Krutrim
India-native GPU cloud on A100 80GB and H100 instances, managed AI Pods and Kubernetes, billed in INR with data staying in India.
Krutrim is worth it if Indian data residency is a hard requirement and you need A100 or H100 capacity. The numbers hold up: A100 80GB at ₹189/hr on-demand dropping to ₹98/hr on a 1-year commitment, H100 at ₹213/hr, and fractional AI Pods from ₹24/hr for an A100 Tiny slice. MCP server support and streaming inference APIs make it viable for agent workloads. Pass if you need regions outside Bangalore and Hyderabad, or a wide catalog of managed databases.
Verified 16m ago · liveness 67/100 · cite: rightaichoice.com/tools/krutrim
- Indian startups that must keep training data and inference inside India
- Teams that need A100 or H100 capacity billed in INR with reserved-rate discounts
- Platform teams already standardized on Kubernetes and Terraform
- Agent builders needing MCP support and streaming inference endpoints
- Global products needing regions outside Bangalore and Hyderabad
- Architectures dependent on managed Postgres, message queues or a broad SaaS marketplace
- Workloads that must run at edge locations or under non-Indian regulatory regimes
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Skip Krutrim if your architecture depends on managed Postgres, message queues or a multi-continent footprint, or if you need GPU capacity outside India — two regions in Bangalore and Hyderabad is the whole map.
Reserved discounts only pay off if your forecast is right; on-demand A100 80GB at ₹189/hr is nearly double the ₹98/hr 1-year rate, so idle GPUs are expensive.
Krutrim sits in the value tier for Indian GPU capacity: A100 80GB at ₹189/hr on-demand and ₹98/hr on a 1-year commitment, H100 at ₹213/hr, and fractional AI Pods from ₹24/hr. That undercuts India-region pricing from AWS, Azure and GCP, but you give up their global regions and managed-service catalog. It fits seed-to-mid-stage Indian teams and enterprises with in-country compliance needs, not global platforms paying for breadth.
In short
Krutrim — India-native GPU cloud on A100 80GB and H100 instances, managed AI Pods and Kubernetes, billed in INR with data staying in India. Best for Indian startups that must keep training data and inference inside India, Teams that need A100 or H100 capacity billed in INR with reserved-rate discounts, Platform teams already standardized on Kubernetes and Terraform. Free to start; paid plans from ₹0.011.
What's new in Krutrim
Checked todayAcross the latest 2 updates: 1 feature update and 1 pricing change.
Pricing page updated with H100 AI Pod variants
Added H100 AI Pod variants (Tiny, Nano, Mini, 1x, 2x, 4x, 8x) from ₹30/hr up to ₹1,700/hr, and refreshed GPU instance options with monthly, 6-month and 1-year reserved pricing.
Added MCP server and AI agent framework support
Enabled MCP server support and AI agent frameworks, including real-time streaming inference APIs aimed at tool-calling agents rather than batch jobs.
What people actually say about Krutrim — is it worth it?
We scanned public community sources for Krutrim on Aug 14, 2026 and could not establish that the discussion we found is about this tool rather than something else sharing its name. Our own analysis of that scan says the posts were off-subject. Rather than publish a sentiment score built on the wrong subject, we publish nothing here and re-run the scan.
Viability Score
How well maintained and how widely used is Krutrim? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: September 2026
How we score →Key Features
- Dedicated A100 80GB GPU instances with NVLINK at ₹189/hr on-demand
- Dedicated H100 instances from ₹213/hr on-demand up to H100 × 4 at ₹852/hr
- Kubernetes-managed AI Pods from A100 Tiny at ₹24/hr to A100 × 8 at ₹1,360/hr
- H100 AI Pod variants (Tiny, Nano, Mini, 1x, 2x, 4x, 8x) added May 2026
- Per-second billing with no minimum floor; compute free while sandbox instances are stopped
- Sandbox compute tiers nano to xlarge from ₹1.56/hr
- General-purpose CPU VMs from 1 vCPU / 4 GB at ₹3.00/hr up to 32 vCPU / 128 GB at ₹97/hr
- Reserved pricing across 1-month, 6-month and 1-year commitments
- Managed Kubernetes control plane at ₹7.00/hr
- Object storage with first 5 GB free, tiered down to ₹1.54/mo per GB above 500 TB
- SSD block volumes at ₹0.011/GB-hr with snapshots and backups
- VPC, floating IPs and managed DNS
- Application and network load balancers with data-transfer metering
- SDKs for Python, Go, Java and Rust, plus CLI and Terraform provider
- MCP server support, AI agent frameworks and real-time streaming inference APIs
About Krutrim
Krutrim Cloud is an India-first AI cloud from the Ola Group, running out of two regions — Bangalore and Hyderabad — so your training data and inference traffic never leave the country. The catalog covers dedicated GPU instances (A100 80GB at ₹189/hr on-demand, H100 at ₹213/hr with NVLINK), Kubernetes-managed AI Pods that let you rent a fractional slice of an A100 from ₹24/hr or scale to an 8-way H100 node at ₹1,700/hr, general-purpose CPU VMs from ₹3.00/hr, object storage with the first 5 GB free, SSD block volumes, a managed Kubernetes control plane at ₹7.00/hr, and networking in the form of VPC, floating IPs, DNS and application or network load balancers. The developer layer is where Krutrim differentiates. You get SDKs for Python, Go, Java and Rust, a CLI, a Terraform provider, and AWS-compatible APIs that let you point existing tooling at an Indian endpoint instead of rewriting it. April 2026 added MCP server support and AI agent frameworks with real-time streaming inference APIs, which matters if you are serving tool-calling agents rather than overnight batch jobs. Everything is metered per second with no minimum floor and stays free while a sandbox instance is stopped. The trade-off is breadth. Krutrim publishes SOC Type I, SOC Type II, ISO 20000, 27001, 27017 and 27018, a 99.99% uptime SLA, and 24/7 monitoring with an average first response under 15 minutes, but there is no marketplace of managed databases and only two regions. It suits Indian startups and enterprises that need A100 or H100 capacity with residency and rupee billing, not teams whose architecture depends on ten managed services.
Behind the Verdict
Krutrim's strength is that it prices like a utility and stays specific about it. The pricing page lists per-second metering with no minimum floor, compute at ₹0/hr while a sandbox instance is stopped, and reserved GPU rates that fall steadily: an H100 goes ₹213/hr on-demand, ₹198/hr monthly, ₹186/hr at 6 months and ₹173/hr at 1 year. AI Pods give you an unusual amount of granularity — A100 Tiny at ₹24/hr with 5 GB of GPU memory up to A100 × 8 at ₹1,360/hr with 320 GB — so you can prototype on a sliver and scale the same abstraction to a full node. The developer surface is real rather than aspirational: Python, Go, Java and Rust SDKs, a CLI, a Terraform provider, and AWS-compatible APIs for moving existing workloads. April 2026 brought MCP server support, AI agent frameworks and real-time streaming inference, which puts Krutrim in the small group of clouds that treat tool-calling agents as a first-class workload. Compliance paperwork — SOC Type I and Type II plus four ISO certifications — and a 99.99% SLA with sub-15-minute first response are stated publicly, and the Ola migration reference (thousands of VMs, petabytes, sub-30% infrastructure cost) is a genuine production proof point, not a benchmark. The honest weaknesses are catalog and geography. Two regions, no managed Postgres or message queues, no serverless compute, and load balancer data transfer at ₹0.40/GB on an application LB whose base charge is ₹5,840/mo — that base charge is easy to overlook if you only compare GPU rates. Reserved discounts require you to forecast your load; on-demand rates are roughly double the 1-year rates, so a team that guesses wrong pays for it. Where it fits: an Indian startup or enterprise running fine-tuning or inference that must stay in-country, already comfortable with Kubernetes and Terraform. Where it doesn't: a global product needing multi-continent failover, or a team that wants the hyperscaler managed-service menu.
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Real-world workflow fit
Concrete scenarios for the personas Krutrim actually fits — and what changes day-one when you adopt it.
Provisions a dedicated A100 80GB instance at ₹189/hr, moves training data into object storage (first 5 GB free, then ₹1.66/mo per GB), and wires the run through the Python SDK with VPC isolation.
Outcome: Training stays inside India for regulatory review, and the itemized INR invoice makes the cost per run auditable.
Deploys the service on a Kubernetes-managed AI Pod, starting on an A100 Tiny slice at ₹24/hr, then scales to A100 × 2 at ₹340/hr once traffic justifies it, using MCP server support and streaming inference APIs.
Outcome: Ships a tool-calling agent endpoint without provisioning GPU nodes by hand, paying only for the GPU fraction actually in use.
Points existing Terraform against the AWS-compatible APIs, recreates VPC and load balancer topology, and commits to 1-year reserved CPU rates at ₹2.10/hr for the 1 vCPU / 4 GB tier.
Outcome: Roughly comparable infrastructure at lower cost with data resident in India, and the same Kubernetes and Terraform workflow the team already runs.
Use Cases
- Fine-tune and train models on dedicated A100 or H100 instances with NVLINK interconnect
- Serve low-latency LLM inference from Kubernetes-managed AI Pods, scaling from a fractional slice to 8 GPUs
- Run tool-calling AI agents using MCP server support and streaming inference APIs
- Prototype cheaply on a stopped-when-idle sandbox instance billed per second
- Migrate AWS workloads to Indian infrastructure using AWS-compatible APIs and INR invoicing
- Host production APIs and backend services on CPU VMs behind load balancers with auto-scaling
- Store AI training data and model artifacts in object storage, tiered from free to ₹1.54/GB/mo
- Run Kubernetes-orchestrated CPU and GPU node pools from one control plane
Limitations
- Two regions only — Bangalore and Hyderabad — so multi-continent failover is not on the table.
- The managed-service catalog is narrow compared with AWS, GCP or Azure: no serverless compute, no managed message queues, and no broad set of managed databases.
- Reserved rates require accurate load forecasting, since on-demand GPU pricing runs roughly double the 1-year rate (A100 80GB at ₹189/hr on-demand versus ₹98/hr on a 1-year commitment).
- Network economics need watching: an Application LB carries a ₹5,840/mo base charge plus ₹0.40/GB data transfer, which is material for public-traffic services.
- Storage and stopped-instance charges continue accruing after compute stops.
as of 2026-09-30
Verification history
We have re-verified Krutrim 19 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
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Showing the 6 most recent of 19 verification passes.
Free to cite with attribution — this page re-verifies continuously.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
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 sits in the value tier for Indian GPU capacity: A100 80GB at ₹189/hr on-demand and ₹98/hr on a 1-year commitment, H100 at ₹213/hr, and fractional AI Pods from ₹24/hr. That undercuts India-region pricing from AWS, Azure and GCP, but you give up their global regions and managed-service catalog. It fits seed-to-mid-stage Indian teams and enterprises with in-country compliance needs, not global platforms paying for breadth.
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.
Fastest path is a sandbox instance: no credit card required and the vendor states setup in under five minutes. A developer launching a first AI Pod or GPU instance through the console or Python SDK is typically running within 15-30 minutes. A full production landing zone — VPC, IAM, Kubernetes node pools, reserved commitments — takes a few days of Terraform work.
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.
- →From AWS: point existing tooling at the AWS-compatible APIs, then recreate VPC, load balancer and IAM topology before cutting workloads over.
- →From GCP or Azure: redeploy Kubernetes manifests against the managed control plane and move object data into tiered object storage.
- →From on-prem GPU servers: move training data into object storage and re-run pipelines on dedicated A100 or H100 instances.
- →From another Indian cloud: map instance sizes to AI Pods, starting on a fractional A100 Tiny slice before committing to reserved rates.
- →From ad-hoc notebooks: wrap the workflow in the Python SDK or Terraform provider so runs are reproducible and cost-tracked.
- ↗To AWS: restore object and block storage data, then rebuild Kubernetes workloads against EKS and managed databases.
- ↗To GCP or Azure: export training data and redeploy against their GPU SKUs, accepting their region-by-region data residency terms.
- ↗To another Indian GPU cloud: re-point Terraform at the new provider and re-run training pipelines from stored checkpoints.
- ↗To on-prem: pull model artifacts and datasets out of object storage and stand up equivalent GPU nodes behind your own load balancer.
Integrations
Resources & Guides
Tutorials & Learning
YouTube returned 6 videos for “Krutrim”, and we withheld 6: 6 could not be judged, because “Krutrim” is a single word that other videos use for other things. We are showing none, because we could not prove any of them are about Krutrim.
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