Semantic Kernel Review 2026: Features, Pricing, Alternatives

Who Built Semantic Kernel, and What Happens to It Now?

What Is Semantic Kernel?

• Kernel: the central object that holds registered AI services (chat completion, embeddings), plugins, and configuration — every Semantic Kernel application builds one
• Plugins: native code functions or prompt templates, decorated with metadata (@kernel_function in Python, `[KernelFunction]` in C#), that the model can discover and call — Semantic Kernel's equivalent of "tools" in other frameworks
• Planners: logic that decides which plugin(s) to invoke to satisfy a request; the modern default relies on the model provider's native function calling (FunctionChoiceBehavior.Auto()) rather than the older dedicated planner classes
• Vector store connectors (memory): standard interfaces for storing and retrieving embeddings across supported vector databases, used for retrieval-augmented generation
• Process Framework: a workflow abstraction built on top of the Kernel for longer-running, multi-step business processes
• Enterprise components: built-in filters, observability and telemetry hooks, and structured logging aimed at production Azure deployments

import asyncio
from semantic_kernel import Kernel
from semantic_kernel.connectors.ai.open_ai import AzureChatCompletion
from semantic_kernel.connectors.ai.function_choice_behavior import FunctionChoiceBehavior
from semantic_kernel.contents.chat_history import ChatHistory
from semantic_kernel.functions import kernel_function

class LightsPlugin:
    @kernel_function(description="Turns a light on or off")
    def change_state(self, is_on: bool) -> str:
        return f"Light is now {'on' if is_on else 'off'}"

async def main():
    kernel = Kernel()
    kernel.add_service(AzureChatCompletion(deployment_name="gpt-4o-mini", api_key="...", base_url="..."))
    kernel.add_plugin(LightsPlugin(), plugin_name="Lights")

    history = ChatHistory()
    history.add_user_message("Turn on the light")

    chat = kernel.get_service(type=AzureChatCompletion)
    result = await chat.get_chat_message_contents(
        chat_history=history,
        settings=AzureChatCompletion.get_prompt_execution_settings_class()(
            function_choice_behavior=FunctionChoiceBehavior.Auto()
        ),
        kernel=kernel,
    )
    print(result[0])

asyncio.run(main())

How Much Does Semantic Kernel Cost?

• Semantic Kernel (the SDK): free forever, MIT license, self-hosted or cloud, no usage caps on the framework
• Model API costs: pay-as-you-go for whichever LLM provider you connect (Azure OpenAI Service, OpenAI, and others) — Semantic Kernel does not host or resell model access itself
• Azure hosting costs (optional): deploying on Azure App Service, Azure Functions, or Azure Kubernetes Service carries standard Azure compute pricing — Semantic Kernel does not require Azure, but Microsoft's own samples and enterprise components assume it by default
• Vector database costs (optional): connecting Azure AI Search or another vector store for memory/RAG carries that service's own separate pricing
• Support: Microsoft's committed maintenance window (critical bug fixes and security patches) for existing Semantic Kernel deployments runs at least into 2027, at no extra cost beyond your existing Microsoft or Azure agreements

How Do You Install and Get Started With Semantic Kernel?

1. 1
   Install the SDK for your language: dotnet add package Microsoft.SemanticKernel (.NET), pip install semantic-kernel (Python), or add the com.microsoft.semantic-kernel dependency (Java).
2. 2
   Set your model provider's credentials as environment variables or configuration — Semantic Kernel does not provide model access itself; connect Azure OpenAI, OpenAI, or another supported connector.
3. 3
   Create a Kernel, add an AI service (for example AddAzureOpenAIChatCompletion in .NET or AzureChatCompletion in Python), and register any plugins the model should be able to call.
4. 4
   Enable automatic function calling (FunctionChoiceBehavior.Auto()) so the model can invoke registered plugins directly, without writing a dedicated planner.
5. 5
   For a brand-new project, evaluate whether to start directly on Microsoft Agent Framework instead — Microsoft's stated successor, generally available since April 2026 — rather than building on an SDK now in maintenance mode.
6. 6
   Read the official Semantic Kernel documentation and the GitHub repository for the current API reference and migration guides.

Who Should Use Semantic Kernel?

When Should You NOT Use Semantic Kernel?

• Starting fresh in 2026 with no existing Semantic Kernel investment — go straight to Microsoft Agent Framework, Microsoft's own general-availability successor, instead of building on an SDK in maintenance mode
• A team outside the .NET/Azure ecosystem that would benefit from the larger community, tutorials, and integration catalog around LangChain or LlamaIndex
• A project that specifically needs AutoGen/AG2-style multi-agent conversation patterns — named agents debating or negotiating in a group chat — rather than a single kernel calling plugins
• A lightweight script or prototype where a direct API call to the model provider is simpler than adopting the Kernel/plugin/planner abstraction
• Use Semantic Kernel instead of switching mid-project only when you already have a working production deployment and the committed support window covers your timeline

Semantic Kernel vs. Alternatives

Common Mistakes When Evaluating Semantic Kernel

Frequently Asked Questions

Sources

Related Reading

• LangChain Review: Features, Pricing, Alternatives — the largest general-purpose alternative, Python/JavaScript-first with a bigger community than Semantic Kernel.
• AutoGen Review: Microsoft's Multi-Agent Framework — the other Microsoft Research project that Microsoft Agent Framework converges with Semantic Kernel.
• CrewAI Review: Role-Based Multi-Agent Framework — a Python-first alternative built around role-based agent "crews" rather than a kernel/plugin model.
• LangGraph Review: Features, Pricing, Alternatives — a graph-based agent runtime for teams that need explicit state, cycles, or human-in-the-loop control.
• LlamaIndex Review: RAG Framework Compared — a retrieval-focused alternative for teams whose primary need is document indexing and RAG.
• Local LLM Software Directory — the full catalog of local AI tools this article belongs to, including where Semantic Kernel fits among agent frameworks.