Developers decide whether your AI platform wins
Independent research on what developers adopt, what they abandon, and why, for the product and marketing teams building AI platforms.
Trusted by the top technology companies
From hyperscalers and chip vendors to open source foundations.
- Red Hat
- Salesforce
- CNCF
- Mozilla
- Google Cloud
- Hugging Face
- Anthropic
- Cloudflare
What gets in the way
- The landscape moves faster than anyone can track. Telling real adoption from noise is now a research problem, not a reading problem.
- Telemetry does not capture motivation. You can see what developers did in your product and not why they chose it.
- The competitive picture is unclear. Who is winning, in which category, in which segment, in which region, is genuinely hard to answer.
- New AI features have no defensible market size. Asking for budget without one is asking the board to take your word for it.
- Pricing and packaging have no outside benchmark. Every decision is made against your own history.
- The board wants category leadership, with proof. Claiming it is easy; evidencing it is not.
What we do about it
- Developer adoption and sentiment tracking by language, framework, role, company size and region
- Satisfaction and loyalty measurement
- Segmentation and profiling, with how to engage each segment
- Competitive benchmarking across features, pricing, packaging, docs, SDKs and ecosystem maturity
- Brand awareness and perception studies
- Thought-leadership reports built for PR, events and sales enablement
- Board-ready slides carrying third-party proof
Questions we answer
- Should we build this feature, or kill it?
- How big is the market for this AI use case?
- What will developers pay, and for which bundle?
- Where are we losing to our top three competitors?
- Which developer programmes deserve next year’s budget?
When teams come to us
- A competitor’s pricing or feature move has landed with your core developers
- The board has asked where you lead and where you lag, with proof
- Sign-ups, conversion or retention have moved and nobody can explain it
- A major launch, a funding round or a partnership needs validation
- An AI feature needs independent evidence before it ships
What teams ask us first
“Why not use AI for our research?”
AI does not guarantee the source or the accuracy of what it returns. A decision this size needs data you can trace to a sample.
“Why add this when we already have internal research, or an agency?”
This complements that rather than replacing it. What we add is primary data on developers that nobody else collects.
“Why not track the market ourselves?”
You can track your own market. The part an internal experiment cannot produce is the comparison with peers.
Why SlashData
- Tracking software development trends since 2008. We have tracked them through every platform shift since then.
- 30,000+ developers a year. Reached across 160+ countries, in 8+ languages, through 80+ channels, in two waves a year.
- Developers decide AI platforms. They are the people who adopt the tools, and they are the population we measure.
Frequently asked questions
Where does the data come from?
From developers, asked directly in our own surveys, across 160+ countries.
Can we ask our own questions?
Yes. We design and field a survey to your questions, reaching developers outside your user base, inside it, or both.
Can we filter the data ourselves?
Yes. Data Dashboards give your whole organisation access under one licence, with over 40 filters.
Can you size a specific segment of developers?
Yes. We size the developer population by region, language and technology, using our own model calibrated against survey data.
Talk to an analyst
Tell us the decision you are trying to make, and we will tell you whether we have the data for it.
Talk to an analyst