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34 Listings in MLOps Available
What is Promptfoo? Promptfoo is LLM testing and red teaming software offering an open-source tool for testing prompts and red-teaming LLM applications for security vulnerabilities. Founded in 2024 and based in San Mateo, California, USA, Promptfoo helps developers and security teams hardening LLM apps work more efficiently and achieve better outcomes. Key features of Promptfoo Prompt and model comparison tests Automated red teaming CI/CD integration Security vulnerability scanning Analytics and reporting Integrations with OpenAI, Anthropic, GitHub Actions and more Who uses Promptfoo? Promptfoo is built for developers and security teams hardening LLM apps. It suits teams that want prompt and model comparison tests without spreadsheets and disconnected tools. Why choose Promptfoo? Compared with alternatives like Patronus AI, Promptfoo differentiates on prompt and model comparison tests. Pricing is quote-based and scoped to your usage and team size.
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What is Langfuse? Langfuse is LLM engineering and observability software offering an open-source platform for tracing, evaluating and managing prompts for LLM applications. Founded in 2023 and based in Berlin, Germany, Langfuse helps engineering teams building LLM applications work more efficiently and achieve better outcomes. Key features of Langfuse LLM tracing and debugging Prompt management and versioning Evaluations and datasets Self-hosting and open source Analytics and reporting Integrations with OpenAI, Anthropic, LangChain and more Who uses Langfuse? Langfuse is built for engineering teams building LLM applications. It suits teams that want LLM tracing and debugging without spreadsheets and disconnected tools. Why choose Langfuse? Compared with alternatives like LangSmith, Langfuse differentiates on LLM tracing and debugging. Pricing is quote-based and scoped to your usage and team size.
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What is Ragas? Ragas is RAG evaluation software offering an open-source framework for evaluating RAG pipelines and LLM applications with objective metrics. Founded in 2023 and based in San Francisco, California, USA, Ragas helps AI engineers building RAG work more efficiently and achieve better outcomes. Key features of Ragas RAG metrics Test data generation LLM app evaluation Integrations Analytics and reporting Integrations with OpenAI, Anthropic, LangChain and more Who uses Ragas? Ragas is built for AI engineers building RAG. It suits teams that want RAG metrics without spreadsheets and disconnected tools. Why choose Ragas? Compared with alternatives like DeepEval, Ragas differentiates on RAG metrics. Pricing is quote-based and scoped to your usage and team size.
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Anyscale is the managed platform built by the creators of Ray, the open-source framework for distributed Python and AI workloads. It gives teams a way to scale data processing, model training, hyperparameter tuning, and large-scale inference across clusters without managing the underlying orchestration. Anyscale handles autoscaling, scheduling, and observability so teams can run Ray applications in production reliably. Anyscale is used by companies building large language models, recommendation systems, and batch AI pipelines that outgrow a single machine. Because it is Ray-native, code written locally scales to thousands of cores with minimal changes, and the platform adds enterprise features like governance, cost controls, and managed clusters on top of open-source Ray.
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What is Cradle? Cradle is AI for protein engineering software offering a generative AI platform that helps biologists design and optimize proteins faster. Founded in 2021 and based in Amsterdam, Netherlands, Cradle helps biotech and R&D teams engineering proteins work more efficiently and achieve better outcomes. Key features of Cradle AI protein design Sequence optimization Wet-lab integration R&D acceleration Analytics and reporting Integrations with Salesforce, Slack, APIs and more Who uses Cradle? Cradle is built for biotech and R&D teams engineering proteins. It suits teams that want AI protein design without spreadsheets and disconnected tools. Why choose Cradle? Compared with alternatives like Cradle, Cradle differentiates on AI protein design. Pricing is quote-based and scoped to your usage and team size.
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Baseten is an inference platform that helps teams deploy machine learning and generative AI models into production with low latency and reliable autoscaling. Its Truss packaging framework standardizes how models are containerized, and its infrastructure handles GPU provisioning, scaling, and observability so engineers can focus on the application rather than the serving stack. Baseten is favored by companies shipping AI-powered features that need predictable performance under real traffic. It supports open-source and custom models, offers fast cold starts, and provides monitoring, logging, and canary deploys. For teams that outgrow do-it-yourself inference, Baseten offers production-grade serving without a large platform team.
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What is LiteLLM? LiteLLM is open-source LLM proxy software offering an open-source proxy and SDK that calls 100+ LLM APIs in the OpenAI format with spend tracking. Founded in 2023 and based in San Francisco, California, USA, LiteLLM helps platform teams centralizing LLM access work more efficiently and achieve better outcomes. Key features of LiteLLM OpenAI-compatible proxy 100+ providers Budgets and spend tracking Load balancing Analytics and reporting Integrations with OpenAI, Anthropic, Google Gemini and more Who uses LiteLLM? LiteLLM is built for platform teams centralizing LLM access. It suits teams that want OpenAI-compatible proxy without spreadsheets and disconnected tools. Why choose LiteLLM? Compared with alternatives like Portkey, LiteLLM differentiates on OpenAI-compatible proxy. Pricing is quote-based and scoped to your usage and team size.
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What is Patronus AI? Patronus AI is AI evaluation software offering an automated evaluation platform with research-grade judge models for testing LLM and agent outputs. Founded in 2023 and based in San Francisco, California, USA, Patronus AI helps AI teams that need rigorous model and agent testing work more efficiently and achieve better outcomes. Key features of Patronus AI Judge models for evaluation Hallucination and safety testing Agent trajectory evaluation Benchmarks and test suites Analytics and reporting Integrations with OpenAI, Anthropic, LangChain and more Who uses Patronus AI? Patronus AI is built for AI teams that need rigorous model and agent testing. It suits teams that want judge models for evaluation without spreadsheets and disconnected tools. Why choose Patronus AI? Compared with alternatives like Galileo, Patronus AI differentiates on judge models for evaluation. Pricing is quote-based and scoped to your usage and team size.
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What is Martian? Martian is model routing software offering a model router that sends each prompt to the best LLM for cost and quality. Founded in 2022 and based in San Francisco, California, USA, Martian helps AI product teams work more efficiently and achieve better outcomes. Key features of Martian Dynamic model routing Cost reduction Quality prediction OpenAI-compatible API Analytics and reporting Integrations with OpenAI, Anthropic, Google Gemini and more Who uses Martian? Martian is built for AI product teams. It suits teams that want dynamic model routing without spreadsheets and disconnected tools. Why choose Martian? Compared with alternatives like Not Diamond, Martian differentiates on dynamic model routing. Pricing is quote-based and scoped to your usage and team size.
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What is Portkey? Portkey is AI gateway software offering an AI gateway for routing, caching, guardrails and observability across 250+ LLMs. Founded in 2023 and based in San Francisco, California, USA, Portkey helps teams running LLMs in production work more efficiently and achieve better outcomes. Key features of Portkey Unified API to 250+ models Fallbacks and load balancing Guardrails Cost and usage observability Analytics and reporting Integrations with OpenAI, Anthropic, Google Gemini and more Who uses Portkey? Portkey is built for teams running LLMs in production. It suits teams that want unified API to 250+ models without spreadsheets and disconnected tools. Why choose Portkey? Compared with alternatives like LiteLLM, Portkey differentiates on unified API to 250+ models. Pricing is quote-based and scoped to your usage and team size.
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What is OpenRouter? OpenRouter is LLM marketplace and router software offering a unified API and marketplace to access hundreds of AI models with price and uptime routing. Founded in 2023 and based in New York, New York, USA, OpenRouter helps developers and AI apps work more efficiently and achieve better outcomes. Key features of OpenRouter Single API for 400+ models Provider fallback Usage-based billing Model rankings Analytics and reporting Integrations with OpenAI, Anthropic, Google Gemini and more Who uses OpenRouter? OpenRouter is built for developers and AI apps. It suits teams that want single API for 400+ models without spreadsheets and disconnected tools. Why choose OpenRouter? Compared with alternatives like LiteLLM, OpenRouter differentiates on single API for 400+ models. Pricing is quote-based and scoped to your usage and team size.
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What is Giskard? Giskard is AI testing and red teaming software offering an AI testing platform that detects hallucinations, security and quality issues in LLM agents. Founded in 2021 and based in Paris, France, Giskard helps enterprise AI teams work more efficiently and achieve better outcomes. Key features of Giskard Automated red teaming Hallucination detection Continuous testing Open-source library Analytics and reporting Integrations with OpenAI, Anthropic, LangChain and more Who uses Giskard? Giskard is built for enterprise AI teams. It suits teams that want automated red teaming without spreadsheets and disconnected tools. Why choose Giskard? Compared with alternatives like Patronus AI, Giskard differentiates on automated red teaming. Pricing is quote-based and scoped to your usage and team size.
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MLOps platforms operationalize machine learning, managing the lifecycle from experimentation and training to deployment, monitoring, and governance, so teams ship and maintain models reliably. This guide explains what MLOps software is, how it works, what matters, and how to choose one.
MLOps platforms operationalize machine learning, managing the lifecycle from experimentation and training to deployment, monitoring, and governance, so teams ship and maintain models reliably. This guide explains what MLOps software is, how it works, what matters, and how to choose one.
MLOps (machine learning operations) software brings DevOps-style rigor to ML: tracking experiments, managing data and features, training and versioning models, deploying to production, and monitoring performance, drift, and reliability.
It spans end-to-end ML platforms and specialized tools for pipelines, feature stores, model registries, serving, and monitoring, and increasingly LLMOps capabilities for deploying and observing LLM applications.
The category exists because getting models into production and keeping them reliable is hard. Buyers weigh lifecycle coverage, integration with their stack and cloud, scalability, governance, and whether they need a full platform or best-of-breed tools.
MLOps tools track experiments and data, automate training and evaluation pipelines, version and register models, deploy them as APIs or batch jobs, and monitor performance, drift, and infrastructure, with governance and reproducibility throughout.
Platforms combine experiment tracking, pipelines/orchestration, feature stores, model registries, serving/deployment, and monitoring, integrated with cloud, data, and CI/CD systems.
ML and platform teams build pipelines, deploy and version models, set monitoring and governance, and iterate as data and requirements change, with automation reducing manual ops.
Track runs, parameters, metrics, and artifacts for reproducible experimentation.
Automate training, evaluation, and deployment pipelines reliably and repeatably.
Manage and serve consistent features for training and inference.
Version, stage, and govern models from development to production.
Deploy models as scalable APIs or batch jobs with rollout controls.
Monitor performance, drift, and reliability with audit and governance controls.
Ship and maintain models reliably instead of stalling at the prototype stage.
Tracking and pipelines speed experimentation and deployment cycles.
Versioned data, code, and models make results reproducible and auditable.
Monitoring detects drift and degradation before it harms outcomes.
Registries and controls support compliance and team collaboration.
| Type | Best for | Ideal size | Pros | Limitations |
|---|---|---|---|---|
| End-to-end MLOps platforms | Full lifecycle in one place | Mid-market to enterprise | Unified, integrated | Lock-in; cost |
| Specialized tools | Tracking, features, serving, monitoring | Any | Best-of-breed | Integration effort |
| Cloud-native MLOps | ML on a cloud provider | Any | Tight cloud integration | Cloud lock-in |
| LLMOps tools | Deploy and observe LLM apps | Any | LLM-specific observability | Emerging category |
Technology: Technology ML teams use MLOps platforms to track experiments, build pipelines, deploy and version models, and monitor performance and drift, operationalizing AI reliably and with governance.
Healthcare: Healthcare ML teams use MLOps platforms to track experiments, build pipelines, deploy and version models, and monitor performance and drift, operationalizing AI reliably and with governance.
Financial Services: Financial Services ML teams use MLOps platforms to track experiments, build pipelines, deploy and version models, and monitor performance and drift, operationalizing AI reliably and with governance.
Retail & E-commerce: Retail & E-commerce ML teams use MLOps platforms to track experiments, build pipelines, deploy and version models, and monitor performance and drift, operationalizing AI reliably and with governance.
Education: Education ML teams use MLOps platforms to track experiments, build pipelines, deploy and version models, and monitor performance and drift, operationalizing AI reliably and with governance.
Professional Services: Professional Services ML teams use MLOps platforms to track experiments, build pipelines, deploy and version models, and monitor performance and drift, operationalizing AI reliably and with governance.
Manufacturing: Manufacturing ML teams use MLOps platforms to track experiments, build pipelines, deploy and version models, and monitor performance and drift, operationalizing AI reliably and with governance.
Media: Media ML teams use MLOps platforms to track experiments, build pipelines, deploy and version models, and monitor performance and drift, operationalizing AI reliably and with governance.
Decide whether you need an end-to-end platform or best-of-breed tools, and confirm coverage of your gaps.
Confirm integration with your cloud, data systems, frameworks, and CI/CD.
Verify it scales to your data, training, and inference workloads.
Assess drift/performance monitoring and governance/audit for production reliability and compliance.
If deploying LLM apps, check observability and evaluation for LLMs specifically.
Understand pricing, infrastructure costs, and lock-in trade-offs.
LLMOps is rapidly maturing, adding evaluation, observability, and governance for LLM and agent applications.
MLOps is automating more of the lifecycle, lowering the barrier to reliable production ML.
Monitoring is expanding to cover quality, safety, and cost for generative systems.
Buyers should prioritize lifecycle coverage, stack integration, monitoring and governance, and scalability.
MLOps (machine learning operations) is the practice and tooling for operationalizing machine learning, managing the lifecycle from experimentation and training to deployment, monitoring, and governance, with DevOps-style rigor. MLOps software spans end-to-end platforms and specialized tools for pipelines, feature stores, model registries, serving, and monitoring, and increasingly LLMOps for LLM applications.
Building a model is only part of the work; getting it into production reliably and keeping it accurate is where many projects stall. MLOps provides reproducibility, automated pipelines, deployment, and monitoring for drift and performance, so models ship faster and stay reliable. Without it, ML efforts often remain stuck in prototypes.
End-to-end platforms offer unified, integrated lifecycle management with less integration effort but more lock-in and cost. Best-of-breed tools (tracking, feature store, serving, monitoring) give flexibility and best capabilities but require integration. The right choice depends on your team size, existing stack, and how much you value flexibility versus simplicity.
Model drift is the degradation of a model's accuracy over time as real-world data diverges from training data. MLOps monitoring detects drift and performance decay so teams can retrain or update models before outcomes suffer. Monitoring is a core reason to adopt MLOps, production models need ongoing observation, not just deployment.
LLMOps applies MLOps principles to large language model applications, adding evaluation, observability, prompt and version management, cost tracking, and safety/quality monitoring specific to LLMs and agents. It's an emerging extension of MLOps. If you're deploying LLM apps, look for LLMOps capabilities alongside traditional model lifecycle tooling.
MLOps tools integrate with major clouds, data systems, ML frameworks, and CI/CD pipelines, though depth varies. Cloud-native options offer tight integration with one provider (and lock-in), while platform and open tools aim for portability. Confirm integration with your specific cloud, data, and frameworks before adopting.
Pricing varies: per-seat, usage/compute-based, or platform subscriptions, plus underlying infrastructure costs for training and serving. Open-source tools shift cost to infrastructure and engineering. Estimate your workloads and team size, and factor in compute and lock-in when comparing total cost.
Prioritize coverage of the lifecycle stages you need, integration with your cloud and stack, scalability to your workloads, monitoring and governance, LLMOps support if relevant, and pricing and lock-in trade-offs. Pilot on a real model or pipeline and assess integration and operability before standardizing.
