GPT Integration Services — Add AI Intelligence to Any Product

When GPT Integration From ZTABS Is the Wrong Fit

• ⚠You don't know your expected token volume. Without a volume estimate, you can't budget or architect (cheap model + caching vs. expensive frontier model). Start with a 2-week pilot, measure tokens per request and request volume, then size the full build.
• ⚠You want zero data leaving your infrastructure. Even with OpenAI enterprise (no training on data), your prompts transit through OpenAI's servers. For strict data-residency, use self-hosted Llama/Mistral via vLLM (see self-hosted-ai-deployment) or Azure OpenAI with private endpoints.
• ⚠Your use case is deterministic (math, SQL, config). LLMs hallucinate at 1–5% rates even on 'easy' deterministic tasks. Use rule engines, SQL generators with schema validation, or typed function-calling with strict JSON schemas — don't ship a plain LLM call for correctness-critical paths.
• ⚠You need <500ms p95 latency for non-trivial prompts. GPT-4o typical TTFT is 500–1500ms; full response 2–8s. For sub-500ms needs, use GPT-4o mini, cached embeddings + kNN, or smaller fine-tuned models; consider pre-generation at write-time instead of request-time.