CONvergence
Your Data Stack Is Eating Your AI Budget
Every system you added to keep up now needs its own license, team, and sync job. Couchbase converges them onto one platform, so that budget and time go to the AI work your board wants.
WHY CONVERGENCE BEATS CUTTING
Stop trimming the stack. Replace it.
Cutting vendors lowers one bill, once. Converging onto one platform lowers the cost of everything you build next, starting with AI.
Spend less running what you already have
Retire the cache, search engine, and sync jobs bolted around your core database. Fewer licenses, fewer servers, fewer 2 a.m. pages.
Give AI data it can trust
Agents read the same live data your apps run on, not a stale copy three pipelines away. Answers stay current and correct.
Run it everywhere you do business
One platform in our cloud, yours, your data center, and on devices that keep working offline. No rebuild for each location.
Key results
53%
of customers cut TCO by more than half after moving to Couchbase
4x
faster rebalancing in Server 8.5, so you size for real demand
カスタマーストーリー
Fewer systems, bigger results
See how industry leaders retired tool sprawl and put the savings to work.
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COMMUNICATIONS
Viber converged two systems into one. Couchbase now serves its data and cache for 15B events a day.
60%
fewer servers, with increased performance
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TELECOM
Türk Telekom moved its databases onto Couchbase and cut costs across hardware and labor.
30%
lower overall cost, with 40% hardware savings
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HOSPITALITY
Marriott chose Couchbase over a legacy rebuild for geo-replicated data and a flexible cloud model.
4K
トランザクション/秒
WHY NOW?
The stack you built is now the thing slowing you down
Nobody plans five databases. They pile up one urgent ticket at a time. Now they cost you money, sleep, and AI momentum.
THE COST
Paying five times for one job
A core database, then a cache when latency slipped, then search, then a vector store for RAG. Each brings a license, a specialist, and a pipeline to keep in sync. Cutting a vendor or two helps for a year. Then the sprawl creeps back, because the architecture never changed.
THE RISK
More systems, more ways to fail
Every system is another contract, another attack surface, and another sync job that breaks at 2 a.m. When two copies of a customer record disagree, your team burns a day figuring out which one is right. Auditors notice. So do customers.
THE STALL
AI pilots that never ship
An AI agent needs live data, search across it, and an answer in milliseconds. Stitch that together from separate systems and you get lag, stale context, and glue code nobody wants to own. The pilot demos well. Then it stalls before production.
More ways to put your data to work
One platform pays off in more places than your cloud bill. See how teams use it at the edge and to scale AI agents.
KEEP WORKING WHEN THE NETWORK DROPS
Innovate at the edge
AMPLIFY YOUR CAPACITY
Scale your agentic workforce
なぜクチベースなのか
Can one platform really do it all?
Fair question. Plenty of platforms claim to do everything and do most of it poorly. Couchbase was built as one engine, not stitched together from acquisitions. It runs caching, documents, search, and AI context for companies like United and Citibank, in the cloud, in their data centers, and at the edge. See how it holds up at that scale, and why teams keep it for the long haul.
One platform, four ways to run it
サーバー
Run it yourself, on premises or in any cloud. One engine for caching, documents, search, vector, and analytics, with no pipelines between them.
カペラ
The same engine, fully managed by Couchbase. Skip the infrastructure chores and put your team on the apps and AI that move the business.
Kubernetes オペレーター
Run Couchbase on the Kubernetes your apps already use, from OpenShift to EKS. The Operator automates deploys, scaling, upgrades, and recovery.
モバイルとエッジ
Extend the same platform to phones, kiosks, and edge sites. Apps keep running when the network drops and sync when it returns.
Keep exploring
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Start building on Capella
Get hands-on with the same engine, fully managed.
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What’s new in Couchbase Server 8.5
Plain-English queries, smarter search, and live joins with your data lake.
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Let’s map your stack
Let’s talk about what your team could build if your data ran on one platform.