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Drishti Shah

Cursor best practices for enterprise teams

Cursor best practices for enterprise teams

Cursor has become one of the most widely adopted AI coding tools in enterprise engineering organizations. The individual productivity gains are real. But operational control across teams is a different problem entirely. Platform teams now face a new challenge: scattered API keys, invisible token spend, no budget controls, and compliance

Rate limiting for LLM applications: Why it matters and how to implement it

Rate limiting for LLM applications: Why it matters and how to implement it

Your team launches a new LLM-powered feature. Early tests look promising, but as usage grows, token consumption spikes. GPU queues begin to fill, response times increase, and users start seeing HTTP 429 errors. AI rate limiting governs how requests and tokens flow through the LLM infrastructure. By controlling token throughput,

Stop hardcoding API keys in your AI apps

Stop hardcoding API keys in your AI apps

Every team building with LLMs has API keys to manage. OpenAI, Anthropic, Google, Azure, AWS Bedrock, Mistral; each provider issues its own credentials, and each integration needs a key to work. The default approach is simple: copy the key, paste it into your app config, and move on. But as

What is autoinstrumentation?

What is autoinstrumentation?

Your AI agent works in staging. In production, something starts breaking. Responses are slower than expected, and token usage spikes. And occasionally, the agent takes a path no one expected. Infrastructure metrics look normal, but users are still reporting issues. Traditional monitoring tells you whether servers are running, but it

Akto Partners with Portkey to Bring Guardrails to AI Gateway

Akto Partners with Portkey to Bring Guardrails to AI Gateway

Teams are no longer just experimenting with LLMs. They’re shipping AI agents, MCP-connected workflows, and production GenAI features that call real tools, access internal systems, and move data across multiple layers. To manage that complexity, many engineering teams are putting AI gateways in front of their models to handle

What is AI lifecycle management?

What is AI lifecycle management?

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GPT-5.4 vs Claude Opus 4.6: a guide to choosing the right model

GPT-5.4 vs Claude Opus 4.6: a guide to choosing the right model

GPT-5.4 vs Claude Opus 4.6: head-to-head across 12 benchmarks: coding, tool use, reasoning, and more. See which model wins where, and when to use each.