Q: Cost model

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.

Q: Why team overhead grows with every system you add

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.

Q: How we apply premium support costs

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.

Question 1

Adjust cost basis & staffing assumptions

Accepted Answer

Cost basis by tool

Defaults use each vendor's own public pricing or published benchmark, per unit of throughput, per year. Adjust to your actual negotiated rates for a more precise number.

Firestore

$ / ops-sec / yr

Ditto

$ / ops-sec / yr

MongoDB (Atlas Dedicated)

$ / ops-sec / yr

DynamoDB

$ / ops-sec / yr

Redis

$ / ops-sec / yr

Memcached

$ / ops-sec / yr

Oracle

$ / ops-sec / yr

SQL Server

$ / ops-sec / yr

PostgreSQL

$ / ops-sec / yr

MongoDB (Vector, Scalar Quantization)

$ / ops-sec / yr

AlloyDB

$ / ops-sec / yr

Pinecone

$ / ops-sec / yr

Elasticsearch

$ / ops-sec / yr

Couchbase (Capella Enterprise)

$ / ops-sec / yr (support included)

Couchbase (Capella Enterprise) — Vector Search

$ / ops-sec / yr (support included) — used only when a vector search tool is selected, in place of the general rate above

Support plan (% of spend)

Whether premium support is already included varies by vendor — check each tool's badge or tooltip. Add it here as a % of that vendor's spend where it isn't already included.

MongoDB (Atlas Dedicated + Vector)

MongoDB does not publicly disclose Enterprise support pricing. Enter what your plan actually costs, as a % of your MongoDB spend — applies once even if both MongoDB rows are selected.

Redis

Redis does not publish a clean support-tier percentage. Enter what your plan actually costs, as a % of your Redis spend, if applicable.

Pinecone

Pinecone's Pro-level support is already included in the base price. Its Premium tier requires an Enterprise annual commitment with no published price — enter a % here only if you have a quote.

Elasticsearch

Elastic publishes this rate directly: Gold +5%, Platinum +10%, Enterprise +15% (defaulted here). Adjust if your plan differs.

Question 2

Cost model

Accepted Answer

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.

Question 3

Not every vendor is priced the same way

Accepted Answer

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.

Question 4

Why team overhead grows with every system you add

Accepted Answer

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.

Question 5

How we apply premium support costs

Accepted Answer

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.

Question 6

Vector search recall isn't apples-to-apples

Accepted Answer

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.

Question 7

How we estimate LLM token savings

Accepted Answer

annual savings = annual model spend × addressable share × cache hit rate, shown as a range. Reported as its own line, deliberately, since inference costs vary.

Question 8

How does the database TCO calculator work?

Accepted Answer

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.

Question 9

How accurate are the cost estimates?

Accepted Answer

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.

Question 10

Do I need to share my email to use the calculator?

Accepted Answer

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.

Question 11

What databases can I compare in the calculator?

Accepted Answer

The calculator covers MongoDB, Redis, Elasticsearch, DynamoDB, Postgres, Oracle, SQL Server, Firestore, Ditto, AlloyDB, Pinecone, and Memcached across document, cache, SQL, vector, and search.

Question 12

What is database convergence?

Accepted Answer

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.

Question 13

Why not use a cheaper dev/test tier?

Accepted Answer

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.

Question 14

How is Complexity Reduction calculated?

Accepted Answer

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.

Question 15

Why are savings shown as a range?

Accepted Answer

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.

Question 16

What does $/ops-sec/yr mean?

Accepted Answer

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.

Question 17

Do vector and token costs double-count?

Accepted Answer

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.

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