Execution visibility
See what happened in each run
Inspect model calls, tool activity, sub-agent steps, recorded costs, and duration. Open the details when a result needs explaining.
You’re putting more AI into production. Do you know what it’s doing?
Know what your AI is doing.
Know what it costs.
Build prompts, agents, and workflows in one workspace. Connect your tools, deploy your AI, and inspect each run’s steps, outputs, and recorded costs—so you can decide what to improve.
Free Developer plan · No credit card required
What did we spend on AI yesterday?
Which agent is costing us the most?
Why did this workflow get 3× more expensive?
What actually happened inside that failed run?
Was any of it worth it?
Fetch Hive gives you the answer.
An AI bill tells you what you spent. You still need to understand the work.
Which run used those tokens? What did the agent call? What did it produce? Fetch Hive brings the execution details together so your team has something concrete to inspect.
Where did the spend go?
Follow the cost back to the run.
Inspect the model and tool usage behind the work.
What happened?
Follow the execution step by step.
Review calls, outputs, timing, and failures.
Was the result useful?
Review what the run produced.
Use the output and its recorded cost to decide whether the workflow is worth repeating.
Judge the work alongside the cost.
A completed run is only the beginning. Review what it produced, inspect the recorded cost, and decide whether the result meets your team’s needs.
01
The task
Produce a company research brief with sources. The input is a company URL; the expected output is a one-page document your team can read.
02
The execution
Every model call, tool call, duration, and recorded cost. Open a meaningful step to see its input and output.
03
The output
A readable excerpt from the resulting brief, with sources where supported, next to the run summary.
Before you repeat this run, ask:
Does it answer the question?
Are the sources useful?
How much editing does it need?
Is the result worth repeating at this cost?
Value is a human judgement. Fetch Hive gives you the execution details and recorded cost to make it.
Start with one workflow you can understand.
Define the work, connect the tools, run it, and read the result. Each run records its steps and cost so you can improve the next one.
01
Define the work
Write the prompt or design the workflow visually. Set inputs, expected output, and which model handles each step.
02
Connect the tools
Add integrations, knowledge, and sub-agents the run can call. Each becomes a visible step with its own recorded usage.
03
Run and inspect
Trigger the run, then open it: steps, calls, outputs, timing, and recorded cost—ready to review or improve.
Build the workflow. Understand the run. Improve the next one.
Prompts, agents, tools, and execution details belong together. Give your team one place to build AI and understand how it behaves.
Connected tools
Give agents the tools to finish the job
Connect search, apps, workflows, and specialist agents so your AI can gather context and take action. Inspect the tool activity in the run afterward.
Delegate complex work. Keep the steps visible.
Hive Agent breaks a task into specialist steps, runs them with your tools, and records each one—so delegation never means losing sight of what happened.
Hive Agent · Task
Prepare a company research brief
3 specialist steps
1m 43s
Usage recorded per step
Research
Searched sources and gathered facts
Sonar Pro
Completed · 38s
Analysis
Compared findings and flagged gaps
Claude Sonnet 4
Completed · 21s
Writing
Drafted the brief with citations
Gemini 2.5 Pro
Completed · 44s
Put AI to work in the tools your team already uses.
Connect 50+ apps once. Workflows read from and write to them directly, and every tool call is recorded in the run—so the output lands where your team works, with the steps behind it.
Source documents
Input
Research workflow
Runs in Fetch Hive
Brief in Google Docs
Output
Slack notification
Delivered
Understand what you’re paying for.
Fetch Hive separates platform usage from hosted model usage. Review the plan allowance and model charges before choosing how to run your AI.
Platform usage
Your plan provides a task allowance. Different operations consume tasks; a multi-step workflow can use more than one.
Hosted model usage
Calls through Fetch Hive-managed model access use a separate hosted credit balance.
Your own provider keys
Eligible plans let you use supported provider credentials. Provider billing and Fetch Hive platform usage remain separate.
Know what to expect before your first run.
Understand what Fetch Hive records, how you can use it, and how usage is billed.
Inspect the execution details recorded for the run, including model calls, tool activity, outputs, status, timing, and usage. The available detail depends on the run type and the underlying service.
Still have questions?
