Top 50 Ml Ops Platform - Series A

This startup offers MLOps solutions that streamline the deployment and management of machine learning models in production environments. By optimizing the workflow from model development to deployment, it minimizes operational bottlenecks and improves the reliability of AI applications.

VESSL AI offers an end-to-end MLOps platform that enables machine learning teams to build, train, and deploy models efficiently across various infrastructures with a single command. The platform addresses the challenges of resource management and deployment speed by providing serverless deployment, real-time monitoring, and automated CI/CD workflows.

The startup offers a Python-centric feature store that enables data teams to efficiently build and manage feature, training, and inference pipelines at scale. This platform enhances the performance and availability of AI and machine learning applications across diverse data sources and environments, facilitating the development of data-driven products for business growth.

The startup offers a cloud-based data processing AI platform that enables the deployment of real-time applications without infrastructure constraints. Its software allows data engineers and architects to efficiently process large data volumes, enhancing outpatient monitoring and real-time bidding while minimizing investment costs.

Simplismart provides a high-performance inference engine that enables rapid deployment and fine-tuning of generative AI models on-premises or across various cloud platforms. This technology reduces model deployment time from months to days, significantly lowering operational costs while enhancing inference speed and scalability.

The startup offers a collaboration software tailored for data-centric AI teams, automating data management, model evaluation, and production monitoring. This platform enhances decision-making by providing an intuitive interface for saving and sharing models, experiments, and datasets, while integrating robust data intelligence tools throughout the machine learning workflow.

Modelbit is an infrastructure-as-code platform that enables machine learning engineers to deploy, manage, and scale ML models in production environments with a single git push command. It addresses the complexities of model deployment, including autoscaling, retraining, and drift detection, by allowing all configurations to be managed directly from the user's git repository.

The startup offers an artificial intelligence platform that enables organizations to extract, prepare, and process data for machine learning workflows, facilitating the transition from experimentation to production. This platform provides a secure and scalable solution to enhance the efficiency and performance of AI projects, addressing the challenges of data management and deployment in AI development.

Rens provides an API‑first model management platform that centralizes version control, containerized deployment, and real‑time monitoring for machine‑learning models. It automates generation of versioned containers, provisions Kubernetes‑native inference services with auto‑scaling and canary rollouts, and streams latency, error and drift metrics to customizable dashboards with alerting. The platform integrates with Kubeflow Pipelines, MLflow, and CI/CD tools while offering role‑based access control and immutable audit logs for governance.

Paragon provides an AI product operating system that integrates data curation, model training, deployment, and API monetization into a single platform. It offers HIPAA‑compliant, audited data pipelines with domain‑vetted labeling, reproducible version‑controlled training, CI/CD‑driven MLOps, drift monitoring, and usage‑based billing to help regulated enterprises launch and scale specialized AI solutions.

The startup offers a cloud-based platform that optimizes AI infrastructure and workloads through the integration of open-source tools and resource estimation. This platform enhances operational efficiency by improving the deployment process of artificial intelligence applications for businesses.

Seldon is a machine learning deployment platform that enables organizations to deploy and manage models at scale, reducing deployment time from months to minutes. By providing production-ready inference servers and advanced experimentation tools, Seldon enhances operational efficiency and reduces infrastructure costs, delivering an average productivity gain of 85%.

TrueFoundry provides a platform that automates the deployment and management of machine learning models on users' own infrastructure, integrating seamlessly with GPUs and TPUs for efficient resource utilization. By simplifying the complexities of model training, inference, and monitoring, it enables data scientists and ML engineers to focus on delivering actionable insights while significantly reducing cloud costs.

This company provides a platform for building and deploying machine learning models. It focuses on streamlining the MLOps lifecycle for data science teams. The service enables efficient model versioning, tracking, and production deployment.

The startup operates a data intelligence platform that utilizes machine learning for automated performance and fault modeling in microservices and serverless computing architectures. This technology enables developers to minimize manual errors and enhance software delivery efficiency.

Eta Compute develops a no-code MLOps toolchain that optimizes machine learning models for low-power edge devices, enhancing their efficiency and accuracy. This technology enables enterprises to effectively monitor resources while minimizing energy consumption and inference time.

The startup offers a machine learning platform that allows businesses to implement AI solutions without the need for data scientists, enabling rapid deployment within days. This technology enhances operational efficiency and decision-making, delivering measurable returns on investment significantly faster than traditional methods.

Opsera provides an agentic DevOps platform that unifies software development, AI model, and data pipelines under a single orchestration layer, integrating existing tools via zero‑code connections. It embeds DevSecOps guardrails and AI agents for automated remediation, while delivering a unified dashboard with real‑time DORA, DevEx, and AI productivity metrics; the solution is available as SaaS, on‑premises, or hybrid for enterprise DevOps, MLOps, and DataOps teams.

CrunchDAO operates a decentralized network protocol connecting organizations with global machine learning talent to solve complex ML challenges. It facilitates secure ML competitions where data scientists submit models for rewards, leveraging collective intelligence to achieve performance gains. The platform offers tools like the Crunch Hub and specialized engines for low-latency predictions and secure ML orchestration.

Outerbounds provides a platform for engineering production-grade AI products by integrating data, models, and agents with software discipline. It enables rapid development and evaluation of AI systems using Metaflow, supporting CI/CD workflows for models and code. The platform securely deploys these systems within the customer's cloud environment, offering access to top-tier GPU providers while optimizing compute costs.

Outerbounds provides a platform for engineering production-grade AI products by integrating data, models, and agents with software discipline. It enables rapid development and evaluation of AI systems using Metaflow, supporting CI/CD workflows for models and code. The platform securely deploys these systems within the customer's cloud environment, offering access to top-tier GPU providers while optimizing compute costs.

Delphina functions as a data agent that builds a living map of an organization's data landscape by integrating warehouse data and analyses. It allows users to query data using natural language within platforms like Slack, automatically planning analysis, writing SQL, and returning results. The platform also offers proactive workflow agents for monitoring metrics and governance tools for oversight and access control.

Union.ai is a Kubernetes-native workflow orchestration platform that streamlines the development, management, and deployment of AI models at scale. It addresses challenges such as disconnected development teams, high cloud costs, and slow time-to-market by providing a unified interface for automating workflows and optimizing resource usage.

RunPod is a cloud platform that provides globally distributed GPU resources for deploying and scaling machine learning applications, enabling developers to run AI workloads without managing infrastructure. The platform reduces cold-start times to under 250 milliseconds and offers flexible pricing, allowing users to efficiently handle fluctuating demand while minimizing operational costs.

Chalk provides a unified data platform designed specifically for building and serving AI and machine learning applications. It offers ultra-fast data pipelines, on-demand compute, and built-in scheduling and caching within the customer's cloud environment. This infrastructure simplifies data engineering workflows, enabling teams to deploy real-time models with low latency and maintain auditability across training and serving.

Data scientists and ML engineers often struggle with fragmented tooling that makes it hard to prototype locally, track experiment artifacts, scale compute to the cloud, and deploy models to production without extensive rewrites or manual orchestration. Metaflow is an open‑source Python framework that unifies the entire ML/AI lifecycle. It lets users author workflows in plain Python notebooks or scripts, automatically version inputs, parameters, and outputs, and run steps locally with instant feedback.

The startup offers an artificial intelligence orchestration platform that simplifies model management, intelligent caching, and cost tracking for businesses. This platform enhances operational efficiency by optimizing performance and reducing costs, enabling seamless scalability.

TensorOpera AI provides a full-stack platform for building, deploying, and orchestrating AI agents from concept to production. The platform supports multi-agent collaboration, dynamic workflow execution, and optimized model serving across cloud and edge environments. It accelerates time-to-market by integrating agent creation, model scaling, and federated learning capabilities.

WhyLabs provides real-time monitoring and management tools for machine learning and generative AI applications, enabling teams to detect and mitigate security risks, model drift, and performance issues. By automating threat remediation and ensuring data privacy, WhyLabs reduces manual operations by over 80% and accelerates incident resolution by 20 times.

Gantry enhances machine learning products by integrating analytics, alerting systems, and human feedback mechanisms to improve model performance and reliability. This approach addresses the challenges of model accuracy and responsiveness in dynamic environments, enabling users to make informed decisions based on real-time insights.

Skymind provides an enterprise machine learning platform built on the open-source deep learning framework Deeplearning4j, specifically designed for Java Virtual Machine (JVM) environments. The platform enables organizations to efficiently develop, deploy, and integrate machine learning models into their existing applications, addressing the challenge of bridging data science with software engineering.

Cake provides an enterprise AI management platform deployed entirely within a customer's VPC for regulated industries. This platform unifies data, models, and orchestration while enforcing governance, security, and cost controls across the AI/ML lifecycle. It enables faster development and scaling of GenAI and Machine Learning applications using open-source components.

OctaiPipe provides a Federated Learning Operations (FL-Ops) framework that enables on-device AI deployment for Edge AIoT devices, minimizing data transfer to the cloud and enhancing privacy and security. This technology addresses the need for real-time decision-making and efficient management of critical infrastructure by ensuring localized intelligence and reducing vulnerability to cyber threats.

Provides an auto-documentation platform for AI/ML models that integrates with existing tools like Python, MLFlow, and cloud platforms to automatically log model lineage, generate compliance-ready documentation, and maintain audit trails. This reduces documentation time by 90%, accelerates model production by 25%, and ensures regulatory adherence through real-time governance and traceability.

Thoras.ai is an AI cloud management platform that optimizes resource allocation and performance monitoring for cloud infrastructure. By utilizing machine learning algorithms, it enhances operational efficiency and reduces costs associated with cloud resource management.

FeatureByte is an AI-based self-service feature platform that streamlines the AI feature lifecycle, enabling data scientists and ML engineers to rapidly create and deploy production-ready pipelines from raw data. By automating the feature engineering process, FeatureByte allows teams to deploy five times more models in hours instead of weeks, maximizing the value of existing data assets without additional infrastructure costs.

Profet AI provides a no-code AutoML platform that enables manufacturing companies to implement AI solutions quickly, allowing domain experts to leverage machine learning without requiring extensive technical knowledge. The platform addresses the challenge of knowledge transfer from experienced workers by facilitating rapid deployment of AI applications across various operational areas, enhancing decision-making and efficiency.

The startup offers an artificial intelligence platform that utilizes a visual editor and a straightforward application programming interface to enable users to create and implement personalized automation within their applications. This technology addresses the complexity of task automation by allowing clients to easily design contextualized workflows tailored to individual user needs.

Neubird provides an AI‑powered Site Reliability Engineering (AI SRE) platform that continuously monitors hybrid cloud environments, automatically detects anomalies, pinpoints root causes, and executes remediation actions to resolve incidents within minutes. The service integrates with existing observability, incident‑management, and communication tools such as DataDog, PagerDuty, Slack, AWS, Azure, and Google Cloud, enabling SRE, DevOps, and on‑call teams to prevent outages, reduce mean‑time‑to‑repair, and optimize capacity. Neubird monetizes through a subscription‑based pricing model offering tiered access to its autonomous production‑ops agent.

Saturn Cloud provides a developer-friendly platform for building, scaling, and deploying AI and machine learning applications across any cloud environment, utilizing Docker containers and JupyterLab for seamless development. It eliminates infrastructure management tasks, allowing data scientists to focus on experimentation and production deployment while ensuring high security and compliance for enterprise data.

The startup offers an automated machine learning platform that utilizes artificial intelligence for industrial data analytics, focusing on predicting material quality and machine failures. This technology enables factories to enhance energy efficiency, reduce production costs, and minimize downtime.

Koyeb provides a serverless platform that enables rapid deployment of web applications, APIs, and machine learning models across multiple cloud environments without the need for infrastructure management. By utilizing high-performance CPUs, GPUs, and automatic scaling, Koyeb significantly reduces deployment time and operational complexity, allowing developers to focus on building applications rather than managing servers.

Thread AI provides Lemma, a composable AI orchestration platform for building and managing AI workflows and agents. This platform allows teams to prototype and productionize AI solutions using low-code interfaces or APIs, significantly reducing infrastructure complexity. Lemma ensures secure adoption through features like data encryption, PII redaction, and unified governance across all operationalized models.

Lyceum simplifies AI model training by automating GPU infrastructure selection and deployment. The platform offers one-click GPU deployment, intelligent hardware matching, and predictive runtime analysis to optimize job scheduling for speed and cost efficiency. This allows AI developers and data scientists to focus on model development without managing complex infrastructure.

TensorOpera offers a unified platform for developing and commercializing generative AI applications. It provides enterprise-grade infrastructure for scalable model training and deployment, along with agent APIs, to streamline the AI lifecycle and accelerate time-to-market.

Oumi offers an AI-native platform that automates the full lifecycle of custom model development, from automated evaluation and data synthesis to fine‑tuning and deployment. Enterprises use the service to create task‑specific 3‑7B models that outperform generic frontier models in accuracy, cost, and latency while retaining full ownership of weights, data, and infrastructure. The business model includes a free open‑source stack, a subscription‑based Pro tier, and custom‑priced enterprise engagements with dedicated support and professional services.

Io.net Cloud offers a decentralized computing network that provides machine learning engineers with instant, permissionless access to global GPU resources for their workloads. This platform enables efficient deployment of pre-configured clusters, significantly reducing costs and deployment time for AI startups.

Featureform develops an open-source virtual feature store that enables data scientists to define, manage, and serve machine learning features across organizations, ensuring a centralized repository with versioning and lineage tracking. This platform addresses the challenges of fragmented data workflows by facilitating collaboration, monitoring, and compliance in machine learning projects.

Litmus provides an Industrial DataOps platform that unifies and standardizes operational technology (OT) data from diverse industrial assets. This enables scalable industrial AI and analytics by collecting, normalizing, and contextualizing data at the edge, accelerating digital transformation and driving data-driven automation.

The startup develops machine learning software that extracts actionable insights from complex data sets to enhance decision-making in organizations. By leveraging domain expertise, the software enables businesses to improve operational efficiency, reduce risks, and increase revenue.