Your own model endpoint in 3 clicks.
Train it in TypeScript.
Start on a hosted endpoint, collect real traffic, then fine-tune the weights and serve the result from code.
Built for coding agents, not dashboards.
Free during the alpha. No credit card.
From hosted endpoint
to your own weights.#
Get your endpoint
Three clicks give you a hosted, OpenAI-compatible Gemma endpoint with its own URL and revocable API keys.
Let the logs accumulate
Point your app at it. Requests and responses are stored as runs you can replay and turn into training data.
Fine-tune in TypeScript
Write the training run as code with the open-source Arkor framework, or let your coding agent write it. Managed GPUs run it. The weights change only when you decide.
Serve your weights
Training produces a LoRA adapter. Load it onto the base model at the same endpoint and route traffic to it.
Trusted by professionals from
Supported by
Who Arkor is for.#
For builders
Ship an LLM feature on a model you operate.
You build a product in TypeScript and want the model behind it to be something you can inspect, tune, and serve on your terms.
Support triage
Classify and route tickets with a model tuned on how your team actually labels them.
Extraction on your documents
Pull structured fields from the formats your backend really receives.
Domain-specific assistant
An in-product assistant that answers in your product's vocabulary.
For providers
Serve tuned models to your own users.
You deliver models to clients or end users and need endpoints you can hand over, swap, and revoke without running inference infrastructure.
Client deliverables
Deliver a tuned endpoint per client, each with its own URL and revocable API keys.
Vertical SaaS models
Expose a domain model as part of your product, versioned like the rest of your stack.
Published research models
Put a fine-tuned adapter behind a stable URL others can call.
Hear it from the founder.#
Hina@soleil_colza_Introducing Arkor: Your own LLM endpoint that improves while you sleep 🐑💤 Get your model in 3 clicks, swap your existing endpoint, and let it train from production logs. I’ll be sharing more about it in daily videos! DM if you want your own model for your product.July 3, 2026 · View on X →
Hina@soleil_colza_natural language is part of your codebase now. stale comments and docs give coding agents bad context, and bad context produces bad code. we built drift-check, a linter for natural-language drift. It’s cut our merge/review time and bot costs by ~30%. our friends are loving it 🫶🏻July 6, 2026 · View on X →
Ask me anything.#
How to use open-weight models in your product, how to start fine-tuning, or how to ship personalized AI features for your users.
I'm happy to help!
See what fine-tuning changes.#
Run a support-triage call against a base open-weight model, a fine-tuned version, and a frontier reference. Same input, same prompt.
Support triage
A customer message arrives. The model reads intent, assigns a category and urgency level, and recommends a next action. No keyword rules, no routing trees.
Waiting for input.
Waiting for input.
Waiting for input.
Try the base model
A code review bot on Arkor's base model.#
drift-check flags stale comments and docs on every pull request. It runs on plain Gemma 4 31B on Arkor, the same base model your endpoint starts with, and it works surprisingly well. Install it to feel the starting point.
Your code and review results are not stored.
Your model.
Your endpoint. Your terms.#
A hosted endpoint in under a minute. Free during the alpha.
Get your endpoint