Does my data train public foundation models?

No. Overmind states there is zero third-party training — your proprietary operational data never leaves your pipeline to train public foundation models.

Overmind — Turn agent production data into models you own

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Key Features

Agent observability with code context

Connect your repository and production traces to see how your agent makes decisions — inspect the failing step, the tool call, and the context behind it. The platform scans your code, discovers agent capabilities, and maps tools, prompts, and control flow.

Data Workshop for trace-to-dataset preparation

Build datasets from your agents' production traces. Clean, filter, and prepare examples for training and evaluation, capturing knowledge specific to your business. Includes quality checks and a Workshop chat agent.

Evaluations and optimization against production tasks

Test prompts and models against real tasks from your production data. Compare results with your current model, inspect failures, and catch regressions before deployment. Runs include evaluation gates and judges.

Training and deployment of models you own

Fine-tune an open model on your own data with managed training jobs and live run dashboards. You own the weights — host on Overmind via an OpenAI-compatible API or download checkpoints and serve them yourself.

OTLP trace ingestion across Console, CLI, and MCP server

Send traces via OTLP, browse spans and sessions, and access the platform from the web Console, the Overmind CLI, or an MCP server.

How It Works

Code and production context

Connect your repository and production traces to find the cause of agent failures: inspect the failing step, the tool call, and the context behind it.

Data preparation

Turn real work into training data by building datasets from production traces in the Data Workshop.

Evaluation and optimization

Prove improvements before you ship: test prompts and models against real production tasks, compare with your current model, and catch regressions.

Training and deployment

Fine-tune an open model on your own data; Overmind handles training and deployment while you keep ownership of the weights.

Pros & Cons

Pros

Covers the full lifecycle — tracing, data preparation, evaluation, training, and deployment — in one platform
You own the trained weights and can download them via Console or CLI to self-host
Open source (AGPL-3.0) and self-hostable via Docker Compose
Free tier includes real capabilities, including 2 training jobs and 2 deploy jobs per month plus unlimited eval runs
Serves trained models through an OpenAI-compatible API for easy integration
Zero third-party training: proprietary data isn't used to train public foundation models

Who It's For

Best for

Engineering teams shipping agents that need to improve from production data
Teams that want to replace costly frontier-model calls with smaller fine-tuned models for narrow, repetitive agent tasks
Organizations with security or compliance needs that want self-hosting and weight ownership
Teams that want to measure candidate prompts and models against real production tasks before shipping

Not ideal for

Users who want to train frontier models themselves — the platform focuses on fine-tuning open models
Teams needing a purely closed-source SaaS with no self-hosting option

Use Cases

Diagnosing why production agent runs fail using traces combined with repository context

Converting production traces into curated training and evaluation datasets

Fine-tuning smaller open models for specialized agent tasks like labeling emails or calling APIs

Running evals and regression checks against a baseline model before deploying changes

Hosting a fine-tuned model behind an OpenAI-compatible API or serving downloaded weights on your own infrastructure

Pricing

Free plan at $0/month for prototyping and instrumentation with limited optimize, training, and deploy runs, plus a $50 one-time credit. Pro at $120/month removes those limits and adds unlimited projects and priority support. Enterprise is custom-priced with SSO, dedicated support, and compliance agreements. Note that training, inference, Agent Optimizer runs, and Data Workshop chat consume credits

Free

$0 / month

For prototypes, demos, and getting agents instrumented. Includes fair-use OTLP ingest, mapping of tools/prompts/control flow, up to 10 optimize runs per month, upload and quality checks with Workshop chat, unlimited eval runs, up to 2 train

Pro

$120 / month

For teams shipping agents that need to improve in production. Same features as Free with higher limits: unlimited optimize runs, eval runs, training jobs, and deploy jobs; unlimited projects; priority support.

Enterprise

Custom

For organizations with security, scale, and compliance needs. Everything in Pro plus dedicated technical support, SSO & custom auth, credentials encrypted at rest with compliance scope agreed per contract, custom onboarding & playbooks, and

FAQ

Can I train and deploy a model on Free?

Yes. Free includes up to 2 training jobs and 2 deployments a month. You can prepare your dataset in the Data Workshop and serve your trained model through an OpenAI-compatible API.

How does the $50 starter credit work?

New accounts receive a $50 one-time credit. Training, inference, Agent Optimizer runs, and the Data Workshop chat agent use credits. When your balance runs out, add credits in Settings to start new training, eval, deployment, or optimization runs. You can still send traces and view existing results, and your plan's monthly limits still apply.

Can I take my trained model elsewhere?

Yes. You can download the trained weights and checkpoints from the Console or with the Overmind CLI, then serve them on your own infrastructure.

Is Overmind open source?

Overmind