List of CometLLM Customers
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United States
Since 2010, our global team of researchers has been studying CometLLM customers around the world, aggregating massive amounts of data points that form the basis of our forecast assumptions and perhaps the rise and fall of certain vendors and their products on a quarterly basis.
Each quarter our research team identifies companies that have purchased CometLLM for MLOps Platforms from public (Press Releases, Customer References, Testimonials, Case Studies and Success Stories) and proprietary sources, including the customer size, industry, location, implementation status, partner involvement, LOB Key Stakeholders and related IT decision-makers contact details.
Companies using CometLLM for MLOps Platforms include: NatWest, a United Kingdom based Banking and Financial Services organisation with 56600 employees and revenues of $18.01 billion, Pattern Research, a United States based Manufacturing organisation with 10 employees and revenues of $2.0 million, For Good Ai, a United States based Professional Services organisation with 70 employees and revenues of $2.0 million and many others.
Contact us if you need a completed and verified list of companies using CometLLM, including the breakdown by industry (21 Verticals), Geography (Region, Country, State, City), Company Size (Revenue, Employees, Asset) and related IT Decision Makers, Key Stakeholders, business and technology executives responsible for the software purchases.
The CometLLM customer wins are being incorporated in our Enterprise Applications Buyer Insight and Technographics Customer Database which has over 100 data fields that detail company usage of software systems and their digital transformation initiatives. Apps Run The World wants to become your No. 1 technographic data source!
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| Logo | Customer | Industry | Empl. | Revenue | Country | Vendor | Application | Category | When | SI | Insight |
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For Good Ai | Professional Services | 70 | $2M | United States | Comet | CometLLM | MLOps Platforms | 2025 | n/a |
In 2025, Zencoder implemented CometLLM within its MLOps Platforms footprint to observe agent behavior and scale AI coding agents across the United States. The deployment leverages Comet's Opik LLM evaluation and tracing capabilities to instrument agent interactions and accelerate iterative development cycles.
Zencoder configures CometLLM modules for prompt and version control, traces, and prompt libraries, and it is likely using CometLLM for prompt logging and prompt-playground workflows to support prompt engineering and experimentation. Opik tracing within CometLLM captures end-to-end agent traces and prompt histories, producing reproducible artifacts for debugging and research workflows.
The operational scope spans engineering and research teams in the United States, where CometLLM centralizes prompt artifacts and trace archives to support cross-functional debugging and analysis. Integrations are centered on agent instrumentation and trace collection, enabling Zencoder to scale AI coding agents while maintaining observable experiment and prompt provenance.
Governance and process changes include standardized prompt versioning and trace-based review workflows to shorten iteration cycles and improve reproducibility. Zencoder’s use of CometLLM and Opik tracing is credited with accelerating iteration cycles and improving cross-functional debugging and research productivity in the United States.
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NatWest | Banking and Financial Services | 56600 | $18.0B | United Kingdom | Comet | CometLLM | MLOps Platforms | 2025 | n/a |
In 2025, NatWest implemented CometLLM as its MLOps Platforms choice to standardize machine learning experimentation and to track LLM experiments and prompts across UK teams. The deployment focused on centralizing experiment metadata and prompt histories to improve cross team collaboration and governance.
NatWest configured CometLLM to provide experiment tracking, prompt management, evaluation instrumentation, and metadata versioning, aligning with standard MLOps Platforms workflows for model lifecycle management. CometLLM was used to capture prompt inputs and evaluation metrics alongside model artifacts and run metadata, enabling reproducible model development and prompt level traceability. The implementation emphasized run lineage and prompt version control as core capabilities.
Operational coverage included UK data science and ML engineering teams, with integrations at the pipeline and workflow level to surface experiment results and prompt variants into internal training and evaluation processes. Integration descriptions remained generic to reflect pipeline level connectivity rather than named third party products. The configuration enabled audit ready prompt histories and supported AI governance review workflows.
Governance and process changes introduced standardized experiment logging, prompt versioning, and role based access to experiment records to strengthen oversight and reproducibility. The CometLLM deployment improved collaboration, governance, and model reproducibility across NatWest teams.
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Pattern Research | Manufacturing | 10 | $2M | United States | Comet | CometLLM | MLOps Platforms | 2025 | n/a |
In 2025, Pattern Research implemented CometLLM, deploying Comet's tooling in the MLOps Platforms category to optimize e-commerce content generation and to select lower-cost models. The implementation targets the Content Brief workflow in the United States, and Pattern reports an estimated $60,000 annual saving tied to that workflow.
Implementation centers on Comet's Opik LLM-evaluation and the prompt-playground for systematic prompt testing and comparative model evaluation. Because Pattern explicitly uses Opik's prompt library and evaluation features, they also use CometLLM for prompt logging and versioning, enabling reproducible prompt engineering, artifact capture, and iteration tracking.
Configuration focuses on prompt engineering workflows, model selection gates based on evaluation metrics, and prompt version control, all consistent with MLOps Platforms capabilities. For a 10 person manufacturing company, the deployment was scoped to the content and e-commerce teams, providing a centralized prompt repository and evaluation pipeline without enterprise-scale orchestration.
Operational governance emphasizes prompt provenance and evaluation driven selection of lower-cost models, with rollout occurring through iterative prompt experiments within the Content Brief workflow. CometLLM and Opik together provide structured logging, evaluation artifacts, and model choice control to support ongoing content generation and cost management.
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Buyer Intent: Companies Evaluating CometLLM
Discover Software Buyers actively Evaluating Enterprise Applications
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