CComet.ml provides a platform for managing machine learning experiments and lifecycle from data to deployment. It tracks experiments, artifacts, metrics, and models with a Model Registry for keeping track of model versions ready for deployment. The platform integrates Experiment Management with full lineage, letting users see the history of data, training, and deployment, and monitors model performance over time to detect drift or data quality issues. The product targets data scientists, ML engineers, and organizations building and deploying ML models. The pricing is likely subscription or usage-based, charging for storage and features used. The main competitors are other ML lifecycle platforms and experiment trackers. Comet.ml aims to help teams manage ML experiments, track data and models, and maintain a clear history and performance view to improve deployment outcomes.
Work here?Claim your company
Industries
Company Size
51-200
Company Stage
Series B
Total Funding
$74.8M
Headquarters
New York City, United States
Founded
2017
See people who can refer or advise you
Help us improve and share your feedback! Did you find this helpful?
Total Funding
$74.8M
Above
Industry Average
Funded Over
5 Rounds
Industry standards
Introducing Opik's generative dashboard builder. October 26, 2026 At Comet, Comet has seen firsthand how teams in finance, tech, e-commerce, and beyond use the Opik platform to track critical metrics for monitoring and improving their agents. Those metrics vary widely - for instance, a financial services team checking that its internal support agent follows company policy will track very different metrics than an automotive company making sure its customer-facing chatbot quotes car specs correctly. Custom dashboards are a great way to surface the right metrics, but building them takes real effort. You have to understand your data schema, understand the underlying queries, and figure out how best to visualize the results. That's why Comet is thrilled to introduce Opik's new generative dashboards feature. Instead of sifting through your trace metadata, writing queries, or designing charts yourself, just describe what you want to see in plain language and its agent, Ollie, will build a custom widget for you. Want to refine it? Keep chatting with Ollie until it's perfect, then get right back to improving your agents in production. In this post, Comet'll walk through how the generative dashboard works within the Opik UI, what's happening behind the scenes when a new dashboard is requested, and dive into a real-world scenario to show it off in action. How do Opik's generative dashboards work? Generative dashboards work exclusively through Ollie, Opik's conversational AI assistant. Simply ask Ollie what you'd like to see, and you're presented with a few dashboard widget views within the Ollie UI. From there, you can easily include any widget into a new or existing dashboard view by selecting the "Add to Dashboard" button: Behind the scenes, Ollie takes a few steps to build each visualization. First, it translates your written request into a read-only SQL query that pulls the relevant trace, span, thread, experiment, and feedback score data from your ClickHouse instance. Next, it analyzes the result and generates multiple distinct Vega-Lite chart specs suited to your use case to choose between: Once live, it becomes a regular widget you can move, resize and share. To change it later, you can easily click the "Edit with Ollie" button and pick up the conversation where you left off. Generative dashboards in action. Imagine an automotive company that runs an online car-buying platform. Customers can chat with an AI assistant to browse available inventory and ask about each vehicle's specs, from horsepower and fuel economy to trim-level features. Hallucinations here have massive implications - if the assistant gives a customer wrong information, it can cost the company sales and damage its reputation as a whole. To catch these errors, the developer team scores every conversation with a custom LLM-as-a-Judge metric that flags hallucinated vehicle details. They ship new versions of their agent regularly, so they want to know whether each release is improving accuracy or quietly making it worse. With Opik, they can spin up a new dashboard to show them this in a matter of minutes. From a new dashboard in Opik, the team opens Ollie and types their request in plain language: "Show me the average hallucination score for the car specs judge, broken down by agent version, over the last 30 days." Ollie generates a read-only SQL query that pulls the relevant traces and feedback scores, groups them by the agent version stored in the trace metadata, and returns a chart comparing hallucination scores across releases: The first chart shows that newer agent versions hallucinate less, but the team wants to see trendlines within each agent over time as well. They ask Ollie a follow-up: "Make each agent version a line chart not a bar so I can see how each version trends over time..." Ollie updates the widget in place, making it easy to pinpoint when each release begins producing more errors: Once the widget looks right, the team adds it to their dashboard, where it updates as new traces come in: After a few minutes of work, the team can see not just which release is underperforming, but exactly when its accuracy started to slip. That timing helps them trace the problem to its cause (i.e., a prompt change, an inventory update, or a shift in the questions customers are asking) and then dig into the traces from that window to see exactly what the assistant got wrong. Now, every time they ship a new agent version, they can catch errors early and fix or roll back the release before more customers receive incorrect car details. Get started with Opik. Every team building agents has their own definition of success, and the metrics that capture it shouldn't require intense query writing and chart design to surface. With Opik's generative dashboards, you can go from question to custom visualization in a single conversation in just a few minutes, and focus on the value they provide to improve agentic performance. Generative dashboards are generally available now in Opik. To try them, sign up for a free Opik account or log in to your existing workspace, open a dashboard, and ask Ollie what you'd like to see. For more details, check out its documentation, and be sure to share what you build with Comet in its public Slack channel! Parthiv Mathur
Comet unveils cost intelligence tool for AI code spend visibility. Dave Author Engineering teams moving fast with Claude Code and Codex hit a hard wall: real spend visibility. If AI tools are now core infrastructure, not side projects, leaders need answers when budgets spike - fast. Comet's new Opik cost intelligence marks a real shift: granular, real-time, per-engineer tracking that surfaces the actual cost of every coding agent event before the invoice drops. Cost reviews after the fact are obsolete. Comet Opik now delivers cost intelligence for Claude Code and Codex - the first tool exposing exactly where your AI coding budget is going, at the level that matters: per engineer, per team, per task. Positioned as an AI observability and evaluation platform, Opik was already monitoring AI model performance and usage. With the introduction of coding agent cost tracking, it closes the gap for engineering leaders responsible for real budgets: not just model output, but the bottom line. The friction is industry-wide. Engineering tool spend has shifted from the visible (SaaS licenses, infrastructure) to the opaque (AI token consumption billed per-use across sprawling code bases). Until now, leaders couldn't answer even basic questions: * What does each engineer cost Otf-Kit in Claude Code and Codex API usage this sprint? * Are Otf-Kit burning expensive tokens maintaining idle or redundant managed code plugins (MCPs) and skills? * Which teams or feature tracks are driving the majority of spend - and does it align with value shipped? Without cost tracking for AI coding agents, strategic budget conversations defer to vendor invoices - long after optimization is possible. Opik's release turns the black box transparent. Takeaway: Having "AI observability" is table stakes; cost tracking closes the expert visibility gap and arms engineering leaders with actionable data. Opik's core win is granularity: it tracks AI coding spend not just as a lump sum, but in real time, broken down by engineer, by team, and by task. As soon as an engineer invokes a Claude Code or Codex-powered workflow - whether generating code, debugging, auto-fixing a PR, or running automations - Opik attributes tokens, cost, and context usage instantly. Each coding agent event through Claude Code or Codex APIs is measured for: Realtime data allows engineering leads to see - before any invoice arrives - how spend is tracking against budgets at every level of the org chart. This supports everything from high-level budget planning to tactical debugging of runaway team spend after a workflow misconfiguration. The visibility is live - not a lagging report. Integration is API-driven: Opik connects to your Claude Code and Codex usage via native integration, meaning no complex manual reconciliation or batch uploads. Instead, cost intelligence flows continuously, surfaced in dashboards and exportable for finance reviews. The depth of reporting also enables accountability and forecasting for specific projects or features, not just generic usage buckets. Takeaway: Comet Opik moves cost understanding from after-the-fact invoice audits to live, per-action attribution - compressing the feedback loop for better budget control. 11 production screens. Auth, DB, Stripe - all wired. The SaaS Dashboard Kit ships everything already connected. No Vercel config, no Supabase account. Live demo at saas.otf-kit.dev. Opik doesn't stop at reporting. Its real advantage: it automatically eliminates waste in Claude Code and Codex usage without blocking developers or forcing them to ration access. The optimization happens behind the scenes, so teams keep moving fast. Key strategies include: * Eliminating unused skills and idle MCPs: Opik continuously inspects which managed plugins and code skills are loaded but never invoked. These idle attachments can quietly burn tokens every invocation, especially across large orgs where context configs drift. Opik auto-drops unused components from the active context - zero friction for devs. * Fixing misconfigured compaction strategies: AI context size and token compaction settings are a common hidden source of runaway cost. Opik detects when models are running with suboptimal context windows or are holding onto expensive history longer than needed. It applies best-practice compaction, reducing per-call costs. * Exposing and optimizing by model and workflow: Many teams default to higher-cost models (or oversized MCPs) for tasks that don't require them. Opik surfaces where default model selection can be optimized per workflow - with recommendations and (optionally) automatic downgrades when appropriate. These aren't manual review steps - Opik executes them as part of the monitoring pipeline. The result is verified in the source article: one enterprise customer was able to reduce their AI coding spend by "millions annually" after deploying Opik, without restricting developer AI access or slowing output. Manual monthly cost reviews typically come too late, and often can't correlate spend to source. Opik's automated optimizations run with every workflow. Takeaway: Opik delivers continuous, automated cost cutting - not after-the-fact reminders - while keeping developer experience frictionless. Getting started with Opik in a real engineering org is direct. Leaders integrate their Claude Code and Codex API credentials with Opik via the platform's onboarding flow. Once connected, all usage - across automated workflows, direct code generation, agent-triggered debugging, and tool invocations - is tracked from day one. Practical steps: * Integrate with existing workflows: Configure Opik using your AI agent API keys and map users and teams to Opik identifiers. * Access real-time dashboards: Review dashboards segmented by engineer, team, and project. Cost and token burn by feature, MCP, or workflow is visible instantly. * Identify inefficiencies: Use Opik's cost intelligence reports to spot unused skills, idle MCPs, or model-selection anomalies. * Apply (or automate) recommended optimizations: Enable Opik's auto-optimization engine, or review recommendations for model, context, and plugin configuration. Confirm changes directly in the Opik UI, or allow automated enforcement for non-critical settings. * Align spend with development priorities: Reference cost per feature delivered or bug fixed. The data shows which investments return value (and which do not). Every org will surface surprises. Teams often discover a long-misconfigured agent, an MCP loaded for months but never used, or unexpected spikes tied to non-critical workflows. Opik makes these obvious - and fixable. Finance and dev leads are no longer trading off shipping features against a vague sense of growing AI bills. They can tie spend, outcome, and ownership together. Takeaway: With Opik, engineering leaders can see, act on, and control AI coding spending as part of their daily workflow - not in the rearview after fiscal closes. AI adoption is only accelerating. Toolchains stack Claude Code, Codex, plugins, wrappers, and custom MCPS into fast-evolving, interconnected pipelines. Without integrated, real-time cost intelligence like Opik, complexity swamps budget control. Opaque token burns and invisible misconfigurations threaten to turn AI cost into an enterprise risk - not just a line item. Future iterations of platforms like Opik will be forced to go deeper: linking AI cost not only to code generation, but to shipped product value, regression frequency, model performance, and even customer-facing feature usage. AI observability will be synonymous with cost alignment - controlling not just model usage, but business outcome. As Comet's leadership stated in their announcement, engineering spend on AI tooling is rapidly becoming core infrastructure - and the winners will be those who see, debug, and optimize it as such. Takeaway: Integrated, actionable cost intelligence is moving from nice-to-have to core requirement as AI agent complexity and spend grow. Claude Code and Codex have redefined what's possible for engineering velocity - and budget complexity. Comet Opik cost intelligence for Claude Code and Codex is the first move toward true spend visibility: real-time, granular, actionable. It lets engineering leaders to scale AI-led development confidently, controlling budgets without throttling innovation or developer experience. If AI tools are now as central as your code repo, then Opik is the cost intelligence layer every serious team will need next.
Comet has launched Cost Intelligence in Opik, the first tool providing engineering leaders with complete visibility into Claude Code and Codex spending. As enterprise AI coding expenditure accelerates into the billions, most engineering teams cannot track spending per engineer, team or task until invoices arrive. Opik enables real-time monitoring of AI coding tool costs and automatically reduces spending by eliminating unused skills, removing idle MCPs from context and fixing misconfigured compaction strategies. One enterprise customer reduced AI spending by millions annually using the platform. The tool allows teams to attribute spending to specific work, understand which tasks justify AI costs, and identify wasteful token usage across MCPs, skills and plugins. Engineering leaders can now see exactly how developers configure their AI tools and standardise settings before costs compound.
5 Gemini sidebar tricks that made Chrome feel like a new browser. I didn't expect much from Gemini in Chrome until it became part of everything I do online. 7 hours ago The Chrome browser started to feel like it's stuck in the past as new-age rivals like Arc, Dia, and Comet kept playing with experimental features. I have loved being with Arc for that exact reason - it felt fresh while Chrome stayed unchanged. However, Google didn't take long to flip the situation in its favor by shipping the biggest upgrade its flagship browser has seen in a decade. Gemini living in the sidebar has proven to be a game-changer for my workflows in ways I hadn't imagined. It has completely changed how I use Chrome entirely. There is far less tab switching, and a lot more can be done from one place. These are the Gemini sidebar tricks in Chrome that I swear by now. Handholding in multi-step workflows. At least a couple of times a week, I find myself setting up new apps, web tools, or accounts - and trying something new like that requires a bit of handholding. Without AI integration, I would constantly screenshot the window or copy entire pages and paste them into ChatGPT over and over to get answers. With the Gemini sidebar, things have been far more straightforward. What would've otherwise taken me a couple of days of back and forth, or even a call with an expert, got done in a single evening with Gemini. The last time I used it was when I was setting up a LinkedIn ad for the first time. I needed consistent supervision to make sure I didn't click the wrong button or pick an option I shouldn't have, sabotaging the entire campaign. Gemini guided me through the entire process and answered all the questions I had along the way. This would have otherwise taken me a couple of days of back and forth, or even a call with an ad expert. Instead, I got it done in a single evening. It's quite an understated perk that Gemini can see what you are seeing on the screen, so both of you can make decisions together. Latest deals on top tech. Multi-tab research. Rita El Khoury / Android Authority Screenshot Purchase decisions don't come easy, especially when you have tons of options to choose from. You usually end up more confused. I recently needed to upgrade my laptop, so I opened the M5 MacBook Air and the M5 MacBook Pro in different tabs. I simply asked Gemini to use those tabs to suggest what would work better for someone who spends 80% of the time writing and occasionally does mildly heavy tasks like website creation in Wix and some light video editing. I didn't have to tell it the device names or configurations; it already had the context from the tabs. I got my questions answered instantly, got user opinions about the upgrade from an M1 Air from Reedit, and had the verdict within minutes, not hours. Gemini becomes even more useful when you're dealing with a dozen tabs, say, when planning a trip. Those were just two tabs. Gemini becomes even more useful when you're dealing with a dozen tabs - say, when planning a trip while simultaneously digging into Google reviews for the downsides of each place. It scans everything and gives you what actually matters in minutes. Generating images on the fly. Megan Ellis / Android Authority Androidauthority has all seen how good image generation has gotten, but having Nano Banana baked directly into Gemini's sidebar hits differently. I didn't even realize it was there at first - I discovered it accidentally. I had an image of an old-school themed room open in Google Images and asked Gemini to reimagine it in a modern style. It instantly generated a version of that same room with the kind of interiors I would actually like. Not going to lie, it put a gratifying smile on my face seeing how accessible things like these had gotten. You don't need to leave the page or copy-paste anything. It's right there, working with what you're already looking at. I've even used Gemini to generate initial thumbnail ideas while browsing similar YT videos. And I still feel like I'm barely scratching the surface here. That experience got me using it more. I've opened multiple references for desk setups and asked Gemini to bring them together into a minimal home office. Beyond interiors, I've even used it to generate initial thumbnail ideas based on similar videos I was browsing. And I still feel like I'm barely scratching the surface here - the possibilities are pretty much endless. Decoding long Reddit threads. Ryan Haines / Android Authority There's a reason Reddit threads are called rabbit holes. Subtopics keep branching out under every comment, and it becomes almost impossible to track everything without losing sight of why you opened the thread in the first place. If you're troubleshooting something, it's easy to get lost and struggle to find the actual solution buried under hundreds of comments. Gemini has saved me hours by surfacing the relevant responses without requiring me to scroll endlessly. I can also cross-check whether a suggestion is safe or not within the same tab without jumping across tabs. The same applies to long PDFs and contracts. I've used it to dig through jargon-heavy terms and exclusions - especially for things like credit cards - and spot anything that might work against me before signing up. Calibrating mutual fund portfolios. Dhruv Bhutani / Android Authority This one has to be my favorite. I had a fairly limited understanding of the stock market, and it took a lot of effort to learn the basics before I started investing a few years ago. Most of that learning came from trying to decode complex jargon on financial websites filled with charts, graphs, and data that didn't always make sense. I'd often jump to YouTube just to understand simple terms. If I had a mentor, I could've just asked questions and gotten clarity instantly. I was due for an annual recalibration of my mutual fund portfolio, and I asked Gemini to help. With so many data points built over the years, it would have been exhausting to explain everything manually to an AI tool. But with access to my screen, I asked it to look at my portfolio and give its assessment. I also explained the reasoning behind my choices - short-term goals, long-term plans, retirement - and Androidauthority worked through it together. Androidauthority dropped a few funds, reallocated some assets, and left the rest for the next year. I didn't blindly follow all its suggestions, though. I questioned everything and found a middle ground that made sense for my needs. I didn't blindly follow all its suggestions, though. I questioned everything and found a middle ground that made sense for my needs. I didn't rely entirely on AI for my financial decisions, but I can't deny how much it helped. What used to take entire weekends of research was done in about three hours. Don't want to miss the best from Android Authority? * Set Androidauthority as a favorite source in Google Discover to never miss its latest exclusive reports, expert analysis, and much more. * You can also set Androidauthority as a preferred source in Google Search by clicking the button below. I'm genuinely impressed by what the Gemini sidebar can do inside Chrome. Google is already moving toward agent-based workflows with Gemini, and while those features are still limited to users in the U.S., even the current experience is incredibly useful. For everyday tasks, I've always wanted a system that could see what I see and work with me in real time, instead of forcing me to switch tabs, copy text, and share screenshots. This feels exactly what I needed. The only thing I still miss is native voice input - not Gemini Live - but the ability to just speak out my queries while working on something else. I've found a workaround with Wispr Flow, but this is something Google will likely improve. With agent workflows, it's not hard to imagine a future where I just tell Gemini what I need, and it sets up something like a Google Ads campaign end-to-end without my intervention. It's wild that this doesn't even feel like a distant idea anymore.
CrowdStrike and Perplexity partner to deliver enhanced security for Comet Enterprise. Strategic collaboration will bring the CrowdStrike Falcon platform to the Comet Enterprise AI Browser, providing real-time detection, governance, and data protection directly into AI-driven workflows AUSTIN, Texas - March 11, 2026 - Today at Ask 2026, CrowdStrike (NASDAQ: CRWD) and Perplexity announced a strategic partnership to integrate the CrowdStrike Falcon(R) platform with Comet Enterprise, giving enterprise administrators an additional layer of security and monitoring in Perplexity's AI-native browser. The Falcon platform will be available by opt-in, providing enhanced detection, governance, and data protection configurations that stop web threats, control data movement, and provide visibility and response on unmanaged devices. According to the 2026 CrowdStrike Global Threat Report, AI-enabled adversaries increased their operations by 89% year over year, and 82% of detections in 2025 were malware-free. As enterprises embed AI into everyday operations, AI-driven interfaces are becoming primary work surfaces - and new attack surfaces. Securing those interactions across the entire AI stack is essential to stopping data exposure and adversaries operating through trusted AI workflows. This collaboration builds on CrowdStrike's acquisition of Seraphic, strengthening the Falcon platform's browser-layer runtime protection. Through this integration, Comet Enterprise AI interactions will gain an additional layer of real-time data protection delivered through the Falcon platform, providing visibility into sensitive information and enforcing integrated policy controls as employees research, analyze, and make decisions using AI. Enterprise security policies will be applied directly within AI-driven workflows to prevent unauthorized data sharing and exposure of proprietary content - reducing risk without disrupting productivity. "The browser is no longer just where people access information - it's becoming the interaction layer for enterprise AI. As AI scales across the workforce, organizations expect it to operate within their existing security architecture and controls," said Daniel Bernard, chief business officer at CrowdStrike. "Perplexity has led the way in secure AI browsing since launching Comet, and we're excited to partner with them to extend CrowdStrike's protections into AI-native workflows. CrowdStrike was built to secure the AI era, and together, we're securing AI where work happens - governing agentic risk within a single, AI-native platform." "CrowdStrike is one of the most trusted names in enterprise security, with a proven track record of helping organizations protect their most important workflows," said Dmitry Shevelenko, chief business officer at Perplexity. "Comet Enterprise already gives customers secure, AI-native browsing out of the box, and this partnership adds an extra layer of control for enterprises that want to extend CrowdStrike's protections into the browser." Together, CrowdStrike and Perplexity are securing AI-driven work across the enterprise. About CrowdStrike CrowdStrike (NASDAQ: CRWD), a global cybersecurity leader, has redefined modern security with the world's most advanced cloud-native platform for protecting critical areas of enterprise risk - endpoints and cloud workloads, identity and data. Powered by the CrowdStrike Security Cloud and world-class AI, the CrowdStrike Falcon(R) platform leverages real-time indicators of attack, threat intelligence, evolving adversary tradecraft, and enriched telemetry from across the enterprise to deliver hyper-accurate detections, automated protection and remediation, elite threat hunting and prioritized observability of vulnerabilities. Purpose-built in the cloud with a single lightweight-agent architecture, the Falcon platform delivers rapid and scalable deployment, superior protection and performance, reduced complexity and immediate time-to-value. CrowdStrike: Pangea Cyber Corporation stop breaches. Learn more: https://www.crowdstrike.com/ Follow Pangea Cyber Corporation: Blog | X | LinkedIn | Instagram Start a free trial today: https://www.crowdstrike.com/trial (C) 2026 CrowdStrike, Inc. All rights reserved. CrowdStrike and CrowdStrike Falcon are marks owned by CrowdStrike, Inc. and are registered in the United States and other countries. CrowdStrike owns other trademarks and service marks and may use the brands of third parties to identify their products and services. Forward-Looking Statements This press release contains forward-looking statements that involve risks and uncertainties, including statements regarding a strategic partnership between CrowdStrike and Perplexity to extend enterprise- grade security to Comet Enterprise. You should not place undue reliance on these forward-looking statements, as actual outcomes and results may differ materially from those contemplated as a result of risks and uncertainties. There are a number of factors that could cause actual results to differ materially from statements made in this press release, including partnership terms, evolving legal requirements and industry standards, CrowdStrike Falcon's integration into Comet Enterprise and the availability of solutions thereon, and the risks and uncertainties described in the filings CrowdStrike makes with the Securities and Exchange Commission from time to time, including CrowdStrike's most recently filed Annual Report on Form 10-K, most recently filed Quarterly Report on Form 10-Q, and subsequent filings. All forward-looking statements in this press release are based on information available to CrowdStrike as of the date hereof, and CrowdStrike does not assume any obligation to update any of these forward-looking statements to reflect events that occur or circumstances that exist after the date on which they were made. Media Contact Jake Schuster CrowdStrike Corporate Communications [email protected]
Find jobs on Simplify and start your career today
Don't see your dream role? Check out thousands of other roles on Simplify.Browse all jobs
Industries
Company Size
51-200
Company Stage
Series B
Total Funding
$74.8M
Headquarters
New York City, United States
Founded
2017
Find jobs on Simplify and start your career today
Don't see your dream role? Check out thousands of other roles on Simplify.Browse all jobs