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Welcome to ColabHive

Run inference, fine-tuning and model operations across heterogeneous CPU and GPU infrastructure you control.

ColabHive gives infrastructure and AI teams one maintained product and support path across those runtimes. Placement is capacity- and runtime-aware, while public APIs and resources are account-scoped. Private Cluster is the default operating path; Cloud Burst is an Enterprise add-on using the customer's DigitalOcean account, and Share Hive requires explicit account opt-in.

→ Private Agentic Infrastructure — the whole model in one page: capacity paths, CPU/GPU execution lanes, placement, privacy and explicit contract boundaries.

→ Trust & Operations — current security controls, privacy boundaries, operational status and the explicit absence of a general public SLA.

On top of that infrastructure, ColabHive gives you a curated catalog of ready-to-run models and lets you import any compatible model from Hugging Face on demand. Both live in the same catalog and are served through the same inference path — so you are never limited to a fixed list.

  • A curated base catalog — LLMs (GPU and CPU), embeddings/rerank/translate/STT/OCR/moderation specialists, image/audio/video/speech generators, plus trainable templates (tabular, time-series, fine-tuning). Browse it live at GET /api/builder/v1/inference/models.
  • Import from Hugging Face — search a repo, check compatibility, register it, and call it. The platform builds the endpoint for you; the model downloads on its first request.

Two ways to start​

Pick the path that matches what you want to do first. You can mix them freely later.

Running a coding agent?

To use OpenCode or another coding agent with models on ColabHive, go to Coding Agents: pip install colabhive and colabhive agents init configure it in one step.

Path 1 — Run inference now​

Call a model that is already in the catalog. No training, no setup beyond an API key.

from colabhive import ColabHive

client = ColabHive(api_key="hive_...", account_id="...")

result = client.endpoints.infer(
    "qwen-2.5-7b-instruct-public",
    {"messages": [{"role": "user", "content": "Say hello in one word."}]},
)
print(result["result"])

→ Quickstart: Inference — your first request to a real public LLM in ~2 minutes (SDK, REST, and the OpenAI-compatible endpoint).

Path 2 — Train your own​

Upload a dataset and train a model, then serve it through the same inference path.

dataset = client.datasets.upload(name="my_data", file="./train.csv")
job = client.training.create(model="xgboost-regression", dataset_id=dataset.id)
job.wait()

→ Quickstart: Training — train a model end to end.

Bring a model from Hugging Face​

Somewhere between the two: take any compatible Hugging Face repo and make it a live endpoint.

→ Quickstart: Import from Hugging Face — search → info → register → infer.

Install​

pip install colabhive

Get an API key and account ID from console.colabhive.com (Settings → API Keys). API keys start with hive_. Set them as environment variables so you don't paste secrets into code:

export COLABHIVE_API_KEY="hive_..."
export COLABHIVE_ACCOUNT_ID="..."
import os
from colabhive import ColabHive

client = ColabHive(
    api_key=os.getenv("COLABHIVE_API_KEY"),
    account_id=os.getenv("COLABHIVE_ACCOUNT_ID"),
)

The base URL defaults to https://api.colabhive.com. The Builder REST API is under /api/builder/v1; an OpenAI-compatible surface is mounted at /v1.

Optional outbound webhooks

The optional webhook contract sends terminal notifications for inference, agent invocations, training and merges when enabled for an account. Its management routes are in the public OpenAPI; polling remains the universal contract. See Outbound Webhooks for exact limits.

Understand the platform​

  • Private Agentic Infrastructure — the control plane, the three capacity tiers, the CPU and GPU execution lanes, and how privacy and placement actually work.
  • Platform Overview — how a request flows from the gateway through the orchestrator to GPU/CPU nodes, and the model tiers.
  • Model Catalog & Hugging Face — the difference between curated base models and Hugging Face imports, the hf-* naming, and the model lifecycle.
  • Inference Lifecycle — cold start, warm models, and how to keep latency predictable.
  • The Model Flywheel — merge and retrain models as first-class, reusable operations.
  • Elastic Cloud Burst — how the hive rents overflow GPU capacity in the cloud as a last resort, and releases it the moment it can.

Not sure which model to pick? See the Choosing a Model guide.

Documentation sections​

  • Get Started — quickstarts for inference, training, and importing from Hugging Face
  • Concepts — how ColabHive works
  • Models — the live catalog and per-model reference
  • Guides — task-oriented how-tos
  • API Reference — REST endpoints and authentication
  • SDK Reference — the colabhive Python client
  • MCP — use ColabHive from agent tooling
  • Examples — end-to-end walkthroughs

Need help?​

  • Discord: discord.gg/colabhive
  • Email: support@colabhive.com
  • Community install and license: Self-host Community