ChatGPT API

OpenAI’s ChatGPT API gives you access to multiple versions of the GPT family, including GPT-o3-mini and GPT-4.5, each suited for different needs. GPT-o3-mini is ideal for high-speed, cost-effective general language tasks, while GPT-4.5 offers better reasoning, nuanced understanding, and function-calling accuracy.

Dekkode helps you choose the right model for your use case, ensuring optimal performance and cost balance. We also support model versioning and rollout strategies to ensure consistency and smooth upgrades as OpenAI releases improvements.

LLM-powered systems often behave like black boxes—but they don’t have to. With our observability integrations, you get full insight into how the ChatGPT API interacts with your users and systems. We track prompt and completion flows, latency, token usage, and failure rates.

This allows for better debugging, real-time alerting, and post-mortem analysis. We can even integrate observability data into your own monitoring tools like Datadog, Sentry, or custom dashboards.

We implement secure, structured logging for all interactions through the ChatGPT API. This includes prompts, tool use, function responses, and system message configurations—while respecting privacy and compliance rules.

Logs enable transparency for auditing and debugging, but also act as training material for improving prompt design or internal alignment reviews. Logs can be anonymized, encrypted, and stored based on your regulatory needs.

Cost is a critical factor when scaling ChatGPT-based solutions. The API charges per token, and usage can add up quickly without the right controls. Dekkode helps you monitor, predict, and optimize your token usage with batching strategies, response length control, and fallback logic.

We also help architect hybrid setups where high-traffic or low-sensitivity tasks use GPT-3.5 or local models, while GPT-4 is reserved for complex, high-value interactions.

The ChatGPT API supports RAG (Retrieval-Augmented Generation) via tools like function calling or external memory systems. We integrate your custom knowledge sources—documents, databases, and APIs—into the LLM runtime for grounded, context-aware answers.

Whether you're building document Q&A systems, internal search, or long-context knowledge assistants, our team handles chunking, vectorization, and secure lookup—all seamlessly tied into your ChatGPT flows.

Unlike single-turn prompts, the ChatGPT API supports persistent, multi-turn interactions with role-based messages and memory-style context retention. This enables highly personalized, fluid conversations.

We design structured chatflows with memory management, fallback logic, and tone consistency. You can power internal assistants, support agents, and chat interfaces that feel natural, yet stay within your brand and business logic.

Function calling ("Tools") is one of the most powerful features of the ChatGPT API. It lets the model call external functions, fetch data, or perform actions during a conversation. This transforms the LLM from a static responder into a dynamic operator.

We help define, secure, and orchestrate these tools—linking APIs, databases, and workflows into a coherent, intelligent interface. Tools are key to enabling AI assistants that can not just talk, but do things: create tasks, update systems, retrieve real-time information, and more. Read more about Tools here