2 posts tagged with "Claude"

When using Claude via API, you have more than two options: in addition to calling the Anthropic API directly, you can also use it via AWS Bedrock, Google Vertex AI, or Microsoft Azure (Azure AI Foundry). Base pricing is the same across all routes, but there are differences in batch processing and cloud ecosystem integration.

Unit: USD / 1M tokens (MTok). Information as of March 2026.

On-Demand Base Pricing​

Base pricing is identical across all routes.

Note that Vertex AI regional endpoints carry a 10% surcharge over global endpoint pricing. Bedrock offers Long Context variants as separate SKUs at the same price; on the Anthropic API, Long Context is integrated into the standard models.

Cache Pricing​

Prompt Caching rates are also identical across all routes.

Cache writes come in two TTL tiers: 5-minute (short-term) and 1-hour (long-term). Longer TTL means higher write cost, but for applications with lengthy system prompts that are read repeatedly, the savings on read pricing more than compensate.

Batch Processing Pricing​

Bedrock, Vertex AI, and the Anthropic API all offer an asynchronous batch API at 50% off on-demand pricing. Azure does not explicitly list batch pricing at this time.

For large-scale batch workloads (log analysis, embedding generation, etc.), any of these routes can cut costs in half.

Ecosystem Comparison​

New features (such as Extended Thinking) roll out to the Anthropic API first; Vertex AI, Bedrock, and Azure typically follow weeks later.

Which Should You Choose?​

• Simple setup / prototyping: Anthropic API requires just one API key and gets new features first.
• Deep AWS integration: If you need IAM, CloudWatch, or VPC, Bedrock is the natural choice. Tokyo region supported.
• Deep Google Cloud integration: Vertex AI fits right in. Note the 10% surcharge on regional endpoints.
• Deep Azure integration: Available via Azure AI Foundry, integrated with Azure billing and management.
• Heavy batch workloads: Bedrock, Vertex AI, and the Anthropic API all offer 50% off batch pricing.

References​

• Anthropic API Pricing – Claude API Docs
• AWS Bedrock Pricing – Amazon Web Services
• Vertex AI Pricing – Google Cloud
• Claude in Microsoft Azure AI Foundry – Claude API Docs

Adding "skills" to an AI agent lets you extend its capabilities, just like installing a plugin for an app. This article explains how Agent Skills work and what an agent actually does internally when using them.

What Is an AI Agent?​

First, an AI agent is an AI program that receives instructions and autonomously completes tasks.

Unlike a simple AI that just answers questions (like ChatGPT in basic use), an agent can:

• Read and write files
• Execute code and check results
• Call external APIs and tools
• Make decisions across multiple steps on its own

What Are Skills?​

Agent Skills is a mechanism for giving agents new abilities and domain knowledge.

Think of it like handing a new employee a work manual. Once the agent reads the manual (the skill), it understands how to approach that task correctly.

Without skills: "Write a blog post" → Agent writes something generic
With skills:    "Write a blog post" → Agent follows the manual and produces consistent, quality output

Skills are primarily written as Markdown files (SKILL.md) and can include:

• Step-by-step procedures: What to do and in what order
• Scripts: Automatable processes
• Samples and config: Resources for the agent to reference

Why Are Skills Needed?​

AI agents are extremely capable, but they don't know anything specific about your project.

For example:

• "How does this team write commit messages?"
• "What frontmatter format does this blog use?"
• "Which commands are used for deployment?"

Without skills, agents can't know any of this. Skills let agents understand "the right way to do things" before acting.

How an Agent Processes a Skill​

Let's look at what's happening inside the agent.

Here are the key points:

1. Loading the Skill​

The agent reads the skill at the start. The skill content is passed as part of the LLM's input (prompt). The LLM reads this and understands "the right approach for this task."

2. Breaking Down the Task​

Based on the instructions, the LLM breaks the task into smaller steps: "Read 3 existing posts first," "then decide on a filename," "then write the frontmatter," and so on.

3. Calling Tools​

At each step, the agent calls tools as needed — reading files, searching the web, executing code — following the procedure defined by the skill.

4. Feeding Back Results​

Tool results are passed back to the LLM. The LLM looks at the results and decides what to do next, looping until the task is complete.

Skill Commands​

Skills can be invoked as slash commands (/command-name).

When a command is called, the corresponding Markdown file's content is expanded as a prompt, and the agent begins executing those steps.

Skills Are Growing​

The Agent Skills format was developed and open-sourced by Anthropic and is now supported by many tools:

The biggest advantage is that the same skill can be reused across different tools.

Summary​

• Skills are a mechanism for giving agents specialized knowledge and procedures
• You can create one by writing steps and rules in a Markdown file (SKILL.md)
• The agent receives the skill as a prompt; the LLM interprets it and executes each step
• It's an open standard supported by Claude Code, Cursor, GitHub Copilot, and many more

With skills, you no longer have to explain the same things to your AI every time — agents can perform tasks with consistent quality, exactly the way you want.