database TCO estimate
What does running multiple
databases actually cost you?
Every extra system in your data stack is another bill, another on-call rotation, and another sync job. Select your stack below for a directional estimate of what one platform could save you.
IMPORTANT DISCLAIMER: Directional estimates to guide your evaluation, not a formal quote. Full methodology below.
See what sprawl is costing you
Select the tools you’re running today. We’ll break down the technology and the people it takes to run them – side by side with Couchbase Capella Enterprise.
Step 1
Current tools
Step 2
Workload
Step 3
Refine the estimate
By default we use each vendor’s own public pricing or published benchmark, and a labor model that scales with how many tools you’re actually using.
Directional annual impact summary
Estimated annual savings
Complexity reduction
Tools selected
Estimated annual token savings – separate from stack
Want to run these numbers against your specific configurations?
A Couchbase architect will size your workload in a 30-minute session and show what one platform looks like for your stack.
Methodology & validation notes
Each tool: infrastructure cost = $/ops-sec/yr × ops/sec, summed across selections. Team & integration overhead is computed once per stack, not per tool — see below — then added to get each side’s total. The savings estimate comes from the gap between the stack’s overhead and Couchbase’s, never a discount off any competitor’s price.
Some vendors charge per operation, some rent a server by the hour, some publish their own benchmark. We price each by however that vendor actually sells — never whichever framing looks smaller. For hourly-rented servers: price ÷ assumed sustained throughput. That throughput assumption (typically ~800 sustained ops/sec per vCPU or core) is ours, not the vendor’s, and disclosed in every tooltip that uses it. It’s a rate, not a workload cap: it’s used once to compute a $/ops-sec/yr price, which is then multiplied by whatever traffic level you enter — changing your traffic doesn’t change the underlying rate. The model assumes that rate scales linearly with however many instances it takes to hit your traffic level, with no penalty or bonus for running more of them.
Team & integration overhead = [0.35 × (tools selected)0.7 + 0.25 × ((tools selected)1.5 − 1)] × (your traffic ÷ 5,000)0.2 × engineer cost. Couchbase’s overhead uses the same formula simplified to 0.35 × (your traffic ÷ 5,000)0.2 × engineer cost — always treated as a single platform, no matter how many tools you’ve selected. Engineer cost defaults to $165,000/year (fully loaded) and is editable in Step 3; it applies identically to both sides of the comparison. One more database doesn’t need a whole extra admin — teams share tooling across systems they already run — so the admin portion grows slower than 1-for-1 as you add tools.
adjusted cost = base $/ops-sec/yr × (1 + support % ÷ 100). Premium support is priced differently across vendors. MongoDB, Redis, and Pinecone sell support separately, and none of the three publish a clean percentage — Step 3 lets you enter your own if you have a quote, defaulted to 0%. Elasticsearch publishes its support-tier percentage directly (Gold +5%, Platinum +10%, Enterprise +15%), so that’s applied by default. Oracle and SQL Server are a different case again: their support fee is technically optional, but standard practice for production use — declining it means giving up patches, updates, and support indefinitely, which is why real deployments overwhelmingly renew it every year. That fee (22%/year, ~25%/year respectively) is already folded into their base figures rather than modeled as a separate toggle. Couchbase Capella’s own price bundles support the same way, with no separate charge to add. MongoDB’s two rows share one support field, since both run on the same Atlas account and would only be billed once in reality. A similar adjustment isn’t offered for databases delivered through a hyperscale cloud platform — Amazon DynamoDB, Google Firestore, Amazon RDS for PostgreSQL, Amazon ElastiCache for Memcached, and Google AlloyDB. Support for these services is typically sold at the account level, spans dozens of unrelated products, and is shaped by enterprise discount agreements that vary widely by customer. Isolating one database’s share of that arrangement would require assumptions too speculative to stand behind, so those rows reflect infrastructure pricing only.
Vector search costs swing widely based on recall (search accuracy). AlloyDB benchmarks at 80% recall. MongoDB only publishes a ~98% tier for scalar quantization benchmark, while Pinecone doesn’t publish their recall at all. To keep this honest, we compare these tools against Couchbase’s vector-specific rate ($14/ops-sec/yr), benchmarked at an identical 80% recall. This makes the AlloyDB comparison a true head-to-head. We can’t perfectly align MongoDB or Pinecone until they publish 80% recall data, but using our verified rate keeps the baseline grounded in reality.
annual savings = annual model spend × addressable share × cache hit rate, shown as a range. Reported as its own line, deliberately, since inference costs vary.
Database TCO Calculator FAQ
Here are answers to the most common questions about how the calculator works, where the numbers come from, and what to do with the results.
Select the databases you run today and your throughput level. The calculator compares your current yearly costs with the same workloads running on one Couchbase platform and shows a directional savings range.
Nearly every default is sourced to public pricing or vendor benchmarks, except Ditto (no public pricing). Support-plan inclusion varies by vendor — see each tool’s badge, and adjust in Step 3. Not a quote.
No. You can run the full estimate without entering your email. Contact information is only requested if you want to book a meeting with a Couchbase architect.
The calculator covers MongoDB, Redis, Elasticsearch, DynamoDB, Postgres, Oracle, SQL Server, Firestore, Ditto, AlloyDB, Pinecone, and Memcached across document, cache, SQL, vector, and search.
Database convergence is an architecture where one platform serves caching, documents, search, and AI, so apps talk to one system. Your team spends its time on app logic, not integration plumbing.
Some vendors sell cheaper dev/test tiers for bursty traffic, real, but not what this tool models against the steady, production-scale traffic assumed here.
1 minus (1 divided by tools selected). One tool = 0%, more tools raise it (2 to 50%, 3 to 67%, 5 to 80%). Directional, the qualitative case, not a formal risk score.
Even a precise price gets multiplied by a workload with real-world spikes a single number can’t capture. Unlock Step 3 with your own contracts for a tighter estimate.
The cost to sustain one operation per second continuously for a year (31.5M ops), letting every vendor’s pricing model be compared on one scale. Some price reads/writes differently, see each tooltip.
No. Deselecting a vector tool already removes its cost from the stack total. The token savings card is model-provider spend only, the two never overlap.
Couchbase TCO Console. Figures are estimates for discussion purposes and are not a binding quote.
Oracle® is a registered trademark of Oracle and/or its affiliates. MongoDB® is a registered trademark of MongoDB, Inc. Redis® is a registered trademark of Redis Ltd. Elasticsearch® is a registered trademark of Elasticsearch B.V. Microsoft® and SQL Server® are registered trademarks of Microsoft Corporation. PostgreSQL® is a registered trademark of the PostgreSQL Global Development Group. Amazon DynamoDB® is a registered trademark of Amazon.com, Inc. or its affiliates. Ditto® is a registered trademark of DittoLive Incorporated. Google Cloud™, Firebase™, Google Firestore™, and AlloyDB™ are trademarks of Google LLC. Couchbase® is a registered trademark of Couchbase, Inc. All other trademarks are the property of their respective owners. Use of these marks does not imply any sponsorship, endorsement, or affiliation between Couchbase and the respective trademark owners.