How Actian brought VectorAI DB to developers (with Nick Johnson)
Nick Johnson shares how Actian brought VectorAI DB to market and the role Hackmamba played in developer marketing, documentation, onboarding, and product adoption.
On April 28th, Actian launched VectorAI DB at AI Dev x SF, a portable vector database built for AI workloads running at the edge, on-premises, and in air-gapped environments where cloud-native databases aren't an option.
Hackmamba joined the project two months earlier, before the product was ready to launch.
The initial brief was: help Actian reach developers for a brand-new vector database entering one of the most competitive categories in AI infrastructure. The work quickly expanded beyond marketing. We worked with the team on developer onboarding, documentation, product messaging, positioning, activation, community, hackathons, and launch planning. Every decision was measured against a single question: would it help a developer get from first visit to first successful query faster?
By launch day, VectorAI DB had reached more than 300,000 developers across multiple channels. After launch, our work shifted to improving onboarding, refining pricing and messaging, growing developer adoption, and helping Actian hire its first Head of Developer Marketing to continue the next stage of growth.
We're excited to announce VectorAI DB, the first vector database purpose-built for high-performance, reliable AI at the edge.
— Emma K McGrattan (@emmakmcgrattan) April 28, 2026
RAG isn't dead. It just can't run in the environments that need it most.
In manufacturing, 46% of AI pilots never leave the OT network. Healthcare,… pic.twitter.com/l2pErARhGi
I sat down with Nick Johnson, Senior Director of Product Marketing at Actian, to discuss how VectorAI DB came to market. We talked about the launch strategy, changing market conditions, and the lessons learned from bringing an AI infrastructure product to developers.
1. Every product starts with a customer question
I asked Nick what convinced the team that VectorAI DB needed to exist.
Before VectorAI DB, Actian didn't have a GenAI strategy beyond helping organizations ensure they had quality AI-ready data. We would get asked by customers and prospects at trade shows all the time what our AI strategy and products were. With an understanding that vector databases are the core foundation of AI apps, we learned that many of our existing technologies also had the foundation for creating a vector database
I think product teams underestimate repeated customer questions. When the same problem keeps surfacing across conversations, it's usually worth investigating. That's exactly what happened with VectorAI DB. The demand came first. The technology was already there. The product connected the two
2. On-prem was the open lane
I asked Nick how he positioned VectorAI DB so developers immediately understood where it fit in a crowded category.
A lot of it happened by accident. When we were researching how to differentiate against other vector databases, it became clear that the on-prem nature of our database ecosystem was actually an advantage. There were so many news reports about increasing regulations with AI and breaches of cloud-native databases, that having an on-prem, disconnected, and embeddable vector database turned out to be attractive in the market
I've learned that good positioning starts with what your product is genuinely built to do. Actian already had years of experience building software for on-premises, edge, and air-gapped environments. VectorAI DB brought that expertise to the AI market at a time when more organizations wanted AI close to their data. That gave the team a clear story customers could immediately understand.
3. DevRel or developer marketing
I asked Nick what it actually takes to launch a product for developers.
Actian traditionally focused on selling to the C-Suite and Application Owner, so we knew that marketing to developers would have to be an organizational-wide shift. At first, we looked at building out the developer relations function, but we knew that DevRel often focused more on serving existing developer needs and being completely separate from the marketing funnel. Instead, we decided to move forward with the developer marketing function, because we need the developer expertise of DevRel, with the content creation and funnel-creation know-how of marketing. That's why we hired Hackmamba.
Nick's answer reinforced something I've seen across developer-first companies. Launching a product for developers takes more than DevRel. It requires a developer marketing function that can consistently create demand, capture intent, and turn interest into product adoption. For VectorAI DB, these were the channels we invested in, many of which continue to be core developer acquisition channels today:
Technical content and distribution
Led by our Head of Content, Henry Bassey, technical content became our second-largest developer acquisition channel. We focused on middle- and bottom-of-funnel topics that answered the questions developers ask when they're actively evaluating a tool. We published implementation guides, tutorials, comparison articles, benchmarks, and thought leadership pieces. Below is one of our articles ranking on AI overviews.
Distribution was led by our Developer Advocate, Asjad Khan, who built a repeatable distribution strategy across Reddit, developer communities, newsletters, and other technical channels. Every piece of content was shared where developers were already discussing AI infrastructure, vector databases, and retrieval systems. That combination of high-quality content and consistent distribution continued driving qualified traffic, signups, and AI search visibility. Below is an example of one of our Reddit seeding efforts.
Performance marketing
Led by me, performance marketing became our largest developer acquisition channel. We built search ads campaign around high-intent keywords for vector databases, RAG, embeddings, and AI infrastructure, capturing developers already evaluating solutions. We also ran Reddit Ads across AI, machine learning, and vector database communities to reach engineers earlier in the evaluation process.
Every high-intent visitor from Google Ads, organic search, and Reddit Ads who visited key pages like pricing, documentation, or product pages was added to our LinkedIn retargeting audiences.
Creator and newsletter partnerships
We partnered with developer creators and sponsored technical newsletters to reach developers who discover new tools through creators and curated publications. `
Video content
Videos gave developers another way to evaluate the product. We published walkthroughs, tutorials, and implementation videos that showed how VectorAI DB worked in practice.
Universities and hackathons
Led by Geri, university workshops and hackathons became an early developer acquisition and feedback channel. Developers built real projects with VectorAI DB, giving the team immediate insight into onboarding friction, documentation gaps, and SDK usability. That feedback was incorporated into the product and documentation throughout the launch.
Looking back, this is what a developer marketing function looked like for VectorAI DB. Every channel solved a different problem. Together, they helped developers discover the product, evaluate it, build with it, and give the team feedback that improved every release.
4. Why bring in outside help?
I asked Nick when he realized VectorAI DB needed outside support, and why Hackmamba specifically.
We had been trying to hire a Developer Advocate for over a year with no luck. When we learned we would have four months to launch VectorAI DB from concept to market, we knew we needed outside support to help us scale quickly.We researched several developer marketing agencies and set up calls, but Hackmamba was the one that understood our needs from the get-go and proved they could help us scale. William's background as a developer, product manager, and marketer really stood out. He could advocate for our developer marketing needs with leadership and engineering because he understood marketing, software engineering, and how software businesses are built as a product manager.
Product teams spend months removing engineering bottlenecks before launch. They should apply the same thinking to go-to-market. A missing developer advocate, incomplete documentation, or a lack of technical content can delay adoption just as much as an unfinished feature. In those moments, the question isn't whether to build the capability internally or externally. It's whether you can remove the bottleneck before it slows the launch. For VectorAI DB, that meant bringing in a team with deep experience in developer products.
5. The foundations behind developer growth
I asked Nick where Hackmamba had the biggest impact on the VectorAI DB launch, and what made the biggest difference.
Hackmamba's biggest impact came in four areas. Building trust with our leadership team to help us push for the needs of modern developers. Creating developer content that resonated because it was deeply researched and never felt AI generated. Building relationships with universities so students could try out VectorAI DB and give us early feedback on the product before launch. Helping advise on creating the foundations for our PLG motion, whether it was licensing, developer touchpoints, or the developer events that supported our launch.
Looking back, those four areas touched almost every part of the developer journey. We worked with the product, engineering, and marketing teams to improve how developers discovered VectorAI DB, how quickly they could get started, and how the team gathered feedback to make each release better. That work continued well after launch as the product, documentation, and developer ecosystem expanded.
6. The shelf life of AI positioning
I asked Nick whether there were assumptions about the market, messaging, or developer behavior that changed after launch.
This was not exactly a surprise, but the challenges of converting free trial users to paid customers was. I think the assumption that developers would buy an annual contract was incorrect. I think also the speed that the AI market is changing was also a bit shocking. Shortly after we launched, we needed to update our messaging to align with changes around agent frameworks. It became super clear how important it is to stay up to date with market signals so that we are constantly in the same conversations developers are having.
The pricing lesson didn't surprise me. Developers are excellent evaluators, but they're rarely the people approving an infrastructure contract. They want to integrate the product, test it under workloads, and decide whether it's worth adopting. Pricing should support that process, not influence technical decisions like chunking or retrieval quality.
The bigger lesson is how quickly AI categories evolve. VectorAI DB launched when developers were focused on vector search and RAG. A few months later, the conversation shifted to agent frameworks, MCP, evaluation, and observability. Vector databases remained essential, but they became one component of a broader AI stack.
The shift isn't unique to Actian. It's happening across the developer tooling ecosystem.
LangChain is a good example. What started as a framework for chaining LLM calls evolved into LangGraph and LangSmith as developers moved from experimentation to building production-ready agent systems. Harrison Chase describes it as three generations of agent frameworks in just three years.
AI is moving quickly, and developer priorities move with it. Positioning needs regular updates as new workflows emerge, developer expectations change, and categories evolve. Keeping that message aligned with how developers build helps products stay relevant over time.
7. Build the product like a startup
As we wrapped up, I asked Nick what advice he'd give a Head of Product Marketing preparing to launch a new AI developer tool.
If you're in a larger company, focus on the customer experience first and treat the product like a startup. A new product needs room to experiment, iterate, and even fail. Build a dedicated team that's focused only on the product. Avoid forcing it into existing processes, technology, or internal systems. In a PLG motion, the customer experience is often shaped by the tools and systems behind it. Meta had it right. Move fast and iterate.
New products shouldn't inherit the same approval processes, release cycles, and cross-functional dependencies that exist for mature product lines. Those systems were designed for products with established customers, predictable roadmaps, and lower tolerance for risk. A new product needs the autonomy to ship quickly, gather feedback from early adopters, and iterate while the market is still forming. Every additional approval or dependency slows that feedback loop, making it harder to learn what customers actually need and adapt before the opportunity passes.
8. Looking back on our partnership
To close our conversation, I asked Nick how he would describe Hackmamba's role in helping Actian bring VectorAI DB to developers.
We could not have achieved what we achieved without the help of Hackmamba. From the advisory role you played to the quality of the videos and technical content, to the hackathons and event support, Hackmamba's expertise was essential to every part of this launch. Thank you for everything! - Nick Johnson, Senior Director of Product Marketing at Actian
We have a simple belief at Hackmamba: our growth follows our clients' growth. That's the approach we brought to VectorAI DB, and it's the same approach we bring to every developer product we work on.
Looking ahead
Thank you, Nick, for taking the time to answer these questions and for being candid throughout the interview.
Our work with VectorAI DB now has three priorities.
- First, scale the developer acquisition channels we've built over the past few months and get more developers signing up for the product.
- Second, help the team improve the onboarding experience so developers can move from sign-up to building production-ready applications with VectorAI DB faster.
- Third, help the team convert developer adoption into revenue by strengthening the path from first use to paid customers.
We'll also be joining the team at HexaFalls and the Autonomous Agents Hackathon in San Francisco, where developers will get hands-on experience building with the product.
We also helped Actian hire a Head of Developer Marketing. We'll continue working with the team as they expand the programs we built and grow the developer marketing function internally.
Every developer product has a different journey, but the challenge is often the same: helping more developers discover the product, get to their first success quickly, and become long-term users. If you're working through those challenges, we'd be happy to share what we've learned from working with companies like Actian. Book a call today
