Semantic Kernel (SK) is Microsoft’s answer to the chaotic world of LLM orchestration—an open-source SDK that treats AI agents not as magic, but as enterprise software components. Unlike LangChain, which often feels like a collection of scripts taped together, SK is built with the rigor of dependency injection and type safety that .NET shops demand. It costs nothing to license (MIT), but the real price you pay is in token overhead and Azure integration lock-in.
Calculated practically, SK imposes a “reliability tax” on your token bill. If you build a customer support agent using raw API calls, a simple “reset password” query might consume 300 tokens. Wrap that same logic in a Semantic Kernel Agent with three plugins (Email, Auth, CRM), and your input context balloons to 1,500+ tokens just to define the schema for the model. For a workload of 10,000 requests/day on GPT-4o, this jumps from ~$1.50/day to ~$7.50/day. You are paying a 400% premium for the framework’s ability to autonomously select tools and handle errors. For an enterprise handling sensitive data, that stability is worth every penny; for a bootstrapped startup, it’s unnecessary bloat.
The architecture is clean. Concepts like “Kernel,” “Plugins,” and “Connectors” map clearly to traditional software patterns. The new Agent Framework (GA as of early 2026) replaces the older, confusing “Planners” with a robust event-loop system that mimics how operating systems manage processes. It handles the messy reality of AI—infinite loops, hallucinated arguments, and retries—better than any competitor.
However, Semantic Kernel has a clear caste system. If you are a C#/.NET developer, this is the best tool on the market, period. The integration with Visual Studio and Azure is seamless. If you are a Python developer, you are a second-class citizen. Features land in .NET months before they reach Python, and the documentation frequently forces you to translate C# concepts into Python code mentally.
Skip Semantic Kernel if you are building a simple RAG app or a Python-based prototype; the boilerplate isn't worth it. Use it if you are in a corporate Microsoft environment building complex, multi-step agents that need to survive a security audit.
Pricing
The SDK is free (MIT License), but 'free' is deceptive in agentic workflows. The framework's 'auto-function-calling' and 'agent' capabilities rely on injecting verbose JSON schemas into every prompt.
In a production environment processing 50k interactions/month, Semantic Kernel's orchestration overhead can add ~$200-500/month in 'hidden' token costs compared to hand-rolled API calls. There is no hosted tier or markup, but it is heavily optimized for Azure OpenAI, where it shines; using it with non-OpenAI models often requires writing custom connectors, adding development cost.
Technical Verdict
Enterprise-grade architecture with a steep learning curve. The C# SDK is a masterpiece of modern .NET design—dependency injection, logging, and telemetry are first-class citizens. The Python SDK is functional but verbose. Latency is purely a function of the underlying LLM, but the framework adds ~5-10ms of overhead per step. Reliability is its main selling point; it catches the edge cases that crash LangChain prototypes.
Quick Start
# pip install semantic-kernel
import asyncio
from semantic_kernel import Kernel
from semantic_kernel.connectors.ai.open_ai import OpenAIChatCompletion
kernel = Kernel()
kernel.add_service(OpenAIChatCompletion(service_id="chat", ai_model_id="gpt-4o", api_key="sk-..."))
print(asyncio.run(kernel.invoke_prompt(prompt="Hello, World!")))Watch Out
- The Python SDK lags behind C# by 3-6 months; documentation often links to C# examples.
- Using 'Auto Function Calling' without limits can trigger infinite loops, draining your API credits instantly.
- Legacy 'Planner' classes are deprecated but still litter search results; ensure you use the new 'Agent' APIs.
- Default telemetry is verbose; configure filters early or it will spam your logs.