What is the biggest risk when hiring an AI vendor?

The primary risk is purchasing a superficial API wrapper that lacks enterprise security, proper error handling, and latency controls. These solutions often break under production load and expose sensitive data to third-party model providers.

How do I test an AI vendor's real-world model accuracy?

Require the vendor to run your specific edge-case datasets through an automated evaluation harness using metrics like faithfulness and retrieval precision. Never rely on generic benchmark scores or vendor-provided demo environments.

What security guarantees should an AI vendor provide?

AI vendors must offer zero-data-retention SLAs, strong prompt injection defenses, and deployment options within your private cloud (VPC) or on-premise infrastructure. Additionally, all model inputs and outputs must remain strictly private to prevent data leakage.

How do token economics impact the evaluation of an AI vendor?

Token costs compound rapidly as usage scales, meaning an unoptimized architecture can lead to unsustainable operational expenses. Vendors must provide clear token projections, prompt compression strategies, and caching models to maintain predictable unit economics.

How to Evaluate an AI Vendor: Technical Checklist

Evaluating an AI vendor requires auditing technical architecture, data handling, and production readiness rather than reviewing pitch decks. To evaluate an AI vendor effectively, demand production code access, run an evaluation harness against your edge-case datasets, audit p95/p99 latency metrics under load, calculate unit token economics at scale, and verify zero-retention data privacy guarantees.

Most vendor failures happen after signing the contract. Sales demos run on cherry-picked datasets over unconstrained APIs, concealing latency spikes, context window degradations, and spiraling token costs. When deals range from $120k to $500k, engineering leaders must run a rigorous technical due diligence process before committing capital.

This 12-question technical framework allows Heads of Engineering and Engineering Managers to separate production-grade engineering firms from superficial API wrappers.

Technical Due Diligence: 12 Questions for AI Vendors

Category 1: Model Architecture & Evaluation Rigor

1. How do you measure model performance and hallucination rates on domain-specific edge cases?

If a vendor responds with generic metrics like accuracy or ROUGE scores, end the conversation. Production AI systems require custom evaluation harnesses (such as DeepEval, Ragas, or custom LLM-as-a-judge pipelines) evaluated against a human-validated ground-truth dataset. Ask to see their specific test harness for hallucination detection, faithfullness, and retrieval precision/recall.

2. What is your system architecture strategy: API orchestration, RAG, fine-tuning, or open-source self-hosting?

A vendor using basic API wrappers on OpenAI or Anthropic exposes your stack to rate limits, third-party outages, and high variable costs. Ask them to justify why they chose a specific pattern. For instance, fine-tuning an open-source model (like Llama 3 70B or Qwen 2.5) via LoRA adapters might lower long-term inference costs, whereas a Retrieval-Augmented Generation (RAG) framework with pgvector or Qdrant might be required for volatile, real-time enterprise data. If you are establishing your selection framework, review our guide on how to hire an AI development company to align architecture needs with delivery capabilities.

3. How do you prevent data contamination in your evaluation sets?

Vendors frequently test models on datasets that overlap with baseline model pre-training corpora or internal test suites. Require the vendor to demonstrate strict segregation of training, validation, and holdout test data. Ask how they dynamically update test sets to avoid benchmark leakage over time.

Category 2: Production Infrastructure, Latency, & Scaling

  +-------------------------------------------------------------------+
  |                       Enterprise Gateway                          |
  +-------------------------------------------------------------------+
                                    |
            +-----------------------+-----------------------+
            |                                               |
            v                                               v
  +-------------------+                           +-------------------+
  |   Primary Model   |                           | Fallback Model    |
  |  (e.g., Claude 3.5|                           |  (e.g., vLLM /    |
  |   / GPT-4o)       |                           |  Llama 3 70B)     |
  +-------------------+                           +-------------------+
            | (Failure / Timeout)                           |
            +-----------------------+-----------------------+
                                    |
                                    v
                       +-------------------------+
                       | Semantic Cache Layer    |
                       | (Redis / Vector DB)     |
                       +-------------------------+
                                    |
                                    v
                       +-------------------------+
                       | Active Guardrails Layer |
                       | (NeMo / Llama Guard)    |
                       +-------------------------+

4. What is your p95 and p99 Time to First Token (TTFT) and token throughput under realistic load?

Demo applications usually run with a concurrency of 1. Production systems face dozens or hundreds of concurrent requests. Ask for benchmark reports detailing:

TTFT: Time to First Token under simulated p95 traffic.
Throughput: Tokens per second per user during peak load.
Inference engine details: Are they leveraging vLLM, TensorRT-LLM, or TGI for open models? Are they using streaming responses to manage perceived user latency?

5. How does the system handle upstream provider outages or rate limits?

An enterprise AI stack cannot fail completely when an API provider experiences elevated latency or outages. The vendor must show an active circuit breaker pattern with clear fallback paths (e.g., falling back from Claude 3.5 Sonnet to an inline open-source model hosted on AWS Bedrock or an internal vLLM cluster). Ask how they implement semantic caching (using Redis or specialized vector caches) to avoid redundant model calls.

6. What vector database architecture and chunking strategies do you deploy?

Naïve fixed-character chunking with basic cosine similarity leads to poor context retrieval. Ask the vendor:

Which vector store do they use (e.g., pgvector, Qdrant, Pinecone) and why?
How do they handle document chunking (e.g., parent-document retrieval, semantic chunking, or late chunking)?
Do they use hybrid search (BM25 sparse retrieval + dense vector search) combined with cross-encoder re-ranking (e.g., Cohere Rerank)?

Category 3: Security, Data Governance, & IP Rights

7. What happens to our data, prompts, and generated outputs?

Demand a zero-retention SLA. Verify that your enterprise data will never be logged long-term by third parties, stored in unencrypted S3 buckets, or used to train public base models. Ask whether the system can run inside your own VPC (AWS, Azure, GCP) or on-premise infrastructure.

8. How do you secure the application against prompt injection and data exfiltration?

LLM applications introduce new attack surfaces, including indirect prompt injection, jailbreaking, and insecure output handling. The vendor must demonstrate explicit security layers, such as:

Input/output guardrails (e.g., NeMo Guardrails, Llama Guard).
Structured output enforcement (using Pydantic, Instructor, or Outlines) to prevent code execution vulnerabilities.
Sandboxed execution environments (e.g., E2B, Docker isolation) if the AI generates and executes code.

9. Who owns the code, model weights, synthetic datasets, and prompt libraries?

Work-for-hire provisions must explicitly grant your company 100% ownership of custom code, fine-tuned LoRA adapters, evaluation suites, pipeline orchestration scripts, and generated datasets. Ensure there are no proprietary vendor libraries embedded in the system that create permanent operational lock-in.

Category 4: Operational Economics & Observability

10. What are the projected unit operational costs at 10x and 100x current volume?

Model invocation costs accumulate rapidly. Require a clear financial projection detailing token usage per transaction, vector DB query costs, compute overhead, and monitoring costs.

Unit Cost = (Input Tokens × Input Rate) + (Output Tokens × Output Rate) + Vector DB Query Cost + Compute Overhead

Ask how they optimize these costs using prompt compression, smaller specialized models (SLMs), or speculative decoding.

11. What observability and tracing tools are built into the pipeline?

Debugging non-deterministic systems requires tracing individual LLM calls, retrieval steps, tool executions, and prompt variables. Ensure the vendor integrates production tracing platforms like LangSmith, Arize Phoenix, or Traceloop (OpenTelemetry-compliant) directly into the deployment pipeline.

12. How is the codebase structured for maintenance and handoff to our internal team?

Ask to inspect a sample repository or architecture design document. Ensure infrastructure is fully codified using Infrastructure as Code (IaC) tools like Terraform or Pulumi. Pipelines should use clean, standard Python or TypeScript patterns rather than fragile low-code/no-code platforms.

Comparing AI Vendor Archetypes

When evaluating the market—and comparing providers across the best AI development companies in 2026—you will generally encounter three categories of vendors:

What This Means for Your Team

Evaluating an AI vendor is an engineering exercise, not a vendor procurement routine. Treating an AI build like a traditional web or mobile project leads to unreliable systems, unexpected cloud bills, and unmaintainable code bases.

Run this checklist during your technical discovery calls:

Require vendors to complete a live technical audit on your real, uncleaned data.
Require code repos to be IaC-compliant with explicit tracing and evaluation pipelines baked in.
Validate total cost of ownership at scale before signing contract terms.

If you are currently planning a core AI deployment, legacy system modernization, or technical due diligence process and need a team of senior engineers to deliver production-grade systems, contact NextGen Coding Company.

How to evaluate an AI vendor: a 12-question technical due diligence list