This document is an order-of-magnitude worksheet, not a benchmark report or a vendor price quote. It’s intended to make assumptions explicit and auditable, so readers can swap in their own numbers (credits, hours, regions, discounts, labor rates, etc.).
It models a fairly common enterprise shape:
- Strict OLTP SLOs (low p99 latency, high concurrency)
- Frequent point-in-time analytics (snapshot-scoped)
- Daily heavier Spark jobs
- On-demand cloud pricing (for comparability)
It compares a “dual-system” approach (OLTP + separate analytics platform + pipeline) versus a unified HTAP approach.
Disk assumption for this model: 20 TB per data node (EBS-backed).
Dataset assumption: 20 TB logical “hot” dataset, RF=3.
It also demonstrates that Open Source Software does not mean a "free lunch" - the cost of running a unified HTAP stack is not zero, but it is significantly lower than the cost of running a dual-system stack. Instead TCO reductions can be seen to come from both how standardisation offers freedom to operate and how the more modern capabilities impact on infrastructure layers.
The unified architecture demonstrated in this repository eliminates costs through:
- Accord transactions (CEP-15): Strict-serializable ACID without separate transaction coordinators or consensus overhead.
- While neither OLTP nor OLAP systems can achieve such Strict-serializable alone, Accord now also provides basic Consistency guarantees over a unified data platform. This can have a material impact on developer productivity and operational complexity.
- Spark bulk reader/writer via Sidecar (CEP-28): Analytics on persisted structures (SSTables) without ETL pipelines.
- Multiple SQL interfaces on one data store:
- Postgres wire protocol for OLTP (application SQL)
- Spark/Presto for OLAP (analytical SQL)
- No data duplication between interfaces
- Snapshot-coordinated analytics: Point-in-time consistency for analytics without copying data to warehouses.
- Native Kafka CDC via Sidecar: Built-in change streams with RF-aware deduplication, no third-party connectors.
- Vector similarity search: Native support without separate vector database licensing.
This eliminates the "dual-system pipeline tax" (tools + people) that dominates TCO in traditional OLTP + OLAP architectures.
graph TB
subgraph "Traditional Dual-System"
A1[OLTP: Postgres/MySQL] -->|ETL/CDC| B1[Pipeline Tools]
A2["Oracle/SQL Server (legacy)"] --> B1
A3["KV/column stores xN"] --> B1
A4["Document stores xN"] --> B1
A6["Caches xN"] --> B1
A7["Vector DB (new)"] --> B1
B1 --> C1[OLAP: Snowflake/Databricks]
B1 --> C2[OLAP: Snowflake/Databricks]
B1 --> C3[OLAP: Snowflake/Databricks]
C1 --> |ETL| B2[Pipeline Tools]
C2 --> |ETL| B3[Pipeline Tools]
C3 --> |ETL| B4[Pipeline Tools]
B2 --> A3
B3 --> A6
B4 --> A7
B1 --> D1[Kafka/Airflow/dbt]
C1 --> D1
C2 --> D1
C3 --> D1
style B1 fill:#ff9999
style B2 fill:#ff9999
style B3 fill:#ff9999
style B4 fill:#ff9999
style D1 fill:#ff9999
end
subgraph "Unified HTAP Stack"
H2[Single ACID Data Store<br/>Cassandra + Accord]
H2 -->|CQL Wire| W1[OLTP Queries]
H2 -->|Postgres Wire| W2[OLTP Queries]
H2 -->|Spark/Presto| W3[OLAP Queries]
H2 -->|Native CDC| W4[Kafka Streams]
H2 -->|Vector Search| W5[AI/ML]
style H2 fill:#99ff99
end
- Hot dataset (logical): 20 TB
- Replication factor (RF): 3 ⇒ ~60 TB raw
- Operational headroom: +30% (compaction, streaming, repair, snapshots, safety) ⇒ ~78 TB raw
- Workload shape: streaming ingest + strict OLTP SLOs + frequent snapshot analytics + daily heavier Spark jobs
- Cost posture: on-demand pricing, single region (examples use AWS us-east-1)
- Primary platform bills (compute + storage)
- A separate line item for the “pipeline tax” of dual systems:
- Tools/SaaS commonly added in practice (ingestion/orchestration/observability)
- People cost bands (incremental labor attributable to running dual platforms + pipelines)
- Data transfer / egress charges
- Reserved Instances / Savings Plans / committed-use discounts
- Vendor-negotiated enterprise discounts
- Backups/DR beyond what is implicitly assumed
- Security/compliance platform costs (IAM, key mgmt, audit tooling)
- "Cost of delay" / opportunity cost (usually dominates, but hard to quantify)
Validation note: These calculations use publicly documented pricing and credit consumption rates. For your specific evaluation:
- Run the demo stack in this repository to measure actual resource utilization for your workload
- Use vendor calculators with your actual query patterns and duty cycles
- Request quotes from vendors for your specific workload shape and volume
This worksheet is a starting point for TCO discussions, not a substitute for measured data from your environment.
Snowflake's own cost examples commonly use:
- $2.00 per credit
- $23 per TB-month storage Both appear in Snowflake documentation examples.
Key cost driver: Snowflake charges for compute credits on top of underlying cloud infrastructure. This is a license markup that doesn't exist in open-source alternatives.
Credit burn by warehouse size is documented (e.g., Large = 8 credits/hr, 2X-Large = 32 credits/hr).
Assume:
- BI / ad-hoc: 2X-Large (32 credits/hr), 2 clusters, 12 h/day = 32 × 2 × 12 × 30 = 23,040 credits/month
- ELT / feature builds: Large (8 credits/hr), 8 h/day = 8 × 8 × 30 = 1,920 credits/month
Snowflake compute subtotal
- Total credits: 23,040 + 1,920 = 24,960 credits/month
- Spend: 24,960 × $2.00 = $49,920/month (~$50k/month in license fees)
Snowflake storage subtotal
- 20 TB × $23/TB-month = $460/month
Snowflake subtotal (compute + storage): ~$50,380/month
- Of which ~$49,920 is license/credit costs
Notes readers will challenge (fairly):
- Auto-suspend, serverless features, Snowpipe, clustering, and “always-on” warehouses can materially change totals.
- Many orgs negotiate different effective $/credit.
This varies dramatically by HA posture, scale, and managed-vs-self-hosted choices.
- RDS Postgres (db.r6g.2xlarge Multi-AZ, 20TB storage): ~$1,200/month compute +
$2,300/month storage = **$3,500/month** - MSK (Managed Kafka) (3 brokers, kafka.m5.large): ~$1,800/month
- S3 staging storage (20TB + versioning for intermediate data): ~$500/month
- Orchestration (Managed Airflow/Prefect/Dagster): ~$500–$2,000/month
- Subtotal: ~$6.3k–$7.8k/month
- EC2 for Postgres HA (2x r6g.2xlarge + 20TB EBS gp3): ~$1,400/month
- EC2 for Kafka cluster (3x m5.large + EBS): ~$600/month
- S3 staging: ~$500/month
- Subtotal: ~$2.5k/month
Conservative band for comparisons: $2.5k–$8k/month
This is the portion that’s commonly glossed over and is often the real TCO driver.
- Managed ingestion/connectors, orchestration, catalog, observability, quality tooling: ~$5k–$30k/month
Not total headcount—this is the incremental labor that tends to appear when you run two platforms + pipelines:
- Data engineering (ELT/CDC, modeling, backfills, schema drift): 1.0–2.0 FTE
- Analytics engineering (semantic layer, dbt, metrics definitions, governance integration): 0.5–1.0 FTE
- Platform/SRE (Postgres HA + Kafka + orchestration + reliability): 0.5–1.0 FTE
- Data quality operations (monitoring, incident response, reconciliation): 0.25–0.5 FTE
Incremental total: ~2.25–4.5 FTE
Fully-loaded cost per FTE-month varies by geography and seniority; a realistic planning band is:
- $18k–$25k per FTE-month
So the incremental people cost attributable to dual-system + pipelines is often:
- 2.25–4.5 × $18k–$25k ⇒ ~$40k–$113k/month
Two ways to report, depending on how honest you want to be about ops reality:
- Platform bill only (cloud/vendor):
~$50.4k (Snowflake) + ($2.5k–$8k) (Postgres+Kafka) = ~$53k–$58k/month
- ** Of which ~$50k is Snowflake license costs (94% of platform bill)**
- More realistic, fully-loaded (bill + tools + incremental labor):
~$53k–$58k + ($5k–$30k tools) + ($40k–$113k labor) = ~$98k–$201k/month
- ** License costs: ~$50k/month (51% of low end, 25% of high end)**
Databricks cost is harder to model from a single "credits" number because it varies by:
- cluster types (interactive vs jobs vs serverless),
- DBUs (Databricks Units) ( compute units),
- and the underlying cloud VMs.
Key cost driver: Databricks charges DBU markups on top of underlying cloud compute. Like Snowflake, this is a ** license layer** that adds 50-150% markup over raw cloud costs.
Databricks provides product pricing and calculators, but the exact $/month depends on how clusters are configured and kept running.
If you hold the same "interactive + batch" workload shape as above, it is common to see:
- Platform bill only: ~$45k–$75k/month (high variance)
- ** Estimated ~$30k–$50k is Databricks DBU license costs (67-75% of platform bill)**
- Fully-loaded (bill + tools + incremental labor):
Add similar pipeline tax bands as (A) ⇒ ~$90k–$200k/month
- ** license costs: ~$30k–$50k/month (33-56% of total)**
If you want this section to be harder to argue with, replace the band with a concrete calculator output from your intended cluster topology and duty cycle.
This section intentionally uses list-price mechanics to show why Oracle licensing commonly dominates TCO at scale. In real enterprises, discounting can be substantial, but the shape of the math remains.
- Oracle's Processor Core Factor Table is how many shops compute required processor licenses; for many Intel/AMD server CPUs, core factor is commonly 0.5.
Reuse Snowflake math from (A): ~$50.4k/month (compute + storage)
Keeping your original “Oracle software dominates” intent:
- Oracle software (amortized license + support): very often six figures/month (environment-dependent)
- Snowflake: ~$50k/month
- Pipeline tax (tools + people): often $40k–$140k/month
This is why many enterprises experience “TCO runaway” even when individual components are tuned.
This section updates the earlier sizing and pricing so it matches the stated assumption:
- 20 TB disks per node
- EBS-backed storage
- Cheapest EC2 instance matching i4i.2xlarge CPU+RAM when using EBS
Critical difference: This stack uses entirely open-source software (Apache Cassandra, Apache Spark, Apache Kafka, Presto). You pay only for the underlying cloud infrastructure (compute + storage).
- Raw required: 20 TB logical × RF=3 = 60 TB raw
- With 30% headroom: ~78 TB raw
With 20 TB/node, capacity-driven minimum is:
- 78 / 20 = 3.9 ⇒ 4 data nodes (tight)
- More operationally realistic: 6 data nodes (more streaming/repair slack, better failure domain tolerance)
Using on-demand us-east-1 pricing:
- r6g.2xlarge (8 vCPU, 64 GiB): $0.4032/hr, $294.34/month
Amazon EBS gp3 list pricing (us-east-1 example on AWS docs):
- $0.08/GB-month
- 3,000 IOPS and 125 MB/s included, with add-on pricing above that
Per-node storage cost:
- 20 TB = 20 × 1024 = 20,480 GB
- 20,480 × $0.08 = $1,638.40 per node-month
- Data nodes:
- Compute: 6 × $294.34 = $1,766.04
- Storage: 6 × $1,638.40 = $9,830.40
- Subtotal: $11,596.44/month
- Service nodes + Spark (as above): ~$800–$1,200/month
Infra total (realistic posture): ~$12.4k–$12.8k/month
A unified stack still requires good operators, but it avoids most dual-system pipeline overhead.
Typical incremental labor bands:
- Platform/SRE for the unified platform: 0.75–1.5 FTE
- Analytics engineering (semantic layer, governance integration): 0.25–0.75 FTE
Total: ~1.0–2.25 FTE ⇒ ~$18k–$56k/month (using the same $18k–$25k per FTE-month band)
Tooling/SaaS tends to be lower (fewer connectors + less reconciliation):
- ~$2k–$10k/month
The unified HTAP approach provides additional value for AI/ML workloads:
- Real-time feature stores: No ETL lag between OLTP writes and ML feature availability (eliminates feature staleness)
- Vector similarity search: Native support without separate vector database licensing or data sync
- Agentic AI data access: Single governance/security layer for all data modalities (OLTP, OLAP, vectors)
- Reduced training data staleness: Analytics/ML models train on fresh data without pipeline delays
- Simplified MLOps: Fewer data copies to version, validate, and reconcile
These capabilities avoid costs that would otherwise appear as:
- Separate vector database licenses (e.g., Pinecone, Weaviate: $500–$5k+/month)
- Feature store platforms (e.g., Tecton, Feast managed: $2k–$10k+/month)
- Additional data engineering labor for feature pipelines and reconciliation
While the unified HTAP stack reduces architectural complexity, enterprises should budget for:
- Training/upskilling: Cassandra operations, Accord transactions, Spark bulk I/O patterns (~$20k–$50k for team training)
- Migration tooling: Schema conversion, data migration from legacy systems (varies widely by source system complexity)
- Runbook development: Failure scenarios, repair procedures, upgrade workflows (~$10k–$30k consulting/documentation)
- Fewer moving parts: No separate ETL orchestration, fewer connectors to maintain
- Monitoring consolidation: Single platform observability vs multi-system correlation (reduces tool sprawl)
The labor bands in Section D.5 reflect steady-state operations after initial ramp-up (typically 3–6 months).
Note: This is a POC stack (see README). Production deployments should budget for additional operational tooling (observability, backup/restore automation, DR orchestration) and validate SQL feature coverage for specific workloads.
- Platform bill only (cloud): ~$12.4k–$12.8k/month
- No license costs — 100% open source
- All costs are raw cloud infrastructure (compute + storage)
- Fully-loaded (bill + tools + labor): ~$32k–$79k/month
This section mirrors (D), but replaces monthly cloud bills with a one-time hardware purchase and (optionally) an amortized monthly equivalent for apples-to-apples comparison.
This is a BOM-style estimate. On-prem pricing varies widely by vendor, discount level, spares posture, and whether you already have rack/switching.
- Raw required: 20 TB logical × RF=3 = 45 TB raw + head-room Assume ~20 TB usable NVMe per data node, implemented as:
- 3 × 7.68 TB NVMe U.2 (PCIe Gen4) striped (≈ 23.04 TB raw), leaving room for filesystem + operational slack
Procurement posture:
- 6 data nodes + 2 service nodes = 8 servers
Servers (compute/RAM baseline per node) Use a 1U single-socket server class with ≥8 cores and 64 GB RAM. As a concrete reference point, the HPE Store (US) lists an HPE ProLiant DL325 Gen11 Smart Choice configuration "starting at" $5,339 (reseller-indicative pricing). For budgeting, use:
- $5,300–$7,500 per server (depends on CPU, NICs, rails, PSU redundancy, support level)
7.68 TB enterprise NVMe U.2 (PCIe Gen4)
Representative “new/in-stock” listing example: Samsung PM9A3 7.68TB at $2,000.
Top-of-rack switching (25GbE example) A representative 48×25GbE data-center switch price example: $10,732 (discounted "our price" listing for an Arista 7050X3 configuration). Budget:
- $6k–$15k depending on brand/new-vs-used/support
NICs + optics/cabling + rack/PDU (typical adders) These vary a lot by standards and vendor. Budget placeholders (edit to your standards):
- $300–$900 per server (25GbE NIC + optics/cables allocation)
- $1,500–$4,000 (rack accessories / PDU allocation if needed)
The tables below use point estimates for clarity:
- Servers: $5,339 each
- 7.68 TB NVMe: $2,000
- Switch: $10,732
- NIC/optics/cables: $500 per server (placeholder)
- Rack/PDU allocation: $2,000 (placeholder)
- Contingency/spares: 10% (recommended for spares + shipping + variance)
| Line item | Qty | Unit | Extended |
|---|---|---|---|
| Servers (≥8 cores, 64 GB baseline) | 8 | $5,339 | $42,712 |
| 7.68 TB NVMe U.2 Gen4 (data, 3 per data node) | 18 | $2,000 | $36,000 |
| 1.92 TB NVMe U.2 Gen4 (commit log, optional but recommended) | 6 | $750 | $4,500 |
| 25GbE ToR switch | 1 | $10,732 | $10,732 |
| NICs/optics/cables (allocation) | 8 | $500 | $4,000 |
| Rack/PDU/accessories (allocation) | 1 | $2,000 | $2,000 |
| Subtotal | $99,944 | ||
| Contingency + spares (10%) | $9,994 | ||
| One-off CapEx total | ~$109,938 |
This is not a cash cost—just a way to compare against monthly cloud spend.
| One-off CapEx | 36-mo amortization | 60-mo amortization |
|---|---|---|
| ~$109.9k | ~$3.05k/mo | ~$1.83k/mo |
Because "fully-loaded" depends mostly on org structure and labor accounting, the cleanest comparison is platform bill only (compute + storage + baseline infra).
Unified HTAP platform bill: ~$12.4k–$12.8k/month
- Snowflake license costs eliminated: ~$50k/month
- Remaining infrastructure comparison:
- Traditional: ~$3k–$8k (Postgres + Kafka + pipeline infra)
- HTAP: ~$12.4k–$12.8k (unified infrastructure)
- Net platform savings: ~$40k–$45k/month (76-78%)
The HTAP stack replaces three separate systems (OLTP + OLAP + pipeline) with one unified platform that:
- Handles both transactional and analytical workloads
- Provides native Spark/Presto analytics (no Snowflake needed)
- Includes built-in CDC and streaming (no separate pipeline tools)
- Supports vector search and AI/ML workloads
The key insight: You're comparing ~$12.8k of open-source infrastructure against ~$50k of Snowflake license fees alone.
- Traditional: ~$98k–$201k/month
- HTAP: ~$32k–$79k/month
- Savings: ~$66k–$122k/month (61-67%)
Unified HTAP platform bill: ~$12.4k–$12.8k/month
- Databricks DBU license costs eliminated: ~$30k–$50k/month
- Remaining infrastructure comparison:
- Traditional: ~$15k–$25k (Postgres + Kafka + pipeline + base compute)
- HTAP: ~$12.4k–$12.8k (unified infrastructure)
- Net platform savings: ~$32k–$62k/month (72-83%)
- Traditional: ~$90k–$200k/month
- HTAP: ~$32k–$79k/month
- Savings: ~$58k–$121k/month (64-74%)
Savings are typically very large because the Oracle software envelope dominates at scale.
Key insight: Oracle license costs alone often exceed $100k/month, plus Snowflake's ~$50k/month. The HTAP stack eliminates both license layers.
- AI/ML capabilities: Eliminates $2.5k–$15k+/month in separate vector DB and feature store costs
- Reduced time-to-insight: No ETL lag means faster business decisions (opportunity cost)
- Simplified governance: Single security/audit layer vs federated policies across multiple systems
%%{init: {'theme':'base'}}%%
graph LR
SF["<b>Snowflake Stack</b><br/>$98k-$201k/mo<br/><br/> Platform: $53-58k<br/>(~$50k license costs)<br/> Tools: $5-30k<br/>Labor: $40-113k"]
HTAP["<b>Unified HTAP</b><br/>$32k-$79k/mo<br/><br/> Platform: $12.4-12.8k<br/>(No license costs)<br/> Tools: $2-10k<br/>Labor: $18-56k"]
DB["<b>Databricks Stack</b><br/>$90k-$200k/mo<br/><br/> Platform: $45-75k<br/>(~$30-50k license costs)<br/> Tools: $5-30k<br/>Labor: $40-113k"]
SF -.->|"84.1% savings"| HTAP
DB -.->|"84.0% savings"| HTAP
style SF fill:#ffcccc,stroke:#666,stroke-width:2px
style HTAP fill:#ccffcc,stroke:#666,stroke-width:2px
style DB fill:#ffd4cc,stroke:#666,stroke-width:2px
If you want this page to survive critical readers, expect these pushbacks:
-
"These Snowflake hours are arbitrary." True. They're placeholders. The doc explicitly calls out that it's a duty-cycle assumption and invites readers to swap in their own duty cycle. Use Snowflake's calculator with your actual query patterns.
-
"You ignored discounts / commitments." Also true. We keep on-demand for comparability, but committed-use discounts apply to both approaches and can be layered on later. Enterprise discounts often favor incumbents (Snowflake/Databricks) but also apply to cloud infrastructure (AWS/GCP/Azure).
-
"Labor estimates are hand-wavy." They are, but leaving them out is worse. The fix is to present them as bands and make the underlying role assumptions explicit (this doc does that). The "pipeline tax" (dual-system labor overhead) is the real TCO driver in most enterprises.
-
"EBS cost dominates; why not instance-store?" Correct: with 20 TB/node, gp3 storage becomes the main infra cost line. If you want to model instance-store (i4i/i3en), you need a different node shape (and different operational tradeoffs around durability and repair).
-
"This is a POC, not production-ready." True (see README line 295). The TCO model assumes production-hardened deployment. Enterprises evaluating this stack should:
- Budget for additional operational tooling (observability, backup/restore automation, DR orchestration)
- Validate SQL feature coverage for their specific workloads (see README lines 386-391 on Postgres compatibility)
- Plan for DR drills and failure injection testing (README lines 475-482)
- Consider managed service options if/when available (not modeled here)
- Account for initial training/migration investment (see Section D.7)
PRs that replace any band with measured numbers (or calculator outputs) will improve the credibility of this document substantially.