The Coding Agent Harness
That Runs on your own LLMs.
Podcode deploys a private model to your GPU account in a few clicks, keeps it running, and puts a full agentic coding harness on top. Already host your own models? Connect those instead. Have a Codex subscription, or use other frontier models? No problem, connect those too.
Open models will catch up. Your stack should be ready.
We believe open models will one day be as good as frontier models. When they are, the teams that run their own will win, so we are building the infrastructure that makes deploying your own private models easy, starting now.
Private by default
Run models on your laptop, on your own GPUs, or in your own cloud account. Your code and prompts go where you send them, and nowhere else.
Open models, first-class
Qwen, Llama, gpt-oss, Kimi, MiniMax and more, through Ollama, llama.cpp, LM Studio or vLLM. Swap in each new release the day it ships.
Self-deploy in a few clicks
Deploy a model to your own RunPod account from inside Podcode, pause it when you are done, and resume it in a click. No serving stack to build.
A harness tuned for smaller models.
Bigger isn’t the only way to better. Shared memory, agent profiles, per-task routing and a plan-check-retry loop do the work a bigger model would otherwise have to, so the open models you host punch above their weight.
Memory that compounds
The model forgets. The harness doesn’t. Workspace memory carries your decisions, fixes, and conventions into every session, so a smaller model starts with the context a bigger one would have to be re-taught.
Profiles and routing
Reviewer for review, Builder for code, Researcher for digging. Routine work runs on the economical model; hard tasks escalate. You pay frontier prices only when a task actually needs them.
Plans, checks, retries
The agent plans before it edits, runs the tests, and reworks what fails. A disciplined loop beats a bigger brain with no process.
Everything around the model, built in.
A capable agent is more than a model. Podcode handles the parts that make it dependable, from staged runs and isolated worktrees to sub-agents and a live browser, so a smaller model can take on real work.
Stages with checks between them
A harness runs the work as plan, implement, review, with an acceptance check before each hand-off. If the tests keep failing, the run stops and tells you why instead of shipping a guess.
Its own worktree, your call to apply
Every session works in an isolated git worktree. Nothing lands in your repo until you apply it, and conflicting merges come back as proposals you review.
Sub-agents that work in parallel
The lead agent hands exploration, research and review to sub-agents that run side by side, each in a tab you can open to see exactly what it did.
A browser and terminal of its own
Agents start your dev server, open the page in a live browser and check the result for themselves. Take control of the browser whenever you want.
Sessions that don’t run out of room
Automatic compaction keeps long sessions going, and output limits are sized to each local model’s context window, so small models don’t fall over mid-task.
You stay in control
Approve the plan before anything is edited, choose which tools run without asking, and watch context, spend and every tool call live as the agent works.
Spin up in minutes. Pause when you stop.
Deploy your private model in a click, then let it sleep. A paused pod isn’t billing. When you’re back, it resumes in a click - same model, same weights, right where you left off.
x7hq2kd9mzp3rareadyep-m31xq9cv02readyk2c81pqa7wn4tepaused 2h agoDeploy a model, then start a session on it. Click anywhere to try it yourself.
The agent and the hosting, in one app.
Coding-agent tools give you an interface. GPU platforms give you hardware. Podcode gives you both, already working together.
A good interface, but hosting the model is left to you. Until you do, your code goes to their cloud.
Raw hardware. Choosing a model, serving it and running an agent on top are left to you.
Pick a model, deploy it to your machine or your own GPU, and start a session. The agent and the hosting are built to work together.
Your code, your prompts and your agent traces stay in your tenancy.
Run in your cloud account, your Kubernetes cluster, your own datacenter or fully air-gapped, under the controls your security team already requires.
Predictable AI spend, however much your team uses it.
Per-token pricing grows with every prompt, retry and agent step, so your bill rises as adoption does. With Podcode, your team's AI cost is the GPU capacity you choose plus a flat fee on Enterprise, both set before the month starts. Usage can grow. The bill doesn't.
Seamless self-hosting for your own private LLMs.
Your own LLM costs less than you think.
Per-token pricing charges you for every step an agent takes. Run the model yourself, on your machine or your own GPU, and the cost is the hardware, whether the agent takes ten steps or ten thousand.
Private agents,
without the platform team.
Free for individuals. Download Podcode, point it at the models you run, and start your first session in a few minutes.