Which GPT model should I use?

Usually a mix: a capable model for hard steps and a cheaper or smaller one for easy, high-volume steps. We route by task and prove the choice with evals rather than paying for the biggest model everywhere.

How do you keep costs under control?

Prompt and context trimming, caching, model routing by difficulty, and hard token budgets, all visible in observability. We treat cost as a first-class metric alongside quality and latency.

★ OpenAI GPTLLM Models8-week engagement

OPENAI GPT · DIRECT INTEGRATION

GPT in production, not just in a notebook.

OpenAI's GPT models are a strong, well-tooled default with broad capabilities and a mature ecosystem. We integrate them directly, with the evals, cost control, and structure that production demands.

LLM APIEval pipelinesTypeScriptVector store

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Cycle

8 weeks · fixed price

Stack

OpenAI API, direct

Output

Production code + eval suite

Handoff

Full source ownership

[THE SHORT VERSION]

The default frontier model, engineered for production.

GPT models are capable, broadly supported, and backed by mature tooling for structured output, function calling, and embeddings. The gap between a demo and a product is the same as always: evals, retries, cost and latency control, structured output validation, and a vendor-neutral abstraction. That gap is the work we do.

When it fits

General-purpose reasoning, generation, and extraction
Function calling and structured output workflows
Teams wanting a broadly supported, well-documented model

When it does not

On-prem-only requirements (use an open-weight model)
Tasks where a cheaper model meets the eval bar

[HOW WE BUILD IT]

How we build with OpenAI GPT.

Direct API, thin abstraction

Calls go straight to the OpenAI API behind a small provider interface, so switching or adding models stays a config change.

Structured output, validated

We use structured output and function calling, then validate against a schema. No hoping the JSON parses.

Evals before you trust it

An eval set from your real tasks gates every prompt and model change. Quality is measured, not vibed.

Cost, latency, and fallback

Token budgets, caching, streaming, and a fallback path, with observability on every call.

[WHAT YOU GET]

What the engagement leaves behind.

Direct

No orchestration framework

Schema

Structured output validated

Eval-gated

Quality measured, not assumed

Observed

Every call, cost and latency

[COMMON QUESTIONS]

Questions we get asked.

Which GPT model should I use?: Usually a mix: a capable model for hard steps and a cheaper or smaller one for easy, high-volume steps. We route by task and prove the choice with evals rather than paying for the biggest model everywhere.
How do you keep costs under control?: Prompt and context trimming, caching, model routing by difficulty, and hard token budgets, all visible in observability. We treat cost as a first-class metric alongside quality and latency.

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