Any model, any runtime
No lock-in. Mix providers across your team.
Claude
GPT
Gemini
Cursor
Grok
Mistral
Perplexity
DeepSeekMix providers across your team, or bring your own with a custom adapter.
Local coding agents
Run Claude Code, Codex, Cursor, Gemini, or OpenCode as agents directly on your own machines.
- Claude Code, Codex, Cursor, Gemini, OpenCode
- Run on your own machine or a desktop app
- Ideal for code and deep, tool-heavy work
Cloud & hosted models
Use any model the Vercel AI Gateway exposes, or run in your own cloud, from frontier models to fast and cheap ones, per agent.
- Any model via the Vercel AI Gateway
- Run in your own AWS, Azure or GCP
- Pick frontier or fast-and-cheap, per agent
Custom adapters
Implement one small interface to run agents on your own infrastructure or a provider we do not ship yet.
- Implement one small interface
- Run on your own infra or any provider
- The runtime owns budgets, retries and lifecycle
Run any engine, local or in your cloud
How bring your own agents works
- 1
Pick an engine per role
Choose Claude Code, Codex, Cursor, Gemini, OpenCode, the AI Gateway, or your own.
- 2
Assign it to an agent
Each agent runs on the engine that fits its job and budget.
- 3
Run anywhere
Local coding agents on your machines, hosted models in the cloud, or custom infra.
- 4
Swap any time
Change an agent’s engine later with no rewrite. The lifecycle stays the same.
See it in context
Right-size the cost
Before
Every agent runs on the same expensive frontier model, even the ones doing trivial triage.
With Padiso
Cheap, fast models handle high-volume triage; frontier models are reserved for the work that needs them.
A better model ships
Before
Switching providers means a painful migration across your whole agent codebase.
With Padiso
You point the relevant agents at the new engine and keep moving. Everything else stays untouched.
What teams use it for
Mix providers per team
Use the strongest engine for each role instead of one vendor for everything.
Run local coding agents
Claude Code, Codex, Cursor, Gemini, or OpenCode directly on your own machines.
Add an unsupported provider
Write a small adapter to run agents on infrastructure Padiso does not ship yet.
The numbers behind agentic operations
You don't have to take our word for it. Here's what the analysts and operators are reporting right now.
50%
of enterprises using generative AI will deploy autonomous agents by 2027, double the 2025 rate.
23%
of organizations are already scaling agentic AI, with another 39% running experiments.
40%+
of agentic AI projects risk being scrapped by 2027 without clear value, a case for starting scoped.
Most teams reach payback within a quarter or two of going live.
Padiso estimate, based on typical back-office workloads: hours saved on repetitive work, fewer errors, and coverage that no longer needs extra headcount. Your mileage depends on volume and the processes you automate first.
It already works at this scale
Not a demo. A team in the same kind of work, with results they published.
Klarna
Customer service
By Q3 2025, Klarna’s AI assistant was doing the workload of around 853 employees and driving an estimated $60M in savings, while keeping resolution times a fraction of human handling.
$60M
estimated savings
853
employees’ worth of work
2/3
of support chats
The strongest results come from scoped use cases with connected data and clear KPIs, and from keeping humans in the loop on the hard cases. That is exactly the model Padiso is built around.
6+
runtimes supported out of the box
Use the best engine for each job, and swap providers without rewriting a thing.
Run your business on agents.
Start free, or talk to us about putting an agent workforce to work across your operations.