mlop
mlop is a San Francisco-based, Y Combinator-backed open-source MLOps platform offering experiment tracking, real-time monitoring, and anomaly alerts for machine learning engineers and ML teams, with 100% Weights & Biases API compatibility.
- Company typePrivate
- Founded2025
- HeadquartersSan Francisco, United States
- Headcount1–10
- GTM typeB2B
- OfferingSoftware
What mlop does
mlop is a privately held B2B SaaS company headquartered in San Francisco that builds an open-source MLOps platform for machine learning engineers and data scientists. The core product is an Experiment Tracking Platform that enables users to log metrics, parameters, gradients, and artifacts in real time, visualize model architecture and training progress, monitor system metrics (CPU, GPU, memory) automatically, and receive alerts via email, Slack, and Discord when training degrades. The platform is distributed via GitHub and PyPI, integrates natively with PyTorch, PyTorch Lightning, and Hugging Face Transformers, and is 100% API-compatible with Weights & Biases to support one-line migration from incumbent tools. A proprietary actionable insights engine detects anomalies such as exploding gradients and suggests fixes, and the company has an open roadmap extending into managed Compute Instances (private beta) and Inference (coming soon).
The business model is freemium with a self-serve free tier ($0/month, 1 seat, 10 GB storage, no credit card required), a custom-priced Pro tier for businesses scaling with AI (up to 10 seats, 100 GB storage), and a custom-priced Enterprise tier offering unlimited seats and storage, self-hosted deployment, security audits, and 24/7 founder support. Go-to-market is product-led and community-led, leveraging the open-source codebase, an active Discord community, Google Colab tutorials, and documentation as the primary acquisition surface, with direct sales reserved for the Enterprise tier. mlop is backed by Y Combinator (W25 batch) and closed a $500,000 seed round on 2025-06-11; the company operates with 1-10 employees and is incorporated as mlop, inc. in the United States.
mlop firmographics
Firmographics- Name
- mlop
- Legal name
- mlop, inc.
- Website
- https://mlop.ai
- Company type
- Private
- Founded year
- 2025
- Operating status
- Operating
- Headcount range
- 1–10 employees
- Short description
- mlop is a San Francisco-based, Y Combinator-backed open-source MLOps platform offering experiment tracking, real-time monitoring, and anomaly alerts for machine learning engineers and ML teams, with 100% Weights & Biases API compatibility.
- Ownership category
- akta.pro rank
mlop industry classification
Industry- Product category
- MLOps Platform / Experiment Tracking Software
- NAICS
- Software Publishers (5132)
- SIC
- Services-Prepackaged Software (7372)
- akta.pro primary industry
- Experiment Tracking, Metadata & Model Registry (HDAAABAC)
- akta.pro secondary industry
- ML Lifecycle Collaboration & Workspace Management (HDAAABAK)
Keywords
Where mlop is headquartered
LocationHeadquarters
- HQ city
- San Francisco
- HQ country
- United States
- HQ region
- North America
Markets served
mlop business model
Business model- GTM type
- B2B
- Offering type
- Software
- Cost components
- Technology or R&D, Personnel, Infrastructure, Marketing or Sales, Operations
Revenue model
- Subscription - Free Tier: Free tier for personal use or small teams with unlimited logging hours and 10 GB storage. Serves as acquisition and adoption driver for the platform.
- Subscription - Pro Plan: Pro plan for businesses scaling with AI. Includes up to 10 seats and 100 GB storage with email support. Custom pricing, contact-based sales.
- Subscription - Enterprise Plan: Enterprise plan for large-scale organizations with unlimited seats, unlimited storage, 24/7 founder support, self-hosted option, security audit, and private Slack channel.
Pricing tiers
| Model | Billing | Price |
|---|---|---|
| Freemium | Monthly | Free - Personal use or small teams |
| Subscription | Multi-year contract | Pro - Businesses scaling with AI |
| Subscription | Multi-year contract | Enterprise - Large-scale organizations |
Go-to-market motion2 records
Distribution channels4 records
Marketing channels7 records
mlop product offering
Product offeringCore offering
mlop provides an open-source MLOps platform for tracking, optimizing, and collaborating on machine learning experiments. The platform offers real-time monitoring of metrics, parameters, and gradients; multi-media logging for images, audio, and video; model graph visualization; and automated alerting across Email, Slack, and Discord. It is 100% API-compatible with Weights & Biases, enabling easy migration for ML engineers and data science teams via a Python SDK and web dashboard.
Product overview
mlop is a unified open-source MLOps platform built for experiment tracking. The core offering is the Experiment Tracking Platform, which integrates with PyTorch, PyTorch Lightning, and Hugging Face Transformers to provide real-time monitoring of metrics, parameters, and gradients. The platform includes Smart Analytics Dashboard for visualization, Alerting and Notifications for anomaly detection (including GPU time savings), and is 100% API-compatible with Weights & Biases for easy migration. Compute Instances (Private Beta) and Inference (Coming Soon) expand the platform into broader ML lifecycle management.
Differentiator
Problem solved
Functional benefit
Products and services
- mlop Experiment Tracking Platform An open-source MLOps platform for tracking, optimizing, and collaborating on machine learning experiments. Provides real-time monitoring of metrics, parameters, gradients, and system resource usage (CPU/GPU/memory); multi-media logging for images, audio, and video; model graph visualization for PyTorch and PyTorch Lightning; automated anomaly detection and alerting through Email, Slack, and Discord; Git status tracking for reproducibility; and 100% API compatibility with Weights & Biases for migration. Targeted at ML engineers, data scientists, and ML platform teams, with self-serve free, Pro, and Enterprise plans.
Quantifiable outcome
- Save hours of GPU time through real-time alerting when training issues occur
- +1 more outcomes
Companies that use mlop
Customer profileSegments4 records
Ideal customer profiles2 records
mlop technology and API
TechnologyTechnology focussed Yes
API detail
- Has API
- Yes
- API docs
- API detail
Core technology
AI maturity
App detail
Integration6 records
AI capability6 records
Feature6 records
mlop partnerships and signals
Strategic signalScale indicators3 records
Recent moves6 records
Expansion highlights6 records
mlop competitors and assessment
Company assessmentDirect peers
- Weights & Biases: Category-leading ML experiment tracking platform with the most direct product overlap; mlop is explicitly positioned as a 100% W&B-compatible alternative, serving the same ML engineers with the same core workflows (runs, metrics, artifacts, sweeps).
- MLflow: Open-source ML lifecycle platform originally from Databricks that handles experiment tracking, model registry, and deployment; competes with mlop on the same tracking and model management surface, especially in Python-first, self-hosted environments.
- Comet.ml: End-to-end ML experiment tracking, model monitoring, and evaluation platform serving ML engineers and data scientists; comparable in target persona, freemium PLG motion, and feature breadth (tracking, artifacts, alerts, collaboration).
- Neptune.ai: Experiment tracking and model registry platform with strong focus on team collaboration, reproducibility, and metadata management; very similar feature surface to mlop and serves the same ML engineer persona.
- ClearML: Open-source MLOps suite covering experiment tracking, orchestration, data management, and model serving; comparable because it offers both self-hosted/community editions and enterprise plans and overlaps with mlop's planned compute and inference roadmap.
- AimStack: Open-source, self-hostable ML experiment tracking tool with comparisons table aimed explicitly at W&B, MLflow, and TensorBoard; directly comparable to mlop's open-source, developer-first positioning.
- Valohai: ML orchestration and experiment tracking platform with self-hosted enterprise deployment and a focus on reproducibility and compute orchestration — comparable to mlop's combination of experiment tracking plus its Compute Instances roadmap.
Broad incumbents
- TensorBoard: Google's open-source visualization and tracking toolkit bundled with the TensorFlow/PyTorch ecosystems; a much broader incumbent providing lightweight tracking functionality that overlaps with mlop's free tier for individual practitioners.
Emerging players
- DVC (Iterative): Open-source data and experiment versioning tools (DVC, Studio, MLEM) from Iterative, focusing on Git-based ML reproducibility and experiment tracking; overlapping with mlop's reproducibility features and PyTorch developer audience.
- Determined AI: Open-source deep learning training platform with experiment tracking, hyperparameter tuning, and distributed training; overlaps with mlop's experiment tracking plus emerging compute and training management features.
Market position
Strengths5 records
Weaknesses5 records
Competitive moat5 records
Key risks7 records
Key highlights6 records
Customer concentration
mlop social profiles
Digital presencemlop financial estimates
Financial estimateRevenue estimate
Valuation estimate
mlop leadership team
Management profileNumber of profiles
mlop funding detail
Funding detailFunding overview
Funding rounds1 record
Investors1 record
Funding detail is available on the Subscription and Enterprise plan.Contact sales →
mlop M&A and investment
M&A and investmentM&A
Investments
M&A and investment is available on the Subscription and Enterprise plan.Contact sales →
Frequently asked questions about mlop
What does mlop do?
mlop provides an open-source MLOps platform for tracking, optimizing, and collaborating on machine learning experiments. The platform offers real-time monitoring of metrics, parameters, and gradients; multi-media logging for images, audio, and video; model graph visualization; and automated alerting across Email, Slack, and Discord. It is 100% API-compatible with Weights & Biases, enabling easy migration for ML engineers and data science teams via a Python SDK and web dashboard.
Is mlop a public or private company?
mlop is a private company. It is classified as venture growth investor backed and is currently operating.
When was mlop founded?
mlop was founded in 2025. It employs 1 to 10 people.
Where is mlop based?
mlop is headquartered in San Francisco, United States, in the North America region.
How does mlop make money?
Three revenue lines are on record. Subscription - Free Tier is the primary driver. The others are subscription - Pro Plan and subscription - Enterprise Plan.
Who are mlop's main competitors?
Direct peers on record are Weights & Biases, MLflow, Comet.ml, Neptune.ai, ClearML, AimStack and Valohai. TensorBoard is listed as a broad incumbent. Emerging players are DVC (Iterative) and Determined AI.
Does mlop have an API?
Yes. mlop provides a Python SDK with public API endpoints for experiment tracking. The API supports operations including run initialization, metric logging, file storage, alerts, and model graph visualization. API keys are supported with two types: insecure (for development) and secure (hashed, for production). The SDK includes compatibility functions for Weights & Biases migration. Documentation URL: https://docs.mlop.ai/docs/mlop Developer documentation is at docs.mlop.ai/docs/mlop.
What industry is mlop in?
mlop's product category is MLOps Platform / Experiment Tracking Software. Its primary akta.pro industry code is HDAAABAC, Experiment Tracking, Metadata & Model Registry, with a secondary code of HDAAABAK, ML Lifecycle Collaboration & Workspace Management. Its NAICS code is 5132 and its SIC code is 7372.