OpenAI And Anthropic Integration In Your Product

This is integration work, and calling it anything grander would be dishonest. We do not train models. We take the hosted models from OpenAI and Anthropic and build the software around them that lets a feature survive real users: prompts under version control, evals that catch a regression, guardrails, fallbacks, and a spend cap. That software is Python and TypeScript, which is what we do anyway.

Wiring up an OpenAI or Anthropic API call takes ten minutes. Building an LLM feature that does not embarrass you in production takes weeks. The demo always works on the cherry-picked example; production is the long tail of user input that breaks the prompt, leaks PII, exceeds rate limits, hits context windows, or generates plausible nonsense your customers cite as fact.

Most LLM features ship without guardrails. No structured output schemas (so the model occasionally returns invalid JSON). No retries with exponential backoff (so a single 429 kills the request). No prompt versioning (so when the model is updated, behaviour silently changes). No evals (so regressions are detected by customers, not by CI). We rebuild LLM features with all of this in place.

Cost controls are the part founders forget until the bill arrives. A logged-out endpoint that hits GPT-4 is a denial-of-wallet attack waiting to happen. Streaming responses still cost the full output tokens. Caching the cheap parts (system prompts, retrieved context, function definitions) cuts costs significantly. We design the cost shape from day one.

Anthropic vs OpenAI is not "which is better." It is "which is right for this task at this price point." Claude is stronger at long-context reasoning and instruction following. GPT-4 is stronger at structured output and certain coding tasks. Smaller models (Haiku, GPT-4 Mini) are dramatically cheaper for classification and extraction. A senior engineer picks per task, sometimes routes between them dynamically.

AsyncForge has senior engineers shipping LLM features in production today. Submit a chat interface, a RAG pipeline, an agent, an extraction job, or a full LLM integration. Light 4 days, Standard 48 hours, Pro 1 day. Includes prompt versioning, evals, and cost controls.