From cloud building blocks
to one serviced business runtime.
AWS and Google Cloud offer powerful components for almost any architecture. The trade-off is ownership: your organisation or its consultants must assemble, secure, monitor and evolve the complete operation. Datastreams provides the governed operation as one declarative runtime.
Product capabilities and packaging change. This comparison was reviewed against official vendor documentation on 6 September 2026; validate the current fit during procurement.
The short answer
Different starting points lead to different architectures.
Choose hyperscaler building blocks when a capable platform team needs maximum freedom to design bespoke infrastructure. Choose Datastreams when the business wants a complete data-in-motion operation without turning each requirement into a multi-service cloud engineering programme.
Choose AWS or Google Cloud when
- You have a mature cloud platform team and need bespoke infrastructure or services beyond the Datastreams runtime boundary.
- Independent scaling, tuning and replacement of every infrastructure component is a deliberate requirement.
- The organisation accepts provider-specific IAM, observability, billing and operational models as part of its architecture.
Choose Datastreams when
- The desired outcome can be expressed as sources, quality, state, rules, permissions, actions and destinations.
- The organisation wants a complete standard operation on one server instead of owning the integration between messaging, processing, storage and governance services.
- Deployment choice and portability matter, and data should not be stored merely because the reference stack assumes a lake or warehouse.
Decision matrix
Datastreams and AWS or Google Cloud, compared by operating need.
This is an architectural comparison, not a feature-count contest. The right choice depends on the operation the organisation must own.
What is bought
AWS or Google Cloud
Composable infrastructure services for messaging, compute, storage, analytics, IAM, monitoring and many adjacent needs.
Datastreams
A serviced runtime technology for complete declared business data operations.
Building blocks maximise freedom; an integrated runtime minimises assembly.
Typical flow
AWS or Google Cloud
AWS reference architectures combine services such as MSK or Kinesis, Lambda or Glue, Firehose, S3, Redshift, monitoring and BI. Google describes Pub/Sub with Dataflow and BigQuery, with simpler direct subscriptions for narrower cases.
Datastreams
One runtime validates, calculates, maintains required state, decides and distributes to selected endpoints.
Count the services actually required, including monitoring, recovery and security around each boundary.
Starting topology
AWS or Google Cloud
Processing is assembled across selected managed services, each with its own service boundary and operational model.
Datastreams
The complete standard data-in-motion operation can run as one runtime on one server.
Add nodes only where workload, availability or recovery requirements justify them.
Business rules
AWS or Google Cloud
Implemented in application code, SQL, functions, stream jobs, templates or several service configurations.
Datastreams
Kept as a versioned declarative model separate from event payloads and endpoint integrations.
Ask whether the business can inspect the operation without reconstructing cloud code.
Data quality
AWS or Google Cloud
Built using schemas, processing jobs, catalogues, validation code, monitoring and exception destinations selected by the team.
Datastreams
One quality contract gates every delivered output and gives exceptions an explicit path.
Cloud services provide the primitives; the customer owns the complete quality system.
Storage
AWS or Google Cloud
Many reference paths land streams in S3, BigQuery, Redshift or other stores, although both clouds also support direct or alternative patterns.
Datastreams
Storage is optional when data can be processed and delivered while moving; add only the state, history and evidence required.
Avoid paying and governing a central copy when the business outcome does not need one.
Operations
AWS or Google Cloud
The customer owns service selection, IAM, networking, quotas, observability, cost controls, upgrades and cross-service incident diagnosis.
Datastreams
Datastreams services the runtime under an explicit operational and deployment agreement.
Include people, specialist skills and cross-service change in total cost.
One runtime, one server to start
The complete operation does not require a complete technology stack.
A general cloud stack allocates resources by service boundary: message transport, processing jobs, functions, temporary or durable storage, warehouse ingestion, monitoring and networking may each meter work and retain their own operational state. Datastreams can remove intermediate transport and storage when one runtime performs the complete operation on the event. This reduces architectural surface and often the resources that accompany it, but only a like-for-like workload benchmark can quantify the saving.
A standard Datastreams deployment can run the complete declared operation on one server. That replaces several functional service boundaries; it does not mean every production topology is always one machine.
Actual compute, memory, storage and network use depend on event volume, state, rules, destinations, availability and recovery requirements. Redundancy or exceptional workloads can require additional nodes. We compare an agreed workload and topology before making a numeric saving claim.
Primary sources
Read the vendor documentation behind this comparison.
We link to product-owned documentation rather than relying on review sites or anonymous feature tables.
AWS: Modern streaming architecture patterns
Official reference patterns combining streaming, functions, processing, storage, warehouse, search and analytics services.
Open official sourceAWS: Glue streaming sources and targets
Official supported streaming inputs, processing model and data targets.
Open official sourceGoogle Cloud: Pub/Sub
Official positioning of Pub/Sub with Dataflow, BigQuery, storage and operational destinations.
Open official sourceGoogle Cloud: Dataflow streaming analytics
Official real-time ETL and integration paths, templates, custom transforms and output systems.
Open official sourceGoogle Cloud: Direct Pub/Sub to BigQuery
Official acknowledgement that a direct path can simplify architecture and billing when Dataflow transformation is unnecessary.
Open official sourceCompare the architecture around one operation you actually run.
Bring the current services, data movements, quality rules, destinations, operational work and costs. We will map the equivalent Datastreams boundary and identify what would remain.
Start an evidence-based comparison