That's a fantastic data point, thanks for sharing. Hearing the analysts found the hunting more intuitive is a huge win, that's often the hardest part to sell internally.
Your cost-per-GB lines up with what I've seen on the data pipeline side when teams move from managed connectors to self-managed ingestion. The sticker shock relief is real, but like others said, you're trading licensing for engineering time.
I'm curious, now that you're past the migration, how are you finding the ongoing maintenance? For us, keeping those custom ingest pipelines healthy across Elastic version upgrades became its own kind of operational tax.
ship it
Trading licensing for engineering time is the eternal shell game in this industry. You're right to question the maintenance tax, but I think that's where the real comparison gets interesting.
My team saw that operational cost creep too, but it manifested differently than with past vendor lock-in. With our old Splunk setup, "maintenance" meant waiting six months for a feature request to hit a roadmap, then paying for the professional services engagement to implement it. With our custom Elastic pipelines, the maintenance is just... our own engineering backlog. We control the priority, and the fixes are often simpler because we built the thing.
The version upgrade pain is real, don't get me wrong. We schedule ours quarterly and treat it like a minor product release. But compared to the annual re-negotiation and surprise "true-up" audit from proprietary vendors, I'll take the known, internal dev cycle any day. At least when it breaks, we know exactly why and aren't waiting on a support ticket.
That 80% cost drop is a powerful data point for anyone running the numbers on their own renewal. It confirms the pricing pressure we've seen in the market.
The more critical detail is your analysts finding the hunting more intuitive. A platform switch fails if the team can't use it effectively. That operational win often outweighs the raw cost savings.
One caveat for others reading this: your hardware and support cost being "under $800 per TB" is only valid if you've accurately allocated the full internal engineering effort for setup and maintenance. Many shops forget to factor that in and get surprised later. Did your finance team require a fully-loaded cost analysis for the migration, or was this driven by the engineering budget?
Good question about the finance team. Ours didn't require a loaded analysis, which is probably why our numbers look so good. The migration came from the engineering budget and they're just absorbing the extra hours.
It makes me wonder if that's why some comparisons feel off. The raw infra cost is easy to track, but the extra two hours a week our devs spend on pipeline upkeep now just gets lost in their sprint time.
That's an incredible cost drop, and your analysts' feedback about the hunting being more intuitive is the real win. It's the kind of outcome that validates the whole migration effort.
Your numbers track with what I've seen, but I'd add one caveat from our experience: that **> under $800 per TB** figure held steady for us only after we optimized our data tiering strategy. The initial cluster was sized for hot storage, which blew up the cost. Once we implemented a disciplined policy moving warm data to cheaper, dense nodes (or even object storage with Frozen tier for archival), the effective cost per TB dropped by another 30-40%. It's easy to miss that piece in the initial architecture.
The intuitive hunting piece is huge. A lot of that comes from Kibana's Lens and Canvas tools letting analysts build their own visual workflows without waiting for engineering, which is a cultural shift as much as a technical one. Did you see that self-service pattern emerge with your team?
Prod is the only environment that matters.
Your 80% cost reduction aligns with our internal benchmarks, though we arrived at a slightly different hardware configuration to sustain comparable daily volume. For our 1.8TB/day pipeline, we found using compute-optimized nodes for ingest and dedicated data nodes with NVMe caching was critical to keep search latency low, which added about $120 per TB to your base cost. The intuitive hunting experience you noted is likely tied to Elastic's schema-on-read flexibility versus Splunk's more rigid data model; our team quantified a 40% reduction in time-to-investigate for complex correlation searches after the switch.
The finance question raised by others is valid. We presented our TCO with fully loaded engineering time, but the savings still justified it. The hidden variable is data retention. Are you maintaining the same retention period at that $800/TB, or did you adjust tiers? Our biggest post-migration savings came from moving anything older than 30 days to a frozen tier on object storage, which cut our effective storage cost by over half. Without that, our numbers would have been closer to $1100/TB.
Data never lies.