Choosing the wrong AI vendor costs more than money. It costs months of rework, user trust, and competitive positioning. We have evaluated and integrated dozens of AI APIs across client projects, and the difference between a good choice and a bad one usually comes down to what you test before you commit.
This is the evaluation framework we use at Veld when selecting AI vendors and APIs for production applications.
Start With Your Requirements, Not the Vendor List
The most common mistake is starting with "which AI provider should we use?" instead of "what exactly do we need this AI to do?" These are very different questions.
Before you open a single vendor's documentation, define:
- The specific task. "We need to classify support tickets into 12 categories with 90% accuracy" is useful. "We want to add AI" is not.
- Latency requirements. A real time chatbot needs sub second responses. A batch document processor can tolerate minutes.
- Volume projections. 1,000 API calls per month and 1,000,000 API calls per month require fundamentally different pricing and infrastructure decisions.
- Data sensitivity. Are you sending customer PII? Financial data? Healthcare records? This immediately narrows your vendor options.
- Budget ceiling. Not just build cost, but ongoing monthly inference cost at projected scale.
We covered how to match these requirements to specific LLM providers in our guide to choosing LLM APIs. That post dives into the technical tradeoffs between providers.
The Five Dimensions of Vendor Evaluation
Once your requirements are clear, evaluate every vendor across these five dimensions.
1. Model Quality for Your Specific Use Case
Benchmarks are marketing. The only evaluation that matters is performance on your data, with your prompts, for your task.
Run at least 200 test cases through every vendor you are considering. Measure accuracy, consistency, and failure modes. A model that scores 95% on a generic benchmark might score 60% on your niche domain. We have seen this happen repeatedly.
Build a simple evaluation harness. Send the same inputs to each vendor. Score the outputs. This takes a few days and saves months of regret.
2. Pricing at Scale
Every AI vendor looks cheap at prototype volume. The question is what happens at 10x, 50x, and 100x your current usage.
Calculate your projected monthly cost at realistic scale. Include input tokens, output tokens, fine tuning costs, embedding storage, and any premium features you need. Then add 30% because real world usage always exceeds projections.
Watch for pricing traps:
- Per token pricing that spikes with longer contexts. If your use case involves long documents, this adds up fast.
- Minimum commitments or reserved capacity requirements. Fine for enterprise, dangerous for startups.
- Separate charges for fine tuning, evaluation, and storage. These are often buried in documentation.
Our AI integration cost breakdown covers the full picture of what AI actually costs in production, not just the API line item.
3. Reliability and Uptime
AI APIs go down. They get rate limited. They return degraded results during high load. Your application needs to handle all of this.
Evaluate:
- Published uptime SLAs. Anything below 99.9% is a problem for production applications.
- Rate limits at your tier. Free and starter tiers often have limits that will block you during traffic spikes.
- Historical incident reports. Check the vendor's status page history. Frequent incidents signal infrastructure problems.
- Failover options. Can you switch to a backup provider if the primary goes down? This is where building with provider abstraction matters.
At Veld, we build every AI integration with fallback chains. If the primary model is unavailable, the system degrades gracefully to a secondary provider or a cached response, not an error page.
4. Data Privacy and Compliance
This is where most evaluations fall short. You need clear answers to:
- Does the vendor train on your data? Many providers use API inputs for model training by default. You need to opt out explicitly, and verify it is actually honored.
- Where is data processed and stored? If you serve EU customers, you need to know if data leaves the EU.
- What certifications does the vendor hold? SOC 2, HIPAA BAA, GDPR compliance are not optional for regulated industries.
- Can you use a private deployment? Some vendors offer dedicated instances that never share infrastructure.
We cover this in depth in our AI compliance and data privacy post. Do not skip this dimension. A data breach or compliance violation will cost more than every other evaluation dimension combined.
5. Integration Complexity and Lock In Risk
How hard is it to integrate, and how hard is it to leave?
- SDK quality. Is the SDK well documented, actively maintained, and available in your language? Poor SDKs mean more custom code and more maintenance.
- API stability. How often do they introduce breaking changes? Check their changelog.
- Proprietary features. Fine tuned models, custom embeddings, and vendor specific tooling create lock in. That is not always bad, but you should know the exit cost.
- Standard interfaces. Vendors that support OpenAI compatible APIs are easier to swap than vendors with entirely proprietary interfaces.
Build a Proof of Concept Before You Commit
Never sign an annual contract based on documentation alone. Build a proof of concept that tests:
- Real world latency under simulated production load.
- Edge cases that your evaluation harness identified.
- Cost tracking with actual token counting at realistic volumes.
- Error handling including timeouts, malformed responses, and rate limit behavior.
This proof of concept should take one to two weeks. If a vendor makes this difficult, that tells you something important about what production integration will look like.
Our Recommendation Process
When clients come to us through our consulting services, vendor evaluation is one of the first things we do. We run structured evaluations across the dimensions above, produce a comparison matrix, and make a recommendation backed by data.
For most projects, we find that the right answer is not a single vendor. It is a primary vendor for the core use case with a secondary vendor as a fallback, wrapped in an abstraction layer that makes switching possible without rewriting your application.
We built this pattern for Traderly, where AI reliability directly affects user trust. The abstraction layer added about two weeks to the initial build but has saved significantly more time in ongoing operations.
Avoid These Common Mistakes
Do not choose based on brand recognition alone. The biggest vendor is not always the best fit. Smaller, specialized providers often outperform generalists for specific tasks.
Do not optimize for cost at the expense of quality. A model that costs half as much but produces 20% more errors will cost you more in user churn and support tickets.
Do not skip the compliance review. We have seen companies build entire features on vendors that could not meet their compliance requirements. That is a full rebuild.
Do not ignore the exit strategy. Every vendor relationship should have a documented path to migration. Talk to us if you want help building an AI integration that is vendor resilient from day one.