RealityMine appoints CPO amidst strong growth momentum. Pushing the boundaries of behavioral data. When a company like RealityMine posts a whopping 75% year-over-year revenue bump, you know they're not here to mess around. Enter Soumya Bijjal, a seasoned pro stepping in as their inaugural Chief Product Officer. This isn't just about adding to the C-suite boys' club; it's about locking in on a goldmine of behavioral data that's skyrocketing in demand. RealityMine has its pedal to the metal on this, gearing up to leverage Bijjal's experience to bolster their offerings. Scaling Up in the Age of AI. Now, if you're wondering what Bijjal brings to the table, think about her resume boasting heavy hitters like Nokia and Nordea. She's led product management from startups to scaling giants, notably at Aiven, where she spearheaded their charge from £12 million to over a £100 million in annual recurring revenue. That's no small feat, folks. And here, she's tasked with aligning RealityMine's product strategy with the nitty-gritty realities of AI, commerce, and the ever-changing advertising game. "RealityMine measures what people actually do, not what they report doing," says Bijjal, capturing the unique strength she plans to amplify. Crack the code on real insights. Don't let the jargon fool you - RealityMine isn't just some passive observer. It's a living, breathing organism of data, pulling tangible intelligence from consumer behavior across devices. While others might be content latching onto self-reported surveys and assumptions, RealityMine digs deeper into actual user activity. With Bijjal at the helm, their mission is to transform these insights into valuable, scalable products that don't just report customer departures but decipher the why and predict the where next. Riding the growth wave. One aspect setting RealityMine apart is its massive expansion this year; they've beefed up their workforce by 50%. When you're bringing in additional muscle in engineering, data, and AI expertise, you know something big is on the horizon. Not to mention welcoming Paula Iddon as the Chief People Officer to sustain their vibrant company culture amidst this whirlwind. Investment implications and industry leadership. From an investor's lens, this is a play highlighting a firm that's staking out its territory in a crowded field. Their privacy-safe ethos means clients can sleep a tad easier at night, knowing their data-driven strategies are both robust and responsible. It's about finding the balance between market leadership and the nuances of rapid technological shifts. Revenue isn't the sole measure here. With Bijjal in the drivers' seat, the emphasis is on consistently staying a step ahead - anticipating industry turns and tuning product development to meet these stops on the fly. This leadership appointment signals RealityMine's commitment to not just growth, but sustainable and strategic growth. Wrapping up: A sign of the times. In an era when data is power, RealityMine is arming itself with talent that understands the landscape like the back of their hand. The endgame isn't merely about amassing trends but cementing a strategic foothold that's adaptable and future-proof. As AI weaves deeper into daily narratives, everyone's watching where the tides will take Investors Hangout, LLC next - and with RealityMine's tactical lineup and Bijjal steering the product ship, there's much for investors to feel charged about.

Pay for what you stream: new Aiven for Apache Kafka(R) pricing. Aiven launches consumption-based pricing for Apache Kafka. Pick throughput, retention and topic type. Pay for compute, storage and network. Nothing else. New pricing for Aiven for Apache Kafka(R) is now live for all new customers on AWS, GCP and Azure. For its existing customers: nothing changes. When choosing your cluster you are guided by three things: * Throughput: MB/s of ingress, from near-zero up to hundreds of MB/s+ * Retention: how many days of data you keep * Topic type: classic topics, or diskless topics for high-throughput workloads And you pay for three things: | Line | Rate | What it covers | | Compute | $ per hour, from $235/month | The scale of the cluster, sized by your throughput | | Storage | from $0.10 per GB-month | Total data in object storage, priced by what you retain. That's how much, for how long | | Network | Classic topics: $0.02 in / $0.04 out per GB, Diskless topics: $0.01 in / $0.03 out per GB | Data you actually produce and consume | What this means for you. For its existing customers: nothing changes. Your plans, prices and contracts stay exactly as they are. The above only apply to new services, but do talk to your sales representative if you have questions. New customers see the new model in the console today. Create a service, pick your throughput and retention, and the price is on the screen. Existing customers stay on their current plans and prices, full stop. Aiven will not migrate you, and Aiven will not quietly change your bill. If the new model looks better for your workload, talk to your account team and Aiven will run the numbers with you, including the cases where staying put is cheaper. Its free plan also remains unchanged as does its dev tier. Goals for its pricing structure. Its goals are simple: * Price in the language of streaming: throughput and retention, not CPU cores * Easy to estimate: make every line on the bill traceable to usage, no bundled estimates and no hidden gotchas * Reward efficient architecture: workloads that move to diskless topics pay lower network rates, because object storage does the replication work To get the rates right Aiven asked some fundamental pricing questions: what value does the platform create compared to running Kafka yourself or buying it elsewhere, what does it cost Aiven to serve (for Kafka, network is the real cost), and, as builders ourselves, would Aiven be happy to receive this bill? Pricing Kafka is hard. For six years Aiven priced Kafka the way the industry prices most infrastructure: by the machine. You picked a plan with a name like business-4, defined by CPU, RAM and disk, and hoped it matched your workload. Here is the problem: streaming engineers do not think in machines. Builders of streaming services think in MB/s of throughput and days of retention. Nobody wakes up wanting 9 nodes. Certainly nobody wants to reverse-calculate the disk space they need to provision based on those variables. Machine-based plans force every builder to attempt to translate their workload into hardware. Not only is that inconvenient, it's difficult, and people frequently get it wrong. Aiven spent the past year exploring alternatives. Aiven considered: * per-partition pricing (it punishes good data design) * per-message pricing (unauditable at Kafka scale) * abstract "unit" models (these ended up being opaque: it was too hard to check a unit against a cloud bill) In February Aiven shipped an interim mode, where network cost was estimated and baked into an inflated compute price. It was simpler, but it made every builder pre-pay for a network Aiven assumed, not the network they used. Whilst offering good value, Aiven weren't satisfied. Each model failed the same test: you should be able to predict your bill from your architecture. So Aiven did what Aiven always do at Aiven: build it in the open, ship it, and listen. Why Aiven stopped selling machines. A concrete example. A builder with 1 MB/s in, 3 MB/s out and 3 days of retention used to face a puzzle. Guess startup-4 at $265 and run out of disk. Do the maths properly and you need business-4 plus extra disk: $792 per month, with unnecessary compute idle. On the new model the same workload costs $620: $235 compute, $25 storage, $360 network. Every line maps to something you can measure, and optimize, against your own metrics. Scale to zero traffic and you pay the $235 floor. Grow to 5 MB/s and the bill grows smoothly with you, not in hard to predict jumps. Storage works the same way. Data moves to object storage, so retention is a dial, not a disk-size decision. You will never buy a bigger cluster because a topic got popular over time. Making network pricing predictable. Metered network is the part builders fear, and the fear is earned: the industry has trained everyone to expect shock bills. Aiven designed against that directly. The rates are flat per GB, published on the pricing page, with no tiers and no surcharge for the replication traffic inside your cluster. The pricing calculator shows your full monthly cost, network included, before you create a service. Ingress and egress usage appear in your service dashboard from day one, so the invoice is never the first place you see a number. Diskless topics cut the network line further. Because KIP-1150 moves replication into object storage, Aiven charge half the classic ingress rate. On plans from 10 MB/s upward you can mix classic and diskless topics in one cluster and let each workload pick its own economics. Diskless topics are no longer a separate product or a promo: they are a standard capability of Aiven for Apache Kafka. Next steps. Price your workload in the calculator, or sign up and create a Kafka service in under 10 minutes. The full model is documented in its docs. Pricing is something Aiven expect to evolve as Aiven learn. Just as Aiven did with diskless topics, its commitment is to keep increasing the value of the platform while driving the cost down, not the other way around. Its success is measured by the success of the builders who use the platform. Stay updated with Aiven. Table of contents

Aiven acquires Flow AI, a team building production-grade AI agent infrastructure, to bring agent-native tooling and production data together on one platform.