Krutrim
FreemiumIndia's AI-first cloud platform for developers, with GPU clusters and data sovereignty.
By Tanmay Verma, Founder · Last verified 20 Jun 2026
5.4k views
Added 26d ago
72/100Safe BetIn short
Krutrim — India's AI-first cloud platform for developers, with GPU clusters and data sovereignty. Best for Indian startups and enterprises training and serving AI models locally, Developers wanting a simple cloud console with GPU access in INR, Teams migrating from AWS to a lower-cost, India-first platform. Free to start; paid plans from $24/mo.
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Editorial Verdict
Best for
Indian startups and enterprises training and serving AI models locallyDevelopers wanting a simple cloud console with GPU access in INRTeams migrating from AWS to a lower-cost, India-first platformAI inference at scale with high availability and 99.99% SLA
Not ideal for
Global enterprises needing multi-region beyond IndiaTeams requiring extensive third-party SaaS integrationsUsers seeking the widest possible service catalog (like AWS marketplace)Projects relying on niche regional regulation outside India
If you are an Indian startup or enterprise building AI models and need data sovereignty with production-grade reliability, Krutrim is a strong, cost-effective choice. Its AWS-compatible APIs ease migration, but if you require a wide global region presence or extensive third-party integrations, you may find it limiting. Consider Google Cloud or AWS for broader multi-region needs, or your own on-prem infrastructure for niche compliance beyond India.
Last verified: June 2026
Behind the Verdict
Krutrim Cloud distinguishes itself by being built specifically for the Indian market, with a focus on AI workloads. The platform's strengths include competitive pricing for GPU instances (e.g., A100 Tiny at ₹24/hr, H100 × 1 at ₹213/hr), transparent INR billing, and a unified console that simplifies managing compute, storage, and AI services. The AWS-compatible APIs and SDKs make migration from AWS relatively painless for teams already familiar with that ecosystem. The 99.99% uptime SLA and SOC 2 certifications provide confidence for enterprise adoption. However, the platform has limitations: documentation is sparse, with no detailed tutorials available as of May 2026. Only two regions (Bangalore and Hyderabad) exist, limiting low-latency access for users far from these cities. GPU pricing can be high for large multi-GPU deployments (e.g., H100 × 8 at ₹1,700/hr). There is no free compute tier; only 5 GB of object storage is free. The service catalog is narrower than AWS or GCP, with fewer managed services and third-party integrations. Krutrim is best suited for AI-focused teams in India who want a streamlined, sovereign cloud. It is less ideal for companies that need global reach, a large marketplace of SaaS tools, or extensive documentation and community resources.
Skip Krutrim if Skip Krutrim if you need a multi-region cloud outside India, extensive third-party integrations, or a large catalog of managed services like AWS or GCP.
Latest from Krutrim
Updated yesterdayAcross the latest 2 updates: 1 feature update and 1 changelog entry.
ChangelogChangelog·May 1Newest
Pricing page updated with H100 AI Pod variants
Added H100 AI Pod variants (Tiny, Nano, Mini, 1x, 2x, 4x, 8x) and updated GPU instance options with reserved pricing.
FeatureChangelog·Apr 1
Added MCP server and AI agent framework support
Enabled MCP server support and AI agent frameworks, including real-time streaming inference APIs.
Viability Score
72/100
Safe BetHow likely is Krutrim to still be operational in 12 months? Based on 4 signals — momentum (how recently it shipped), wrapper dependency, revenue model, and web presence.
momentum
55
funding runway
80
website health
90
github activity
45
wrapper dependency
100
Last calculated: June 2026
How we score →About Krutrim
Krutrim Cloud is an India-native, AI-first cloud platform built for developers, startups, and enterprises. It offers a unified stack for compute (CPU/GPU), storage, networking, and AI workloads, all hosted in India to ensure data sovereignty. You get dedicated GPU instances (A100 and H100), Kubernetes-managed AI Pods (including fractional GPUs), object and block storage, managed Kubernetes, load balancers, and DNS. The platform provides AWS-compatible APIs and SDKs for Python, Node.js, Go, Java, and Rust, making it easy to migrate existing workloads. Trusted by over 500 teams, SOC 2 certified, and battle-tested at scale (Ola Group migration achieved <30% cost reduction). Krutrim offers 99.99% uptime SLA, INR billing with transparent costs, and 24/7 support with under 15-minute response. Unlike generic clouds, it is purpose-built for AI workflows and India-first compliance.
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Key Features
- GPU Instances (A100, H100) with NVLINK
- AI Pods – Kubernetes-managed fractional to full GPU nodes
- CPU Virtual Machines (1-32 vCPU, 4-128 GB RAM)
- Object Storage with volume-based tiers
- Block Storage (SSD) with snapshots and backups
- Managed Kubernetes control plane
- Load Balancers (Application and Network)
- Virtual Private Cloud (VPC) and DNS management
- IAM and Private Networking
- AI model fine-tuning and deployment pipelines
- SDKs: Python, Node.js, Go, Java, Rust
- AWS-compatible APIs for migration
- INR billing with transparent cost tracking
- 24/7 support with <15 minute response
- 99.99% uptime SLA financially backed
Real-world workflow fit
Concrete scenarios for the personas Krutrim actually fits — and what changes day-one when you adopt it.
AI startup founder in India
Train a custom LLM on 4x H100 GPUs using Krutrim's GPU instances and managed Kubernetes.
Outcome: Launch a fine-tuned model in weeks with <₹850/hr compute cost and full data sovereignty.
DevOps engineer migrating from AWS
Recreate a multi-tier app using Krutrim's CPU VMs, VPC, and load balancers with AWS-compatible APIs.
Outcome: Reduce infrastructure cost by up to 30% while keeping billing in INR and data in India.
Data scientist at an Indian enterprise
Set up a persistent AI inference pipeline using AI Pods (A100 Nano) with block storage and object storage.
Outcome: Serve 1M+ inference requests/month with 99.99% uptime and transparent billing at ₹49/hr.
Use Cases
- Train and fine-tune AI models on A100/H100 GPU clusters with distributed training support.
- Deploy low-latency LLM inference APIs using managed AI Pods with Kubernetes orchestration.
- Migrate existing AWS workloads to an Indian cloud using compatible APIs and INR billing.
- Run enterprise applications with high-availability VPC, load balancers, and IAM security.
- Manage large-scale object and block storage for AI pipelines and backups.
Models Under the Hood
A100 80 GB GPUH100 80 GB GPUFractional GPU (A100 Tiny/Nano/Mini)Kubernetes-managed AI Pods
Limitations
As of May 2026, Krutrim's documentation and tutorial resources are limited, with no written walkthroughs publicly available. The platform's GPU availability and specific model details (e.g., exact LLM versions) are not fully transparent. Pricing can become steep for large-scale multi-GPU deployments (e.g., H100 × 8 at ₹1,700/hr). Only two regions (Bangalore and Hyderabad) are available, limiting latency for distant users. No free tier beyond 5 GB object storage; compute instances are pay-as-you-go.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Annual total
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Contact sales for a quote
Effective monthly
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Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published Krutrim tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
GPU Instances - A100 80 GB × 1
₹189/hr on-demand; ₹148/hr monthly; ₹132/hr 6-month; ₹98/hr
Ideal for
Solo ML engineer or small team needing dedicated A100 for single-GPU training or inference.
What this tier adds
Starting dedicated GPU tier with 80 GB GPU memory and NVLINK, priced at ₹189/hr on-demand.
GPU Instances - H100 × 1
₹213/hr on-demand; ₹198/hr monthly; ₹186/hr 6-month; ₹173/hr
Ideal for
Startup or research team requiring H100 performance for training medium-sized models.
What this tier adds
H100 with 80 GB GPU memory, ₹213/hr on-demand; more advanced than A100.
GPU Instances - H100 × 2
₹426/hr on-demand; ₹396/hr monthly; ₹372/hr 6-month; ₹346/hr
Ideal for
Team training models needing 160 GB aggregate GPU memory and higher throughput.
What this tier adds
Dual H100 with NVLINK, 400 GB RAM, ₹426/hr on-demand.
GPU Instances - H100 × 4
₹852/hr on-demand; ₹792/hr monthly; ₹744/hr 6-month; ₹692/hr
Ideal for
Enterprise AI lab needing 4-way H100 cluster for distributed training.
What this tier adds
Quad H100 with 800 GB RAM, 320 GB GPU memory, ₹852/hr on-demand.
AI Pods - A100 Tiny
₹24.00/hr
Ideal for
Developer experimenting with small AI models or inference on a budget.
What this tier adds
Fractional GPU: 5 GB GPU memory, Kubernetes-managed, cheapest AI pod at ₹24/hr.
AI Pods - A100 Nano
₹49.00/hr
Ideal for
Hobbyist or student running moderate inference or fine-tuning tasks.
What this tier adds
10 GB GPU memory, ₹49/hr; step up from Tiny for larger models.
AI Pods - A100 Mini
₹73.00/hr
Ideal for
Small team running production inference with moderate throughput requirements.
What this tier adds
20 GB GPU memory, ₹73/hr; good balance of performance and cost.
AI Pods - A100 × 1
₹170.00/hr
Ideal for
Team needing a full A100 GPU with Kubernetes orchestration for training or inference.
What this tier adds
Full GPU: 40 GB GPU memory, Kubernetes-managed, ₹170/hr.
Integrations
Python SDKNode.js SDKGo SDKJava SDKRust SDKTerraformCLI
Where the pricing makes sense
The company stage and team size where Krutrim's pricing actually pencils out — and where peers do it cheaper.
Krutrim's GPU pricing is competitive for Indian users: A100 Tiny at ₹24/hr is cheaper than AWS g4dn.xlarge (approx ₹43/hr). However, for large clusters, AWS may offer better spot pricing. Krutrim's reserved pricing (6-month/1-year) can cut costs by up to 48% compared to on-demand. Overall, it's cost-effective for AI workloads in India but not the cheapest for generic compute.
Setup time & first value
How long it actually takes to get something useful out of Krutrim — broken out by persona, not the marketing-page minute.
You can deploy your first VM or AI Pod in under 5 minutes using the console or SDKs. Setting up a full stack (VPC, Kubernetes, load balancer) takes about 30 minutes. Migrating an existing AWS workload may take 1-2 weeks depending on complexity, aided by compatible APIs.
Switching to or from Krutrim
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
Migrating in
- →From AWS: use AWS-compatible APIs and SDKs; export data to S3-compatible object storage, then import to Krutrim.
- →From GCP: migrate data via object storage transfer or direct VM image export/import.
- →From on-prem: use VPN to connect and rsync/restore backups to Krutrim block storage.
Migrating out
- ↗To AWS: use AWS DataSync or S3 copy tools compatible with Krutrim's object storage.
- ↗To GCP: use gsutil to copy data from Krutrim's S3-compatible endpoints.
- ↗To Azure: use AzCopy or Azure Data Box for large datasets.
- ↗To on-prem: export VM images and storage volumes via standard tools.
Recent material changes
Pricing, brand, ownership, or deprecation changes worth knowing before you commit. Most-recent first.
- •May 2026: Pricing page updated with H100 AI Pod variants (Tiny, Nano, Mini, 1x, 2x, 4x, 8x) and new GPU instance options.
- •April 2026: Added support for MCP server and AI agent frameworks, enabling real-time streaming inference APIs.
Resources & Guides
Frequently Asked Questions
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