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Average price: 53 products listed
53 Listings in MLOps Available
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Price range
$0–$199/mo
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46 tools
New this quarter
39 added
What is Simplismart? Simplismart is a platform for deploying and scaling AI models with optimized inference. It emphasizes tailored configurations instead of one-size-fits-all APIs and supports LLMs, vision language models, diffusion and speech models. Key capabilities of Simplismart Model library: 150+ open-source models plus custom imports from 10+ repositories. Optimized inference: Custom CUDA kernels, KV caching, tensor parallelism and quantization. Auto-scaling: Vendor states scaling in under 500 ms and scale-to-zero. Flexible deployment: Simplismart Cloud, private VPC or on-premises. GPU choice: H100, H200, A100, A10G, L40S and B200 GPUs. Monitoring: Real-time dashboards. Unified control plane: Manages multiple cloud environments. How Simplismart works You pick a model from the library or import your own, choose a deployment target and hardware, and Simplismart serves it with optimizations tuned to that model. Traffic scales automatically, including to zero, and a control plane spans clouds with native integrations for 15+ cloud providers. Who uses Simplismart? AI engineering teams running production generative AI. The vendor lists Tata 1mg, Telus, Sanas, Mindtickle, Swiggy, InVideo, Physics Wallah, Lyric and HeyGen. It states ISO 27001, SOC 2 Type 2, GDPR, HIPAA and CPRA compliance. Simplismart pricing Simplismart Cloud is pay-as-you-go, but the homepage does not publish rates. Private VPC and on-premises deployments are quoted. Simplismart alternatives Hugging Face hosts models and endpoints, while ZenML and Hopsworks cover pipelines and feature stores. Simplismart focuses on inference speed and cost.
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What is Dataiku? Dataiku is an enterprise platform for building, deploying and governing data science, machine learning and AI agent projects. It lets business experts and engineers work in one environment under IT-defined guardrails. Key capabilities of Dataiku Data science and ML: Turn raw data into production models. AI agent delivery: Orchestrate models, agents, rules and human review. Agent management: Track cost, performance and risk per agent. Governance: Mandatory testing and sign-off in the pipeline. Guardrailed self-service: Business experts build within IT limits. How Dataiku works Teams build models and agents in a shared environment, combine them with rules and human review into governed workflows, and run them on existing clouds and data platforms. Governance steps require testing and sign-off before approval, and an agent management view tracks cost, performance and risk. Who uses Dataiku? Enterprises that want business experts and data engineers to build governed AI together use Dataiku. Dataiku pricing Dataiku does not publish prices on its homepage. It offers a Start trial option and a Request a demo option, and pricing is by quote. Dataiku alternatives ClearML is an open-source MLOps platform, Domino Data Lab targets enterprise data science operations, and Valohai focuses on ML pipelines. Dataiku is distinguished by pairing agent governance with business-user access.
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What is AgentOps? AgentOps is a developer platform for tracing, debugging and monitoring AI agents and LLM applications. It records LLM calls, tool calls and multi-agent interactions, and installs with pip install agentops. Key capabilities of AgentOps Visualize and debug: track LLM calls, tools and multi-agent interactions Time travel debugging: replay agent runs Agent-agnostic SDK: supports 400+ LLMs and frameworks including OpenAI, CrewAI and AutoGen Cost tracking: token counts and spend across models Production controls: audit trails, error tracking and prompt injection detection Session export: on Pro and above How AgentOps works You add the SDK to your agent code, and AgentOps records each event, meaning an LLM call, tool call or action. The dashboard visualizes sessions and lets you replay runs. Plans are metered by events per month. Who uses AgentOps? Developers building agents with frameworks such as CrewAI and AutoGen who need traces and cost visibility. The vendor cites more than 4,000 GitHub stars and users including Microsoft, Google, Samsung and Meta. AgentOps pricing Basic is free up to 5,000 events per month. Pro starts at $40 per month with unlimited events, log retention, session export and dedicated support. Enterprise is custom with SLA, custom SSO, on-premise deployment and SOC-2 and HIPAA compliance. AgentOps alternatives Zep and Mastra are agent memory and framework tools, while DataRobot, Dataiku and Domino Data Lab are broader enterprise ML platforms. AgentOps focuses on agent tracing.
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What is ZenML? ZenML is a ML pipeline orchestration AI agent offering an open-source framework to build portable ML and LLM pipelines across any infrastructure. Founded in 2021 and based in Munich, Germany, ZenML helps ML engineering teams automate ML pipeline orchestration work and get results faster. Key capabilities of ZenML Portable pipelines Stack abstraction Artifact tracking Model control plane Open-source core Cloud and on-prem deployment How ZenML works ZenML takes code and data as input and produces pipelines. It combines large language models with task-specific AI, with the vendor managing prompts, models and updates. It connects to tools such as Python, Kubernetes, AWS SageMaker and Databricks, so the agent works inside existing workflows. Who uses ZenML? ZenML is built for ML engineering teams. It suits teams that want portable pipelines and stack abstraction without adding headcount, while keeping people in control of review and final decisions. ZenML vs Kubeflow ZenML is often compared with Kubeflow. ZenML stands out for portable pipelines and artifact tracking. The right choice depends on your workflow, integrations and budget, so compare both on a real task.
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What is Graphlit? Graphlit is a RAG as a service AI agent offering a platform that ingests unstructured content and powers RAG, knowledge graphs and AI agents. Graphlit helps developers building AI apps automate RAG as a service work and get results faster. Key capabilities of Graphlit Content ingestion connectors Knowledge graph extraction RAG pipelines MCP tools Developer SDKs Open-source components How Graphlit works Graphlit takes documents, audio and web pages as input and produces context and text. It is powered by Any LLM (selectable) models, with the vendor managing prompts, models and updates. It connects to tools such as Python, TypeScript, OpenAI and Anthropic, so the agent works inside existing workflows. Who uses Graphlit? Graphlit is built for developers building AI apps. It suits teams that want content ingestion connectors and knowledge graph extraction without adding headcount, while keeping people in control of review and final decisions. Graphlit vs Ragie Graphlit is often compared with Ragie. Graphlit stands out for content ingestion connectors and RAG pipelines. The right choice depends on your workflow, integrations and budget, so compare both on a real task.
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What is Openlayer? Openlayer is an enterprise AI governance platform for discovering, testing, monitoring and controlling AI systems across a company, whether built internally, purchased or embedded in vendor tools. It covers seven layers from discovery to cost controls. Customers include eBay, Comcast, DIRECTV and Sun Life. Key capabilities of Openlayer Discovery: inventories AI systems and detects shadow AI Registration: central registry with risk assessment Testing: 175+ built-in tests with CI/CD integration Observability: monitors quality, cost and latency in production Guardrails: blocks prompt injection, jailbreaks and PII leakage Compliance: maps to EU AI Act, NIST AI RMF and ISO 42001 Cost controls: budget caps by project and team How Openlayer works Openlayer connects via SDKs, CLI, REST API, Git, OpenTelemetry and Snowflake, inventories AI systems, runs tests before deployment and monitors live traffic. Guardrails act in real time and compliance evidence is generated for audits. Who uses Openlayer? AI, risk and compliance teams in financial services, healthcare, telecom and insurance use Openlayer. The vendor reports 6x faster development-to-deployment cycles and 90% less audit preparation effort. Openlayer pricing Openlayer does not publish pricing on its homepage. Contact the vendor for a quote. Openlayer alternatives Openlayer is compared with Traceloop, Laminar and Weights & Biases. Traceloop and Laminar focus on LLM observability, and Weights & Biases on experiment tracking.
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What is DataRobot? DataRobot is an enterprise platform for building, operating and governing AI agents at scale. It covers agentic AI, generative AI and predictive AI, with governance and observability tools, and offers open-source tools Covalent and syftr. Key capabilities of DataRobot Agentic AI platform: builds enterprise agents from customizable blueprints Generative and predictive AI: covers both model types in one suite AI governance: tracks compliance, access controls and automated auditing Observability: monitors agent behavior and quality in real time Compute orchestration: allocates compute dynamically for agents Model registry and AutoML: retained from its predictive roots per the category listing How DataRobot works DataRobot organizes work into Build, Operate and Govern phases. Teams start from blueprints and integrations to create agents, run them with dynamic compute orchestration and quality monitoring, and apply governance through compliance tracking, access controls and automated auditing. It runs on-premises, hybrid or across clouds. Who uses DataRobot? Large enterprises that need agents in production with governance use it. The vendor says it is co-engineered with NVIDIA, is an exclusive partner for SAP's ecosystem, and has been named a Leader three times in Gartner's Magic Quadrant for Data Science and Machine Learning Platforms. DataRobot pricing DataRobot does not publish prices on its homepage. Enterprise licensing is quote based, and trial access and demos are offered through the website. Contact sales for pricing tied to deployment model and usage. DataRobot alternatives Alternatives include Dataiku for collaborative data science, Domino Data Lab for model operations, BentoML for open-source model serving, Seldon for model deployment and Valohai for ML pipelines.
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What is Agentuity? Agentuity is an agent cloud AI agent offering a cloud platform built for deploying, running and observing AI agents across frameworks. Agentuity helps AI agent developers automate agent cloud work and get results faster. Key capabilities of Agentuity Agent deployment Framework-agnostic runtime Observability Agent-to-agent routing Multi-agent orchestration Tool and memory support How Agentuity works Agentuity takes code as input and produces agents and APIs. It combines large language models with task-specific AI, with the vendor managing prompts, models and updates. It connects to tools such as OpenAI, Anthropic, Python and TypeScript, so the agent works inside existing workflows. Who uses Agentuity? Agentuity is built for AI agent developers. It suits teams that want agent deployment and framework-agnostic runtime without adding headcount, while keeping people in control of review and final decisions. Agentuity vs Modal Agentuity is often compared with Modal. Agentuity stands out for agent deployment and observability. The right choice depends on your workflow, integrations and budget, so compare both on a real task.
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What is Future AGI? Future AGI is an AI evaluation AI agent offering an evaluation and optimization platform for LLMs and agents with automated metrics. Future AGI helps AI teams automate AI evaluation work and get results faster. Key capabilities of Future AGI Automated evals Synthetic data Prompt optimization Observability Tracing and evals Dataset management How Future AGI works Future AGI takes prompts and traces as input and produces evaluations. It combines large language models with task-specific AI, with the vendor managing prompts, models and updates. It connects to tools such as OpenAI, Anthropic, LangChain and Python, so the agent works inside existing workflows. Who uses Future AGI? Future AGI is built for AI teams. It suits teams that want automated evals and synthetic data without adding headcount, while keeping people in control of review and final decisions. Future AGI vs Galileo Future AGI is often compared with Galileo. Future AGI stands out for automated evals and prompt optimization. The right choice depends on your workflow, integrations and budget, so compare both on a real task.
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What is Requesty? Requesty is a LLM gateway AI agent offering an LLM router and gateway that picks models, caches and tracks cost across providers. Requesty helps AI developers automate LLM gateway work and get results faster. Key capabilities of Requesty Smart model routing Caching and fallbacks Cost analytics OpenAI-compatible API Tracing and evals Dataset management How Requesty works Requesty takes text as input and produces text and insights. It is powered by Multiple providers (selectable) models, with the vendor managing prompts, models and updates. It connects to tools such as OpenAI, Anthropic, LangChain and Python, so the agent works inside existing workflows. Who uses Requesty? Requesty is built for AI developers. It suits teams that want smart model routing and caching and fallbacks without adding headcount, while keeping people in control of review and final decisions. Requesty vs OpenRouter Requesty is often compared with OpenRouter. Requesty stands out for smart model routing and cost analytics. The right choice depends on your workflow, integrations and budget, so compare both on a real task.
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What is Google AI Studio? Google AI Studio is a Gemini developer playground AI agent offering Google web tool for prototyping with Gemini models, getting API keys and building AI apps. Founded in 2023 and based in Mountain View, California, USA, Google AI Studio helps developers building with Gemini automate Gemini developer playground work and get results faster. Key capabilities of Google AI Studio Prompt prototyping Gemini API keys Build mode apps Multimodal testing Repository context Works with many models How Google AI Studio works Google AI Studio takes text, image, audio and video as input and produces text and code. It is powered by Google Gemini models, with the vendor managing prompts, models and updates. It connects to tools such as GitHub, VS Code, JetBrains and Git, so the agent works inside existing workflows. Who uses Google AI Studio? Google AI Studio is built for developers building with Gemini. It suits teams that want prompt prototyping and Gemini API keys without adding headcount, while keeping people in control of review and final decisions. Google AI Studio vs OpenAI Playground Google AI Studio is often compared with OpenAI Playground. Google AI Studio stands out for prompt prototyping and build mode apps. The right choice depends on your workflow, integrations and budget, so compare both on a real task.
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What is Latitude? Latitude is a prompt engineering platform AI agent offering an open-source platform for designing, evaluating and refining prompts and AI agents. Founded in 2023 and based in Barcelona, Spain, Latitude helps AI product teams automate prompt engineering platform work and get results faster. Key capabilities of Latitude Prompt editor Evaluations Agent design Open-source Tracing and evals Dataset management How Latitude works Latitude takes prompts as input and produces evaluations and insights. It is powered by Any LLM (selectable) models, with the vendor managing prompts, models and updates. It connects to tools such as OpenAI, Anthropic, LangChain and Python, so the agent works inside existing workflows. Who uses Latitude? Latitude is built for AI product teams. It suits teams that want prompt editor and evaluations without adding headcount, while keeping people in control of review and final decisions. Latitude vs PromptLayer Latitude is often compared with PromptLayer. Latitude stands out for prompt editor and agent design. The right choice depends on your workflow, integrations and budget, so compare both on a real task.
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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.
Tech stacks
See where mlops fits in a complete stack, with the other software, AI agents and services each business needs.
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.
