Your AI feature works in a demo. Now it needs to work inside a product, where users submit messy inputs, expect timely responses, and depend on the result.
Choosing an AI inference API means evaluating that whole experience. The right choice depends on the quality your feature needs, how requests behave under load, and what it costs to produce a result your application can actually use.
Start with one real workflow. Define what success looks like, test it consistently, and use those results to guide your integration.
Start with the Outcome Your Application Needs
An inference API gives your application access to a running AI model. Your software sends a request, the model processes it, and the service returns a response.
That describes the connection. Your product requirements determine whether the response is useful.
A support tool might need a concise summary that preserves every unresolved issue. A document workflow might need accurate extraction of a few specific fields. A writing assistant might prioritize clear language and responsiveness.
Before comparing services, describe one successful result in plain language.
For a support summary, that definition could be: “Capture the customer’s problem, the steps already attempted, and the remaining question without inventing details.”
That statement becomes the foundation of your evaluation. It is more useful than asking whether a model is generally “good.”
Treat API Compatibility as the Beginning of Your Evaluation
An OpenAI-compatible interface can make an inference service easier to integrate with familiar clients and request structures.
You still need to verify the particular features your application uses. Endpoint support, model capabilities, parameters, and response behavior can differ between implementations.
For example, vLLM’s documentation identifies unsupported or ignored parameters and explains that some tool-calling behavior depends on the model. That illustrates why a familiar API format should be followed by feature-level testing. vLLM’s compatibility documentation
Write down your requirements before connecting a new service. Include streaming if your interface depends on it, structured output if another system consumes the response, and the input lengths your users typically submit.
This turns integration into a defined task with clear acceptance criteria.
Build a Small Evaluation Around Realistic Inputs
Consider a hypothetical application that summarizes customer support conversations.
A useful initial evaluation might contain 30 conversations: straightforward questions, long troubleshooting exchanges, ambiguous messages, and cases where the customer changes the subject.
Use examples your team is authorized to process, with unnecessary identifying information removed.
For each conversation, prepare a short reference describing the essential facts. You are evaluating whether the model captures those facts accurately; matching a particular sentence word for word is usually unnecessary.
Then score the results against a consistent rubric:
- Accuracy: Does the summary preserve what happened?
- Completeness: Does it include the unresolved issue?
- Restraint: Does it avoid adding unsupported details?
- Usability: Can the next person act on it?
- Responsiveness: Does it arrive within an acceptable time?
- Cost: What did the complete attempt consume?
Review failures individually. A missing date might call for a better prompt, while repeated confusion about the conversation could indicate a model mismatch.
Keep the evaluation set after launch. It gives you a repeatable way to assess future changes.
Measure Cost per Usable Result
The listed price of a request is one part of your operating cost.
Your application may make additional requests to repair formatting, retry a failure, or produce an answer that passes validation. Include those attempts when evaluating the workflow.
Consider an illustrative comparison. One configuration costs 20 units to process 100 tasks, and 80 outputs meet your acceptance criteria. Another costs 24 units, and 96 outputs qualify.
Both cost 0.25 units per accepted output before accounting for other differences.
Those are hypothetical numbers, but the calculation is useful:
Cost per accepted output = total inference spending ÷ accepted outputs.
Also record latency and review effort. A configuration that requires extensive manual correction may be a poor fit even when its inference charges look attractive.
Make your decision using the economics of the feature your users experience.
Test How the Application Behaves When Requests Fail
A successful response demonstrates one path through your system.
Also test a slow response, an unavailable model, an invalid request, and an output that your application cannot use. Decide what the user should see in each case.
For the support-summary example, the application could preserve the original conversation and show that a summary is temporarily unavailable. That allows the support workflow to continue.
Set timeouts and retry limits deliberately. Record enough information to distinguish a service error from an application validation failure, while following your data-handling requirements.
Where fallback is available, evaluate its outputs with the same acceptance criteria. A response arriving through an alternate route still needs to satisfy the original task.
Where Katara Cortex Fits
Katara Cortex offers an OpenAI-compatible API connecting applications with independent compute providers. It handles provider selection, routing, validation, metering, and settlement, with managed fallback for supported models. Explore the Developer Offering
For a development team, that creates an option to evaluate through an existing application workflow.
Begin with one supported model and one clearly defined feature. Use your evaluation results to decide whether the integration meets your quality, response-time, and cost requirements.
Expand When the Evidence Supports It
A focused pilot gives your team several practical benefits:
- A clear definition of acceptable model output.
- A realistic estimate of operating costs.
- Known behavior when requests fail.
- A reusable evaluation for future model changes.
- A smaller integration to troubleshoot and improve.
After the pilot, introduce a limited amount of production traffic with appropriate monitoring. Compare those results with your test set, paying attention to inputs that your initial examples missed.
Expand as you gain evidence that the feature behaves consistently.
FAQ
Should Every Feature Use the Same Model?
Evaluate each feature separately. A short classification task and a detailed writing task may have different quality and response-time requirements. Shared infrastructure can simplify operations, but model selection should follow the work.
How Many Examples Do I Need Before Launching?
Use enough examples to cover your common inputs and consequential failure cases. A small, carefully selected set can reveal obvious problems; expand it as you encounter new patterns. The appropriate level of testing depends on the impact of an incorrect result.
When Should I Repeat the Evaluation?
Repeat it when you change the model, prompt, integration, or important application behavior. Also add examples when production failures reveal gaps in your original test set.
To evaluate Katara Cortex for your application, begin with the API documentation and one workflow you can measure.