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- HealthcareComing soon
- ManufacturingComing soon
- Retail & E-CommerceComing soon
SAP HANA Cloud
Your tables, embeddings and supplier graph live in three databases, with jobs copying between them. Every copy is one more thing to keep in step — SAP HANA Cloud runs relational, graph, vector, spatial and JSON data in one managed engine — the database SAP now places under Business Data Cloud for AI on SAP data.
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How it’s rated
Full scoreboard ↓Quick answer
The SAP platform family
This page covers SAP HANA Cloud — SAP’s managed, multi-model database service. The rest:
Quick facts
30-second orientation- Product
- SAP’s managed, multi-model database service for applications and analytics across enterprise data
- Maker
- SAP SE, Walldorf, Germany; CEO Christian Klein
- Data models
- Relational, graph, vector, spatial and JSON, all served by a single engine
- Scaling
- Compute and storage scale independently of each other, in SAP’s own words
- AI role
- Positioned as the AI database for SAP Business Data Cloud, which SAP launched in February 2025
- In-database AI
- Vector search, knowledge-graph queries and machine learning run inside the database
- Price
- Metered in SAP capacity units; SAP quotes the unit count and the term
- Not to confuse
- On-premise SAP HANA, SAP ASE and SAP IQ are separate SAP products
- India
- Not documented for HANA Cloud, though SAP BTP runs a Mumbai region on Google Cloud
- In India via
- TechBag — workload sizing, SAP quote itemised in INR with GST, rival shortlist
Understand multi-model cloud databases before you buy one
Most product pages skip this. We start here — so you buy a capability, not a buzzword.
What is a multi-model cloud database?
One managed database that stores tables, graphs, vectors, maps and JSON in a single engine.
A separate store per data model vs SAP HANA Cloud — the honest table
What consolidation actually replaces, dimension by dimension.
| Dimension | A separate store per data model | SAP HANA Cloud |
|---|---|---|
| Data models | A separate store for graphs, vectors and maps | Five models handled by one engine |
| AI retrieval | Nightly export into a vector database | Vector search beside the live rows |
| Sizing | Compute and storage bought as one block | Compute and storage scaled apart |
| Machine learning | Data shipped out to an ML platform | Models run inside the database |
| Copy jobs | One sync pipeline per extra store | Fewer copies to keep in step |
| What it is NOT | — | On-prem SAP HANA, a printed price, or an India region |
The cheapest test is one use case: load a sample of SAP data, run vector and relational queries together, and count the exports it could retire.
The five pieces of the platform
Vendors love diagrams; buyers need to know what they’re actually operating. Here’s the whole platform, demystified.
Engine
Relational tables sit beside graph, vector, spatial and JSON data in a single engine, so one query can join a customer record to its embeddings without a second database.
Tiers
SAP’s pricing material lists in-memory storage, a disk-based native storage extension and a relational data lake, so older records need not occupy expensive memory.
Scaling
SAP says compute and storage scale independently. Usage is metered in capacity units, so how far each one is turned up feeds straight into the quote SAP prepares.
Platform
SAP’s catalogue files HANA Cloud with Business Data Cloud, Datasphere and Analytics Cloud, and pitches it as the database where BDC’s AI work on SAP data runs.
One engine for five data models — memory and cheaper tiers underneath, metered in capacity units, run by SAP.
Six capabilities. Store, query, build.
SAP HANA Cloud is SAP’s managed database: one engine for five data models, built for AI on SAP data.
Five models, one engine
Relational, graph, vector, spatial and JSON data share one engine, so a new kind of data does not bring a new database and copy job.
Memory only where it pays
Hot data in memory, colder data on SAP’s disk-based storage extension or in a relational data lake, as SAP’s pricing page describes.
Vector search in the database
Embeddings are stored and searched where the business rows live, so an assistant’s retrieval reads current data, not a nightly export.
Knowledge-graph queries
Graph queries run inside the same database as the tables, so links between SAP records can be traversed without moving them anywhere.
Models run beside the data
SAP says machine learning runs directly inside the database, so a model can score records without shipping them to another platform.
A pretrained model for tables
SAP describes SAP-RPT-1 as a relational pretrained transformer model: AI built for rows and columns of business data, not free text.
Watch SAP HANA Cloud in action
SAP’s Q1 2026 release highlights and two expert deep dives on the Q4 2025 and Q2 2026 releases, all from SAP’s official channel.
Top New Features in SAP HANA Cloud | Q1 2026 Release Highlights
SAP’s short round-up of what the first quarterly release of 2026 added to HANA Cloud.
What’s New in SAP HANA Cloud | Deep Dive with Product Experts | Q2 2026
SAP product experts walk through the Q2 2026 release in more depth than the highlights reel.
What's New in SAP HANA Cloud | Deep Dive with Product Experts | Q4 2025
The same expert format for the Q4 2025 release, useful for seeing how features build quarter by quarter.
Want a live, India-context walkthrough for your environment?
Book a guided demo →Each new kind of data usually brings a new database. SAP HANA Cloud keeps five of them in one engine.
Here’s what genuinely sets it apart — and exactly where it stops.
One engine instead of a store per data model
Estates often add a database for each new kind of data: a graph store, a vector store, a spatial add-on. SAP’s pitch for HANA Cloud runs the other way: five data models in one engine, compute and storage sized separately, and fewer copies to keep in step.
Built to sit under SAP’s data and AI line
SAP calls HANA Cloud the AI database for SAP Business Data Cloud, launched with SAP Databricks in February 2025. Where finance, supply chain and HR records already live in SAP, vector and graph work sits next to the data SAP’s Joule agents are grounded on.
AI inside the database, not beside it
Vector search, knowledge-graph queries and machine learning run inside the database, and SAP adds SAP-RPT-1, a relational pretrained transformer for tabular data. Retrieval reads current rows, not last night’s export, and fewer copies of business data spread to other platforms.
Where it stops
TechBag could confirm no SAP price, so budgets wait on a capacity-unit quote. No India region is documented for HANA Cloud, and no analyst placement is verified. Outside an SAP landscape it is a proprietary engine with a smaller hiring pool than Postgres.
The numbers behind the platform
What your SAP HANA Cloud rollout looks like
Name the first workload
Pick one job — a vector-backed assistant, a supplier graph or analytics on ERP data — and list the SAP sources it reads.
Size capacity units with SAP
Bring data volumes, memory needs and query counts so SAP can quote capacity units, and ask which data can sit on cheaper tiers.
Settle region and access
Get the hosting region in writing, as none in India is documented, then set up users, network paths and source connections.
Run it on real data
Load a sample, run vector, graph and relational queries side by side, and compare results and speed with today’s setup.
Retire one copy job
Move one consumer onto HANA Cloud, switch off the export it replaced, and track the capacity units used each month.
The review scoreboard
Modelled on Gartner Peer Insights structure. *Counts and breakdowns are illustrative pending verified review collection.
Quick poll — what’s driving your evaluation?
Talk to an advisor“We dropped a separate graph database for supplier links; the same HANA Cloud instance now answers the network questions.”
“Our assistant’s retrieval reads embeddings stored next to live order rows, so it stopped quoting last week’s stock levels.”
“Sizing capacity units took three rounds with SAP. Bring real data volumes and query counts to the very first call.”
“Spatial queries on delivery addresses run in the engine that holds billing, which removed one nightly sync job for us.”
“Moving old ledger history off memory onto the disk tier kept our footprint, and the capacity bill, under control.”
“Finding engineers who know HANA took longer than hiring for Postgres. Budget for training before you commit the team.”
Where everyone sits — the grids
Analyst firms bury this view behind paywalls, and G2 retired its Grid. So here’s TechBag’s synthesis of the cloud database market — tap any vendor to see why it sits where it does.
TechBag Cloud Database Grid
Execution strength vs product vision — the classic market map, minus the paywall.
AI database for SAP Business Data Cloud; no analyst placement verified.
Deployment Choice × Data-Model Breadth
The grid nobody publishes — how many places the database can run and keep data, India included, vs how many data models one engine serves.
Five data models; an SAP-run service with no India region documented.
Positions are TechBag’s illustrative synthesis of public review-platform data and vendor documentation — not a reproduction of any analyst graphic. Verify before relying on it.
SAP HANA Cloud vs the cloud database field
Set beside IBM Db2, MongoDB Atlas, EDB Postgres AI, Elasticsearch and MongoDB Atlas Vector Search — on data models, AI, price, scale, support, exit and India.
| Dimension | SAP HANA Cloud | IBM Db2 | MongoDB Atlas | EDB Postgres AI | Elasticsearch | MongoDB Atlas Vector Search |
|---|---|---|---|---|---|---|
| What it is | SAP’s multi-model DBaaS | IBM’s relational engine | Managed document DBaaS | Multi-model Postgres | Search + vector engine | Vectors inside Atlas |
| Deployment | SAP-run service only | Own kit, IBM or AWS | Managed on 3 clouds | On-prem or any cloud | Self-run or cloud | Atlas only |
| Data models | 5 models, one engine | Relational SQL | Documents + search | Rows, analytics, vectors | Indexed JSON documents | Vectors on documents |
| Vector and AI | Vector, graph, ML in DB | Not verified | Vector Search, Voyage AI | pgvector and agents | semantic_text, ELSER | ANN, ENN, reranking |
| Pricing model | Capacity units | Caps; SaaS by the hour | Usage-based | Quoted subscription | Free engine, paid tiers | Inside Atlas usage |
| Published entry price | No figure printed | $630/month on SaaS | Free M0 tier | Quote | Hosted from $99/month | Within Atlas spend |
| Included vs add-on | AI features in the DB | Edition sets the ceiling | Platform features in | One platform, 3 loads | ML search is paid | Voyage models native |
| Scale and storage | Compute, storage apart | SaaS to 128 vCPU | Auto-scaling clusters | Petabyte analytics | Shards; BBQ by default | Validate at scale |
| Integrations | SAP data line | AWS RDS, SAP estates | Cloud marketplaces | Postgres ecosystem | Inference endpoints | MongoDB query language |
| Analyst standing | None verified | None cited | None cited | None cited | Forrester Leader | None cited |
| India storage region | Not documented | Yours; SaaS unlisted | Indian cloud regions | Wherever you deploy | Mumbai and Pune | Atlas Indian regions |
| Support | SAP-run service | Community or quoted | Ops handled by MongoDB | From EDB itself | Paid tiers add support | Atlas operations |
| Lock-in and exit | Proprietary engine | Db2-specific procedures | Portable across clouds | Open Postgres underneath | AGPL, but forks differ | Tied to Atlas |
| Best fit | AI on SAP data | Existing Db2 estates | Managed document apps | Postgres-first estates | Text, vectors, analytics | RAG on MongoDB data |
Which approach fits you?
Honest fit signals — because the fastest way to lose your trust is to pretend one product wins every scenario.
Choose SAP HANA Cloud if…
- ✓The data your AI and analytics need already lives in SAP Cloud ERP, SuccessFactors or Ariba
- ✓You want tables, graphs, vectors and spatial data in one engine rather than a store for each
- ✓You are adopting SAP Business Data Cloud and want the database SAP positions beneath it
Compare alternatives if…
- ✓You need a price before the first call — Db2 SaaS and Elastic Cloud Hosted both publish one
- ✓India storage is a hard requirement — MongoDB Atlas and Elastic Cloud document Indian regions
- ✓Your engineers are Postgres people — EDB Postgres AI keeps vectors and analytics on open Postgres
Do not expect…
- ✓A HANA Cloud price on this page: SAP’s capacity-unit quote decides it
- ✓The on-premise SAP HANA platform — that is a separate SAP product
- ✓A verified analyst placement, or a documented India region for HANA Cloud
SAP HANA Cloud is one of 25 databases & data tools products TechBag carries. The Databases & Data Tools guide narrows them to a shortlist and shows the reasoning. →
What does keeping data copies in step cost you?
Drag the sliders (copy jobs moving data between separate stores; data-engineer hour cost). Estimates model time spent maintaining extracts, sync pipelines and reconciling copies at an assumed 1.5 hours per job a year, with 70% of it removed when tables, vectors and graphs are queried in one engine. Both figures are assumptions. Illustrative.
Loaded cost = salary + overheads per productive hour. Illustrative only — your TechBag quote models your actual environment and modules.
Three ways to consume it
TechBag prints no SAP HANA Cloud price: SAP meters the service in capacity units and renders its rates in the browser, and none could be confirmed. SAP quotes the unit count and term for your memory, storage and compute; SAP Business Data Cloud, which HANA Cloud serves as AI database, is a separate product. TechBag sizes the first workload, then itemises SAP’s quote in INR with GST beside rival quotes.
SAP HANA Cloud
Best for AI and analytics on SAP data
- Metered in SAP capacity units
- Unit count and term quoted by SAP
- Storage region to confirm in writing
+ Platform add-ons
Best for a broader rollout
- Scoped to your estate
- Add-on modules as needed
- Phased, right-sized deployment
HANA Cloud with Business Data Cloud
Best when SAP data feeds AI agents
- Business Data Cloud quoted on its own scope
- HANA Cloud as BDC’s AI database
- Datasphere and Analytics Cloud alongside
Buy it for less — TechBag pricing beats list
Whatever the list prices above, TechBag negotiates a significantly better deal — with GST-compliant INR invoicing and local support. Ask us for your discounted quote.
Get an India-ready quote
Tell us your requirements and current tools — we’ll model it against what you spend today.
The 8 questions to ask every vendor
Take this into your next vendor call — including ours.
Which first use case justifies it: AI retrieval, graph analysis, spatial queries or analytics on SAP data?
How much of the data already sits in SAP systems, and how much would have to be loaded from elsewhere?
Has SAP quoted the capacity units for your memory, storage and compute needs, and the subscription term?
Which tables must stay in memory, and which can move to the disk extension or the data lake tier?
Which region will host your instance? No India region is documented for HANA Cloud, so get it in writing.
Who on your team knows HANA SQL and its graph and vector features, and what will training cost?
If you leave, which multi-model features would need rebuilding elsewhere, and how would the data be exported?
Does the quote itemise capacity units, term and any related SAP products, in INR with GST?
Questions buyers ask
Ready to evaluate SAP HANA Cloud?
Count the copy jobs your data estate runs today, or let a TechBag advisor scope a first workload and get SAP’s capacity-unit quote itemised in INR.
Stats, ratings, review counts and pricing are illustrative and sourced from public materials; verify before purchase.