Cloud Portability

Run Natively on

Without a Rewrite

Your stack
Google Cloud
Amazon EKS
GKE
Amazon RDS
Cloud SQL
Amazon S3
Cloud Storage
Amazon SQS
Pub/Sub
Your App
Runs Natively On
Your code 0 changes
s3.putObject(bucket, key, data)
sqs.sendMessage(queue, msg)
rds.query("SELECT ...")
service adapters
Runs natively on
Azure
Tuned for Azure
Throughput
Latency
Consistency
drawing on past projects
Amazon S3Cloud SQLCosmos DBObject StorageAmazon RDSPub/SubBlob StorageAutonomous DBAmazon DynamoDBGKEAKSOKEAmazon SQSFirestoreService BusAmazon S3Cloud SQLCosmos DBObject StorageAmazon RDSPub/SubBlob StorageAutonomous DBAmazon DynamoDBGKEAKSOKEAmazon SQSFirestoreService Bus
NoSQL DatabaseAmazon EKSCloud StorageAzure FunctionsStreamingAWS LambdaCloud RunEvent HubsOCI QueueAmazon ElastiCacheMemorystoreAzure CacheContainer InstancesAmazon MSKBigQueryNoSQL DatabaseAmazon EKSCloud StorageAzure FunctionsStreamingAWS LambdaCloud RunEvent HubsOCI QueueAmazon ElastiCacheMemorystoreAzure CacheContainer InstancesAmazon MSKBigQuery

A separate port and codebase per cloud

Rewrite to a generic abstraction to stay portable

Locked to one provider, or months of porting to leave

Multiple cloud-specific versions to patch

Compromised architecture to stay portable

Tensor9 is a software adaptation platform. We run your existing stack natively on any cloud, whether you sell software, run your own workloads, or migrate them for a client. We compile your software for each cloud target and translate the managed services it depends on, so you never maintain a separate codebase per cloud.

Yes. Cloud portability applies whether you deliver software to customers or run your own workloads. The same approach lets an enterprise take an application built for one cloud and run it natively on another, without a rewrite, which is common after an acquisition, under a mandate to avoid a single provider, or when a region requires a specific cloud.

Yes. Tensor9 adapts each application to run natively on the destination cloud and translates the managed services it depends on, so a migration lands in weeks and the same approach repeats across clients, instead of a bespoke project each time.

AWS, Azure, GCP, Oracle Cloud, and sovereign or regional clouds. Adding a cloud is a new compile target, not a new project.

No. Tensor9 adapts your existing stack to run natively on the target cloud and translates the managed services it uses into local equivalents, so you deploy without maintaining separate codebases.

Tensor9 maps each one to the target cloud's equivalent automatically. You keep RDS, SQS, S3, and the rest of the managed services you build on, instead of replacing them with a generic abstraction. We match each service on the behavior your code relies on, and where a target's equivalent differs in consistency, ordering, or throughput, we reconcile it in the translation layer or surface the difference before you deploy.

Close, and we are explicit where they are not. Managed services differ in details like delivery guarantees, consistency, and limits. Tensor9 preserves the behavior your application depends on and gives you a written diff of anything that changes, so your team reviews it up front instead of finding it at runtime.

No. An abstraction forces your software into a common shape and asks you to give up the native services underneath. Tensor9 does the opposite: it adapts your software as it is to run natively on each cloud, using that cloud's own services.

For most teams we analyze and compile your existing stack in one to two weeks, and your first deployment on a new cloud can be live shortly after. There is no re-architecture and no separate platform team to hire.

Yes. Running natively on a customer's cloud lets you transact through that cloud's marketplace against their committed spend, which turns each cloud into a channel you can sell through.

Ready to Run Your Anywhere?