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Overview
Company Historically Provides H1B Sponsorship
Prepare a concise company summary describing what Confluent does, how its products work, how it differs from competitors, and its goal.
About Confluent
Industries
Data & Analytics
Enterprise Software
Company Size
1,001-5,000
Company Stage
IPO
Headquarters
Mountain View, California
Founded
2014
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Simplify's Take
What believers are saying
- March 17, 2026 IBM paid $31 per share, validating strategic enterprise value.
- September 3, 2026 Granite Time Series models entered Confluent Cloud early access.
- September 10, 2026 FedRAMP Moderate authorization opened federal deployments on AWS GovCloud.
What critics are saying
- March 2026 IBM acquisition triggered 800-plus layoffs, hurting morale and execution.
- January 2026 shareholder lawsuits challenged merger disclosures, adding integration distraction and legal cost.
- September 9, 2026 AWS us-east-1 disruption hit Authentication, Schema Registry, Flink, and ksqlDB.
What makes Confluent unique
- Apache Kafka leadership and Confluent Cloud anchor IBM's real-time data stack.
- June 2026 Kafka 4.3.0 shipped 25 KIPs, proving deep platform control.
- May 2026 AI features embed Flink, dbt, MCP, and governance into one workflow.
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Funding
Total Funding
$2.3B
Above
Industry Average
Funded Over
8 Rounds
Coatue Management
Index Ventures
BenchmarkBenefits
Best Teammates on Planet Earth - Loving your job has a lot to do with the people around you. Luckily at Confluent, you will find some of the most genuine people who make you excited to come to work each day.
Adjustable Working Arrangements - While most of our employees work typical business hours, we encourage everyone to partner with their manager to make a schedule that works best for them and Confluent.
Robust Benefits - Health and wellness is important and Confluent is proud to offer a total benefits program that ranks in the top percentile of companies similar in size to our industry in our established geographies.
Rest and Recharge Days - Personal time off is great but it is even better when your whole team has the day. Each quarter, teams have three recharge days where the entire team logs off and refreshes before coming back to work.
Weekly Lunch Spend - At Confluent, we have a weekly lunch program called “No Pay Thursday.” On Thursdays, lunch is provided by Confluent at local restaurants and grocery stores, taxes may apply for some countries.
Flexible Paid Time Off (PTO) - Confluent employees work really hard to meet the needs of our growing and scaling business. To make sure that we don’t burn out, we encourage everyone to balance their PTO in an adequate way.
Stock Price
Growth & Insights and Company News
6 month growth
↑ 4%1 year growth
↑ 4%2 year growth
↑ 5%Confluent: Stale, unreliable data could impair enterprise AI. Wed, 9th Sep 2026 (Today) Australian enterprises looking to scale artificial intelligence need to focus less on the capabilities of AI models and more on whether the data feeding those systems can be trusted, according to Confluent, an IBM Company. The data streaming company says trust is emerging as a major barrier to enterprise AI adoption as organisations move beyond experimentation and begin putting generative and agentic AI applications into production. The focus of the AI conversation has changed significantly over the past 12 months, said Andrew Foo, VP Customer Solutions APAC, Confluent, an IBM Company. "In 2026, the promise of AI and AI's agency to reason, make decisions, and take action has very much taken over a lot of the data discussions," he said. While much attention had initially centred on what AI models could do, organisations are now confronting the practical challenges of deploying those systems safely and reliably, he said. "The challenge now is increasingly shifting from what the models themselves can do, to whether the businesses can actually trust the data that's underpinning it," Foo said. Confluent's 2026 data streaming report, based on a survey of about 4600 IT leaders, found that 72 per cent cited challenges around areas including data lineage, timeliness and data quality assurance. Sixty-nine per cent said insufficient real-time data infrastructure was holding back their ability to scale AI. These findings highlight a fundamental issue for businesses seeking to move AI from pilot projects into business-critical applications. They need confidence that the information supplied to generative AI systems is accurate, current and sourced appropriately, whilst ensuring the right controls are in place over who can access that data. That challenge becomes even more significant as companies adopt agentic AI systems capable of taking actions rather than simply generating answers. As a result, organisations need to expand their approach to AI governance beyond the models themselves and towards the data continuously feeding them. Foo warned that organisations should not view governance and speed as competing priorities, particularly as they face pressure to keep pace with the rapid development of AI. Instead, security and governance should be incorporated into data architectures from the beginning rather than being added after an AI application has already been built. "What they should be thinking about is embedding this into their data architecture from the outset," he said. Confluent advocates a 'shift-left' approach to data governance, moving controls closer to the source of the data instead of attempting to introduce them further downstream. It has introduced capabilities designed to support that approach, including automated personally identifiable information detection and redaction within data streams. This allows organisations to protect sensitive information before it reaches downstream AI applications. Confluent has also introduced private connectivity capabilities intended to allow enterprises to connect AI workloads to external models without sending sensitive traffic across the public internet. Such capabilities are particularly relevant in highly regulated sectors including financial services, healthcare and insurance. Foo is adamant that governance should become an embedded part of how organisations build applications rather than being treated as a separate compliance exercise. "You don't do a data governance project. You do data governance in every project," Foo said. "The same applies for AI. You don't just do an AI governance project. You do AI governance in every project." Real-time data becomes critical Another major challenge for organisations adopting AI is the freshness of data available to those systems. Traditional approaches based on batch and micro-batch processing are becoming inadequate for AI applications that need to respond to rapidly changing business conditions. "AI is only as useful as the data and the context that's available to it," he said. "In many enterprise scenarios, yesterday's information simply isn't enough." Businesses continuously generate new events, from financial transactions and customer interactions to inventory movements, security incidents and operational updates. Connecting AI systems to those live streams allows applications to make decisions based on current conditions rather than historical snapshots. Foo cited fraud detection in the banking industry as an example. A bank assessing a new credit card application may have information indicating that a customer previously represented a relatively low risk. But in the hours since the bank's last batch update, that same customer may have made unusual transactions, logged in from another country or used an unfamiliar device. An AI system relying on stale information could therefore approve an application that should have been flagged. With real-time data, however, the system can combine information about transactions, devices, geography and other risk indicators to make a decision based on what is happening at that moment. "AI is doing the right thing. It simply didn't have access to the real-time information it needed to make the right decision," Foo said. For Confluent, the role of data streaming is to turn those continuously changing business events into governed and trusted context that AI applications and agents can use. Where technology leaders should start For companies trying to strengthen their data foundations, the first step should be understanding the information already flowing through the business. Technology leaders need to understand where data is coming from, how fresh it is, who can access it and whether it can be trusted. Foo cautioned against treating AI as a standalone project that requires an entirely separate data environment. Instead, organisations should look to securely connect AI applications to the operational data already powering the business. Leaders should also identify security and governance requirements early, particularly for sensitive and regulated information, rather than attempting to retrofit controls once an AI application reaches production. Focus then shifts to creating governed, real-time data products around the core entities of the business, including customers, products, orders and inventory. "If you have this as a strong foundation from the outset, it will ultimately make it easier to innovate without introducing unnecessary complexity or risk when it comes to advancing your AI ambitions," Foo said.
Time series foundation models change how Mgks build real-time AI. I've watched teams spend months wiring ML models into production only to watch them drift within weeks. The plumbing always takes longer than the math. IBM and Confluent just announced something that sidesteps that entire problem: time series foundation models that live natively inside streaming infrastructure, callable from SQL, swappable with a parameter change. This matters more than the typical integration announcement because it reorganizes where the work actually happens. The old way still dominates. Most forecasting today is statistical models and spreadsheets dressed up as decisions. Teams model the few hundred products or systems where the money is obvious, then cover everything else with safety margins. Extra inventory. Extra headroom. Extra tolerance. That margin gets paid every cycle, and nobody could forecast it anyway. Bespoke ML promised better. One model per series. Hand-tuned. Refit monthly. But one model per series means specialization: data scientists build pipelines, data engineers wire them, and the domain expert who actually owns the decision waits. A demand planner can't touch it. A process engineer can't tune it. A fraud analyst can't customize it for new corridors without filing a ticket. So most organizations still don't bother. The tail of their catalog, their equipment, their transactions runs on margins because modeling thousands of series is not rational when each one takes specialist time. What changes with a foundation model at the edge. A time series foundation model trained across millions of signals learns patterns that generalize. Give it a window of measurements it has never seen and it forecasts what comes next, scores how far behavior sits from normal, finds similar history, and optimizes toward a target. More important: a demand planner can point it at their data stream. A process engineer can customize it on their line. No data science ticket required. IBM ran these in their own operations first, then with design partners in cement, steel, food, telecom. The numbers are real: every point of accuracy in forecasting is worth millions in working capital. Productivity gains run 5 to 10 times. Work that waited for specialists now sits with the people who own the decision. But running a model is not the same as running it in time. A signal's value decays with time. A pump caught drifting today is a work order. The same pump next week is an outage. That's why Confluent matters here: they bring the live state of the business to the model, managed as stateful streams inside Apache Flink, keyed per series and fault-tolerant. No separate database hit. No external feature store query. The model gets the history it needs to make the next call matter. The portfolio approach beats the silver bullet. Here's what I find most pragmatic about this: IBM is not offering one model. They're offering four, all related to foundation models but built for different questions. PatchTST-FM reads time series the way language models read text, patch by patch, each variable in its own channel so noise doesn't cascade. It returns a full distribution so a planner can set reorder points off the 90th percentile. FlowState keeps a running summary with every point and handles both seconds-level sensor data and hourly market data because its dynamics are continuous in time. TTM drops attention for tiny mixing networks so a million-parameter model covers a hundred thousand series nightly on CPU. TSPulse pairs time and frequency views for anomaly detection and the question every operator asks: have Mgks seen this before? Switch models with one SQL parameter. No pipeline redesign. No separate ML stack. That's not a feature, that's a philosophy. Why this matters for how you ship. I think the deeper shift here is about where decisions live. Right now, forecasting and anomaly detection and optimization are typically batch jobs. Nightly runs. Reports. The business reacts the next morning. But in a streaming context, forecasts become triggers. A replenishment order fires automatically. A fraud alert lands before money moves. An optimizer recommends the next setpoint as conditions change. The gap between an event and knowing about it shrinks from days to seconds. And because the model understands context, the score that lands on the downstream topic is already actionable: not just anomaly detected, but here's the closest past case and here's what happened then. Streaming data was always supposed to enable this. Now the ML tooling catches up. Small model size was a deliberate choice, not a compromise: inference runs on your own CPUs or natively inside Confluent Cloud with no external API call per decision. No cloud ingress or egress. That keeps architecture simple and cost rational at scale. The real test will be adoption: whether domain experts actually use these models on their own, or whether bottlenecks just move upstream. But the path is clearer now than it was last year, and that matters.
Confluent has secured a $510 million term loan B facility to fund its acquisition of A&E Networks. The financing, finalised on 13 July 2026, forms part of a broader capital structure adjustment for the strategic deal. The senior secured debt will finance the transaction and related expenses. It will be used alongside existing equity and other debt instruments as a key component of the capital stack. The acquisition is expected to expand Confluent's market reach and diversify its revenue streams. The transaction remains subject to regulatory approvals and customary closing conditions. Investors are advised to monitor the company's upcoming earnings call for further details on the financing structure and integration plans.
Apache Kafka 4.3.0 release announcement. Jun 1, 2026Read Time: 5 min Confluent Enterprise is proud to announce the release of Apache Kafka(R) 4.3. This release contains many new features and improvements. This blog post will highlight some of the more prominent ones. For a full list of changes, be sure to check the release notes. With 25 KIPs and over 600 commits since 4.2.0, this release introduces many new features, improvements and bug fixes to all the components. See the Upgrading to 4.3 section in the documentation for the list of notable changes and detailed upgrade steps. Deprecation notices. * KIP-1244 Drop support for streams-scala in Kafka 5.0 (deprecate in 4.3)Deprecates the streams-scala module. Marked for removal in Apache Kafka 5.0. * KIP-1237: Deprecate group.coordinator.rebalance.protocols configDeprecates the group.coordinator.rebalance.protocols broker configuration. Marked for removal in Apache Kafka 5.0. * KIP-1280: Update MirrorMaker to use KIP-877 to emit metricsDeprecates the existing MirrorMaker metrics. They are marked for removal in Apache Kafka 5.0. Users should transition to the new metric names. Kafka broker, controller, producer, consumer and admin client. * KIP-1023: Follower fetch from tiered offsetAdds a new broker configuration, follower.fetch.last.tiered.offset.enable (default: false). When enabled the last tiered offset is used as the start offset when bootstrapping a new follower. * KIP-1066: Mechanism to cordon brokers and log directoriesIntroduces a new configuration, cordoned.log.dirs to cordon log directories. New partitions cannot be placed on a cordoned log directory. This can be used when scaling or decommissioning brokers or log directories. * KIP-1196: Introduce group.coordinator.append.max.buffer.size configIntroduces the group.coordinator.append.max.buffer.size and share.coordinator.append.max.buffer.size configurations to set the maximum buffer size the coordinators can use. There are also metrics to track the buffer usage. * KIP-1208: Add prefix to TopicBasedRemoteLogMetadataManagerConfig to enable setting admin configsIntroduces a new prefix remote.log.metadata.admin. for setting configurations for the admin client used by the tiered storage's RemoteLogMetadataManager. * KIP-1211: Align the behavior of num.partitions and default.replication.factor for topic creationFixes inconsistencies how num.partitions and default.replication.factor were applied when creating topics. * KIP-1219: Configurations for KRaft Fetch and FetchSnapshot Byte SizeAdds new broker configurations, controller.quorum.fetch.snapshot.max.bytes and controller.quorum.fetch.max.bytes, to control the maximum amount of data Fetch and FetchSnapshot requests can retrieve. * KIP-1235: Correct the default min.insync.replicas to 2 for the __remote_log_metadata topic Adds a new broker configuration, remote.log.metadata.topic.min.isr, to set the minimum in-sync replicas for the internal topic used by tiered storage. * KIP-1240: Additional group configurations for share groupsAdds a number of new broker and group configurations to control the behavior of share groups. * KIP-1251: Assignment epochs for consumer groupsImproves the member epoch validation logic to avoid unnecessary fencing of group members. * KIP-1257: Partition Size Percentage Metrics for Storage MonitoringIntroduces new metrics to track how much of the maximum retention each topic-partition currently uses. * KIP-1258: Add Support for OAuth Client Assertion to client_credentials Grant TypeAdds support for client assertion authentication to client_credentials grant type with OAuth to enhance security and compatibility with OAuth providers. * KIP-1263: Group Coordinator Assignment Batching and OffloadImproves the group coordinator assignment logic to avoid recomputing assignments when unnecessary. * KIP-1274: Deprecate and remove support for Classic rebalance protocol in KafkaConsumer (Phase 1)Logs a message when starting a consumer with the classic rebalance protocol recommending to use the new consumer rebalance protocol instead as the classic protocol will be deprecated in a future release. Kafka Streams. * KIP-1035: StateStore managed changelog offsetsAdds methods to the StateStore API to manage changelog offsets. This is an internal runtime change, and only relevant for custom StateStore implementations. * KIP-1247: Make Bytes utils class part of the public APIExposes the Bytes class as part of the public API so it appears in the javadoc. * KIP-1250: Add metric to track size of in-memory state storesAdds new metrics tracking the number of keys in the in-memory state stores. * KIP-1259: Add configuration to wipe Kafka Streams local state on startupAdds a new configuration, state.cleanup.dir.max.age.ms, to automatically delete state directories that have not been modified for that duration on startup. * KIP-1270: Extend ProcessingExceptionHandler for GlobalThreadAdds a new configuration, processing.exception.handler.global.enabled, to enable ProcessingExceptionHandler to handle GlobalKTable exceptions. * KIP-1271: Allow to Store Headers in State StoresExtends the Processor API to support record headers in state stores. * KIP-1285: DSL Opt-in Support for Headers-Aware State StoresExposes Headers-Aware State Stores (KIP-1271) to the DSL API. Kafka Connect. * KIP-1239: Batch offset translation in RemoteClusterUtilsAdds a new method RemoteClusterUtils.translateOffsets to translate the committed offsets of several consumer groups at the same time. * KIP-1273: Improve Connect configurable components discoverabilityIntroduces a new interface, ConnectPlugin, that all Kafka Connect plugins implement to ensure common methods across all plugin types. * KIP-1280: Update MirrorMaker to use KIP-877 to emit metricsAdds a new configuration, metric.names.formats, for MirrorSourceConnector and MirrorCheckpointConnector to opt-in to the new metric names. Summary. Ready to get started with Apache Kafka 4.3.0? Check out all the details in the upgrade notes and the release notes, and download Apache Kafka 4.3.0. This was a community effort, so thank you to everyone who contributed to this release, including all its users and its 147 contributors (and 3 AIs): 高春晖, 조형준, Abhijeet Kumar, Abhinav Dixit, Alieh Saeedi, Alyssa Huang, Andrew Schofield, Aneesh Garg, Angelo R., Anton Vasanth, ANUSHREE BONDIA, Apoorv Mittal, Arpit Goyal, Artem Livshits, averemee-si, Bill Bejeck, Bolin Lin, Calvin Liu, Chang-Chi Hsu, Chang-Yu Huang, Chia-Ping Tsai, Chia-Yi Chiu, ChickenchickenLove, Chih-Yuan Chien, Chirag Wadhwa, Chris Egerton, Christo Lolov, Claude, Claude Sonnet 4.6, Copilot, cui, Dale Lane, David Arthur, David Jacot, Deepak Goyal, Dejan Stojadinović, dengziming, Ding, Dmitry Werner, Dongnuo Lyu, Donny Nadolny, Edoardo Comar, Eduwer Camacaro, Emanuele Rabino, Emmanuel Oppong, Eric Chang, Erik Anderson, Evan Zhou, Federico Valeri, Fiore Mario Vitale, gabriellefu, Gaurav Narula, Gianmarco, Giuseppe Lillo, gomudayya, Gyeongwon, Do, Harish Vishwanath, Hector Geraldino, high.lee, Himanshu Verma, Hong-Yi Chen, Hy (하이), hy-rice, Ibuki Kaji, Ilyas Toumlilt, Ismael Juma, Izzy Harker, J.V.S Aarathi, Jacob Montemayor, JeevanYewale, Jhen-Yung Hsu, Jian, Jiayao Sun, jimmy, Jinhe Zhang, Joanna-D, Jonah Hooper, José Armando García Sancio, Josep Prat, Jun Rao, Justine Olshan, k-apol, Kamal Chandraprakash, Ken Huang, Kevin Wu, khilesh Chaganti, Kirk True, Kuan-Po Tseng, Lan Ding, Levani Kokhreidze, Lianet Magrans, Lucas Brutschy, Lucy Liu, Luke Chen, Ma Jialong, Mahsa Seifikar, manan.gupta, Manikumar Reddy, mannoopj, Maros Orsak, Matthias J. Sax, Mickael Maison, Ming-Yen Chung, Moshe Blumberg, Murali Basani, Nandini Singhal, Nick Guo, Nick Telford, Nikita Shupletsov, Nilesh Kumar, Lan Ding, Paolo Patierno, Park Jiwon, Parker Chang, Philippus Baalman, PoAn Yang, Prabhash Kumar, Raghu Baddam, Rajarshi Misra, Rion Williams, Rion Williams,, Ritika Reddy, Robin Marechal, runom, S.Y. Wang, Saket Ranjan, Sanskar Jhajharia, Santhan3159, Sean Quah, Shashank, Shivsundar R, Siddhartha Devineni, sstremler, Steven Schlansker, Stig Døssing, Sushant Mahajan, TaiJuWu, TengYao Chi, Tirth, tison, Uladzislau Blok, Viktor Somogyi-Vass, Vincent Jiang, Vincent Potuček, Xuan-Zhang Gong, Zheguang Zhao, Zhiyan Tang, zoo-code Table of Contents * Mickael Maison is a committer and the chair of the Project Management Committee (PMC) for Apache Kafka. He has been contributing to Apache Kafka and its wider ecosystem since 2015. Mickael is a software engineer with over 15 years of software development experience. He is currently working in the Kafka team at Red Hat. He really enjoys sharing expertise and teaching and has been writing monthly Kafka digests since 2018 and enjoys presenting at conferences.
Confluent expands real-time AI capabilities with new security and developer tools. May 20, 2026 Confluent, an IBM company and the data streaming pioneer, today announced new capabilities in Confluent Intelligence and Confluent Cloud that streamline how real-time artificial intelligence (AI) applications are built and secured. These updates remove the security and complexity barriers that stop organizations from moving AI workloads into the real world. Confluent unifies the AI life cycle with tools that developers already live in, integrating Apache Flink pipelines with dbt (data build tool) and introducing a fully managed Model Context Protocol (MCP) server and Agent Skills that let AI manage streaming operations. With automated personally identifiable information (PII) redaction and private connectivity to external models via Azure Private Link, Confluent also embeds enterprise-grade governance directly into the data streams. "Most AI projects fail before they reach a single customer because the data layer breaks down," said Sean Falconer, head of AI at Confluent. "Teams have the models and the mandate, but security risks and fragmented data stop them from shipping. We're fixing that by making the streaming layer the foundation for secure, production-ready AI." The problem is widespread, according to a McKinsey report that says, "... eight in ten companies cite data limitations as a roadblock to scaling agentic AI." Root causes are often tied to security teams blocking data from entering AI pipelines due to exposure risks and developers losing hours to tool-switching to inspect and manage the data streams their AI depends on. The resulting slow, manual process turns what should be a fast iteration cycle into a bottleneck. Confluent Cloud and Confluent Intelligence form the data streaming foundation for production-ready AI that continuously processes historic and real-time data and delivers it as trusted context into AI applications. New capabilities add the security controls and developer tooling that high-stakes industries require. Natural language operations allow developers to use Confluent MCP as a control plane, enabling AI to build, manage, and debug streaming operations using natural language. Agent Skills add a second layer, encoding best practices and workflows so those operations are executed consistently and in line with organizational standards. Together, they enable developers to create and continuously improve real-time applications using AI-powered tools, bringing streaming into modern, agent-driven development workflows. This is generally available for Confluent Cloud. Automated data privacy introduces a new built-in ML function for PII detection and redaction that protects sensitive information directly in Flink SQL, without custom code, external services, or moving data to a warehouse first. This unlocks more AI use cases across highly regulated industries such as financial services, healthcare, and insurance. It is available in early access for Confluent Intelligence. Secure connectivity support for Azure Private Link ensures that AI workloads stay off the public internet with secure, private paths to calling external models and querying external tables. Flink jobs can securely connect to Azure-hosted services such as Azure OpenAI, Azure SQL, and Cosmos DB over Microsoft's private backbone. This capability is generally available on Confluent Cloud. Unified engineering workflows are enabled through the free open source dbt adapter that brings Flink SQL on Confluent Cloud into dbt, the industry-standard framework data engineers use to build and manage data pipelines. Teams can immediately define, test, and deploy streaming pipelines using the same dbt commands and project structure they rely on today. This lowers the barrier to Flink adoption and makes it easier to extend existing data workflows into real-time use cases. It is generally available on Confluent Cloud. Confluent also provides flexibility with additional model support, including support for TimesFM models for robust anomaly detection as well as Anthropic and Fireworks AI models, which developers can directly use in Flink stream processing workflows to build sophisticated real-time AI applications. Highlights include the general availability of the Real-Time Context Engine, which continuously delivers fresh, governed context for AI applications, and new fully managed connectors in Confluent Cloud that further simplify data integration. These capabilities extend recent announcements at IBM Think that integrate Confluent Cloud further into IBM solutions. With Confluent, watsonx.data delivers an AI-ready data foundation and a real-time context layer for AI across hybrid environments.
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About Confluent
Industries
Data & Analytics
Enterprise Software
Company Size
1,001-5,000
Company Stage
IPO
Headquarters
Mountain View, California
Founded
2014
Confluent is Hiring for 21 Jobs on Simplify!
Find jobs on Simplify and start your career today