Editorial Desk
Product updates, architecture notes, and implementation guides from the AICredits engineering and platform teams.
At a glance
Browse by topic
If your app passes user input to an LLM, sensitive data goes with it unless something stops it. Here's how server-side PII masking, keyword blocking, and response healing work in practice.
Most providers only let you set a spending limit at the account level. Here's how to control cost per key, per team, and per environment instead — with real configuration examples.
Embeddings are the cheapest line item in most RAG pipelines — until you re-embed the same documents on every deploy. Here's the real cost and how to avoid the common waste.
A production-ready FastAPI backend pattern for streaming LLM responses to a frontend — server-sent events, error handling, and cost tracking in one template.
Multi-agent frameworks multiply your token spend by design — every agent hop is another LLM call. Here's how to wire CrewAI and LangGraph to AICredits and what a typical run actually costs.
A working LlamaIndex RAG setup — ingestion, embeddings, retrieval, and generation — with the real rupee cost of each stage so you know where your budget actually goes.
If your app resends the same system prompt, codebase, or document on every request, prompt caching can cut that portion of your bill by 90%. Here's how it works and when it actually saves money.
AI agents can rack up massive API bills when they loop, retry, or process large context windows. Here's what goes wrong, real rupee numbers, and exactly how to cap spending before it happens.
Rate limit errors, provider timeouts, and transient failures are inevitable. Here is a production-grade retry strategy with exponential backoff, jitter, and fallback routing.
Your system prompt, conversation history, and injected documents all compete for the same context window. Here is how to manage token budget and avoid costly waste.
Route cheap tasks to cheap models and expensive tasks to capable ones. A practical Python implementation that cuts API spend by 40–70% without sacrificing quality.
Standard HTTP caching doesn't help with LLMs because queries are never exactly the same. Semantic caching matches by meaning — and can eliminate 20–40% of your API spend.
Shipping an LLM feature without evals is flying blind. Here's how to build evaluation systems that tell you if your prompts are actually working.
An LLM API gateway sits between your application and language model providers. Here is what it does, why you need one, and when self-hosted vs managed makes sense.
If your app passes user input to an LLM, you're vulnerable to prompt injection. Here's what it is, real attack examples, and how to defend against it.
A practical routing pattern for multi-provider resiliency and graceful degradation when a primary model slows down or fails.
What to monitor in a unified AI gateway: latency, provider errors, fallback rates, token drift, and wallet burn.
Start from docs quickstart, then move to API reference and pricing formula pages for production integration.
