How to Build AI Features Into Your Product

A practical guide to integrating LLM-powered features into your application with reliability, cost control, and room to scale.

Get started free

Choose your integration approach

Decide between direct provider SDKs, an open-source framework, or a managed orchestration platform. Direct SDKs give you control but lock you to one provider. Frameworks add flexibility but require infrastructure. A managed platform like LLMWise gives you a production-ready API with routing, failover, and observability out of the box.

Select models for each feature

Match models to features based on capability and budget. Use GPT-5.2 for complex reasoning, Claude Haiku 4.5 for high-volume low-cost tasks, and Gemini 3 Flash for real-time features that need sub-second latency. LLMWise gives you access to all nine models through one API key, so you can experiment without managing multiple provider accounts.

Implement with an OpenAI-compatible API

Use the OpenAI SDK or any HTTP client to send requests to LLMWise. The API follows the OpenAI chat completions format, so if you already have OpenAI integration, switching is a one-line base URL change. Streaming, function calling, and multimodal inputs all work through the same endpoint.

Add a reliability layer

Wrap your AI calls with failover, retries, and circuit breakers. LLMWise Mesh mode handles this automatically: define a primary model and fallback chain, and the platform routes around failures in under 200 milliseconds. This turns a multi-day infrastructure project into a single API parameter.

Scale with optimization and cost controls

As usage grows, use LLMWise Optimization policies to continuously right-size your model selection based on real data. Set credit budgets per feature to prevent cost overruns. The Replay Lab lets you test model changes against historical traffic before deploying, so scaling never means guessing.

Key takeaways

✓An OpenAI-compatible API like LLMWise lets you integrate once and access nine models without provider lock-in.

✓Built-in failover and circuit breakers eliminate the need to build reliability infrastructure from scratch.

✓Credit-based pricing with per-feature budgets gives you cost control that scales with your product.

Common questions

How long does it take to add AI features with LLMWise?

If you are already using the OpenAI SDK, you can start sending requests to LLMWise in under five minutes by changing the base URL and API key. New integrations typically take a few hours to build a first working feature, including prompt engineering and error handling.

Do I need to manage my own infrastructure?

No. LLMWise is a fully managed platform. It handles model routing, failover, rate limiting, and observability. You send API requests and receive responses. There are no containers to deploy, no GPUs to provision, and no model weights to manage.

Try it yourself

500 free credits. One API key. Nine models. No credit card required.

Get 500 free credits Run traffic replay

How to Compare LLM Models Side by Side How to Reduce LLM API Costs Without Sacrificing Quality How to Add LLM Failover to Your Application How to Switch LLM Providers Without Breaking Your Product How to Use Multiple AI Models in One Application GPT-5.2 vs Claude Sonnet 4.5