good AI systems include:
- offline evaluation datasets
- benchmark metrics
- failure analysis procedures
Without this, the system cannot be trusted in production.
Step 4: Estimate the Real Cost of the System
AI systems often appear cheap during demonstrations.
In reality, costs can grow quickly.
Typical cost components include:
- model inference costs
- data processing costs
- infrastructure and storage
- monitoring and evaluation systems
- engineering maintenance
For large language model systems, the biggest costs are often:
- inference API calls
- embedding generation
- retrieval infrastructure
Ask vendors to provide a cost model for realistic usage scenarios.
This should include expected monthly usage and scaling assumptions.
Step 5: Evaluate Vendor Lock-In Risk
Some AI vendors design systems that are difficult to migrate away from.
Lock-in can occur through:
- proprietary APIs
- proprietary embeddings formats
- closed model architectures
- restricted data export capabilities
Before signing a contract, ask:
- Can the system run on alternative infrastructure?
- Can the data be exported?
- Can models be replaced?
Architectures built on open components tend to be safer long term.
Step 6: Identify Failure Modes
Every AI system fails in some situations.
Understanding failure modes is critical.
Common failure modes include:
- hallucinated outputs
- incorrect retrieval results
- degraded performance on edge cases
- scaling failures under load
A vendor should be able to explain:
- how failures are detected
- how the system recovers
- how performance is monitored
Systems that cannot explain failure handling are not production-ready.
Step 7: Validate the Implementation Roadmap
Vendor proposals often underestimate the effort required to deploy AI systems.
Ask for a realistic roadmap including:
- integration effort
- infrastructure setup
- evaluation framework
- monitoring systems
- ongoing maintenance
AI systems are not one-time deployments.
They require ongoing engineering investment.
Common Red Flags in AI Vendor Proposals
During evaluation, watch for these warning signs:
- heavy reliance on demos instead of evaluation metrics
- unclear system architecture
- lack of cost transparency
- proprietary infrastructure requirements
- unrealistic performance claims
These issues often appear in early proposals.
Catching them early can save months of engineering work and significant budget.
Final Thoughts
AI vendors can provide valuable technology and expertise.
However, AI systems are complex and expensive to deploy correctly.
A structured technical review before signing a contract can help organizations:
- avoid vendor lock-in
- estimate real infrastructure costs
- validate architecture decisions
- reduce engineering risk
Independent technical evaluation is often the most effective way to ensure the proposed system will actually work in production.
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