What's in this set
AngelMatch's pitch deck database currently holds 8 database pitch decks, which makes this one of the smaller industry filters in the collection. That is worth saying plainly up front: you will not find dozens of examples here, but every deck below is a real fundraise, and a small set still teaches you something a generic template cannot. Seed is the most common round in this filter, followed by Series A and pre-seed, and the raises span 2013 to 2019, with most of the activity concentrated from 2016 onward.
Start with ArangoDB's deck, which raised $10M in a Series A round in 2019 for an open-source multi-model database built around one query language for documents, graphs, and key-value data. It is worth studying because it had to make a technical bet legible to investors who are not database engineers. Ethic's deck raised $13M in a Series A round in 2019, the largest raise in this set, for a data-driven asset manager, and shows how a database-heavy product can be pitched through trust and portfolio outcomes rather than the technology itself. Improvado's deck raised $3M in a seed round in 2018 to aggregate marketing data from dozens of sources into one place, and its pitch leans on integration breadth as the moat.
The rest of the set shows how differently this category can raise. Nazar's seed deck raised $42.5K in 2016 for agentless database performance monitoring aimed at IT teams, and FriendlyData's deck raised $150K in 2017 for a natural language interface to databases. Both raised far less than ArangoDB or Ethic, and both are still useful references if your own round is closer to $150K than $10M. Every deck here links to its own page, where you can view the full deck with an AngelMatch pitch deck database plan.
One more pattern worth noticing before you build your own deck: almost none of these companies are tagged to database alone. ArangoDB also carries a big data and enterprise software tag, FriendlyData is tagged across analytics, AI, and natural language processing, and Improvado sits under advertising and marketing as well as database. If your product stores or moves data but the pitch is really about analytics, AI, or a specific vertical, do not force a pure infrastructure story. Lead with the problem you solve and let the database technology support that story instead of leading it.
What investors look for in a database pitch deck
Database and infrastructure decks get judged on different criteria than a typical SaaS pitch, and the decks in this set make that clear.
Technical differentiation has to be provable, not asserted. ArangoDB raised its $10M Series A in 2019 by showing a genuinely different core: one engine handling documents, graphs, and key-value data instead of three separate systems. Investors in this category want benchmarks, not adjectives. If your deck says your product is faster or more flexible, back it with a number or a side-by-side comparison against the incumbent you expect customers to switch from.
Developer or user adoption substitutes for traditional traction metrics early on. A database or dev tool rarely has meaningful revenue to show at pre-seed. Bytez raised $150K in a pre-seed round in 2019 as a tool for developers and data scientists, and decks at this stage lean on usage, downloads, or waitlist size rather than ARR. If you have none of those yet, a working demo and a clear technical roadmap can carry the deck instead.
Integration depth is a moat when the core technology is not proprietary. Improvado's $3M seed round in 2018 did not rest on a novel database technology. Its pitch argues that connecting dozens of data sources without engineering effort is hard to replicate, and that is the argument worth making if your product sits on top of commodity infrastructure rather than inventing new infrastructure from scratch.
Trust and compliance carry more weight than raw technical specs when the data touches money. Ethic's $13M Series A in 2019, the largest deck in this set, was for a data-driven asset manager. When your database or data product feeds financial decisions, investors weigh security, auditability, and regulatory exposure alongside the technology itself, so budget a slide for how you handle sensitive data even if your product is early.
The ask should match how early the technology still is. Angel checks in this category typically run $25,000 to $500,000, and VC checks run $2M to $100M or more depending on stage. The decks in this set raised anywhere from $42.5K to $13M, which is a wide range even for one industry filter, and it reflects how differently a pre-seed infrastructure bet and a Series A with real customers get priced. Ask for what the next 12 to 18 months of technical milestones actually cost, not a round number that sounds impressive.
If you want a starting point for your own deck instead of building from a blank file, AngelMatch's fundraising templates cover the standard slide order most investors expect.
Once your deck is in shape, the next step is finding investors who actually write checks in this space. AngelMatch's investor database lists 1,700+ investors tagged to the databases market, the same pool that backed decks like ArangoDB's $10M Series A. You can find database and infrastructure investors here and filter by stage or check size before you start outreach, instead of cold-emailing generalist funds who have never backed anything in this space.
Big data pitch decks
