ReasonOS

Removing Ops from DevOps

Stop juggling 1000s of engineering tools that don't work together and take forever to set up. ReasonOS provides one unified environment that consolidates monitoring, CI/CD, security, servers, databases, scaling and etc. into a single AI operating system that works across any cloud

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BEFORE: Tool Sprawl
Complexity Level
⚠️ CHAOS
MON
CI/CD
SEC
>
LOGS
DEPLOY
main.tf
resource
"aws_"
SCALE
▲ 3→5
AFTER: One Platform
REASONOS
● CONNECTED
Monitor
Deploy
Scale
Secure
Overview
HEALTHY
DEPLOYING
SCALING
→ All systems unified
→ 18 tools consolidated
Works across all clouds
aws
$ aws synced
azure
$ azure synced
gcloud
$ gcloud synced
18+ tools → 1 OS
Multi-cloud + One DSL
Unified workspace
Zero context switching

Trusted by engineering teams consolidating their workspace

Products

The Operating System for
Intelligent Engineering + DevOps Automation

We manage your entire stack – single or multi-region – beyond basic servers and databases. Seamless synchronization, no complex Terraform required. Deploy with confidence, at any scale

ReasonOS for Cloud Development

Handle any tool on any cloud with zero setup time, enjoying a perfect, compliant, ready-to-code environment instantly

Start building →Read our blog →
infrastructure.rbi
# infrastructure.rbi - ONE file manages ALL clouds
native.global_infra_config(
provider="aws", region="us-east-1")
provider="google", project="my-project")
provider="azurerm", subscription_id="...")

# Single rule deploys to ALL clouds simultaneously
kubernetes(
providers=["aws", "google", "azurerm"],
node_count=100, # 100 nodes per cloud = 300 total
machine_type="gpu_large"
)

# AUTO: deploy, test, monitor, scale
py_binary(
name = "LLM_MODEL",
srcs = ["train.py"],
auto_deploy = True,
auto_test = True,
auto_monitor = True,
auto_scale = True,
)

# BUILD.rbs - Distribute training across ALL clouds
batch_job(
name = "global_training",
binary = [:LLM_MODEL],
num_workers = 300, # Distributed across 3 clouds automatically
k8s_image = "tensorflow/tensorflow:latest-gpu"
)

# Automatic global load balancing & failover
service_mesh(
clusters=[aws.us-east-1, gcp.us-central1, azure.eastus],
traffic_split="auto", # AI-optimized routing
failover=True
)

# End of infrastructure.rbi


AWS
100 nodes
us-east-1
Google
100 nodes
my-project
Azure
100 nodes
gpu_large
✓ 300 nodes across 3 clouds provisioned

Give Your AI Perfect Memory

ReasonOS understands your entire system—every tool, every line of code— feeding perfect memory to AI agents, giving better, more reliable, and safer automation

See AI in action →
UserService.kt
class UserService {
fun deployToCloud() {
val config = loadConfig()
ReasonOS.deploy(config)
}
}
data class ApiConfig(
val endpoint: String,
val timeout: Duration
)
ReasonOS.autoConfig()
interface UserRepository {
suspend fun findAll(): List<User>
suspend fun save(user: User)
}
ReasonOS.inject()
fun String.toCloudUrl(): URL {
return URL(
ReasonOS.cloudBaseUrl + this
)
}
|
[INFO] ReasonOS Context Engine initialized
[SCAN] Analyzing UserService class...
[LOAD] Loading deployment configuration...
[EXEC] ReasonOS.deploy(config) executing...
[INFO] Processing ApiConfig data class...
[SCAN] Detecting endpoint and timeout params...
[AUTO] Auto-configuring cloud settings...
[EXEC] ReasonOS.autoConfig() complete ✓
[INFO] Analyzing UserRepository interface...
[SCAN] Found suspend functions for async ops...
[INJECT] Preparing dependency injection...
[EXEC] ReasonOS.inject() ready for use ✓
[INFO] Processing String extension function...
[SCAN] Building cloud URL transformation...
[LOAD] Connecting to ReasonOS.cloudBaseUrl...
[ACTIVE] Real-time context sync active ✨
🔍 Live Context
🤖 Auto-sourcing
📦 Build Files
✓ Context Active

Augmenting and Enhancing DevOps

ReasonOS's AI agents handle complex tasks seamlessly, automating the entire project lifecycle—from setup to deployment

See automation →
ReasonOS DevOps Assistant
AI-Powered
Help me optimize this distributed training job for better GPU utilization
ReasonOSis analyzing
I see inefficient GPU memory usage. I'll redistribute your model across more nodes and optimize batch sizes per GPU.
Can you handle the multi-cloud scaling automatically?
✅ 300 GPUs allocated across clouds
✅ Dynamic batch size optimization
✅ Training speed increased 3.2x
🔍 GPU Analysis
🚀 3.2x Faster
🌐 300 GPUs
✓ Training Optimized
Customer Stories

Trusted by engineering teams
building the future

"ReasonOS transformed our DevOps chaos into elegant simplicity. One .rbs file now manages our entire multi-cloud infrastructure - what used to take weeks of coordination across teams now happens in minutes."

Highlighted ReasonOS features

Multi-cloud automationDSL-driven deploymentTool consolidation

Use case

Construction ERP platform

"Before ReasonOS, we had 15+ DevOps tools and constant configuration drift across AWS, GCP, and Azure. Now everything is unified in one platform with perfect consistency. Our deployment confidence went from 60% to 99%."

Highlighted ReasonOS features

Cross-cloud consistencyConfiguration managementAutomated scaling

Use case

Sleep health monitoring platform

"The AI-powered context engine is revolutionary. ReasonOS understands our infrastructure intent and suggests optimizations we never considered. It's like having a senior architect reviewing every deployment."

Highlighted ReasonOS features

AI-powered insightsSmart optimizationIntelligent automation

Use case

Research computing

Explore ReasonOS today

Talk to one of our experts →Read our blog →