Ship AI workloads without
managing the infrastructure.
ShaSentra Labs is an AI infrastructure and MLOps platform. Deploy machine learning models, orchestrate GPU workloads, and scale training and inference — from a single control plane, with usage-based economics.
The orchestration layer for accelerated compute
We sit between your models and the accelerators they run on — scheduling, autoscaling, observability and metering, so teams focus on the workload rather than the cluster.
Model deployment
Move models from notebook to a running endpoint. Reproducible environments and versioned deployments, without bespoke cluster work.
GPU orchestration
Schedule and place workloads across accelerators, with autoscaling rules and scale-to-zero so idle capacity never burns budget.
Observability
Per-GPU utilisation, deployment health and Prometheus-compatible metrics — see exactly what is running and what it costs.
Interactive compute
Managed notebook environments for research and experimentation, provisioned on demand and torn down when idle.
Pipelines
Compose training and inference stages into repeatable pipelines, backed by queued asynchronous provisioning.
Transparent metering
Usage measured at the accelerator-second, so spend maps to real consumption rather than reserved capacity.
From the datacenter to the end user
One vertical path — accelerators in the rack, through orchestration and runtime, out to the API your users actually call.
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01
Datacenter & accelerators
GPU capacity, high-throughput networking and storage — the physical substrate every AI workload ultimately runs on.
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02
Cluster & scheduling
Placement, bin-packing and autoscaling decide which workload lands on which accelerator, and when capacity spins down.
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03
Runtime & orchestration
Containers, provisioning queues and lifecycle management turn a model artefact into a running, recoverable service.
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04
Platform & control plane
Deployments, pipelines, notebooks, quotas and metering — the surface teams actually work against day to day.
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05
API & end user
A stable endpoint your product calls, with latency, utilisation and per-second cost visible the whole way back down the stack.
The full generative stack, running on your terms
Serve open models, ground them in your own data, and fine-tune them — without stitching together five vendors to do it.
LLM serving
Deploy open-weight language models behind an OpenAI-compatible endpoint. Batching, streaming and KV-cache handling are managed for you.
RAG pipelines
Chunking, embedding, vector storage and retrieval as a managed pipeline — so answers are grounded in your documents, not the model's guesswork.
Embeddings at scale
Batch-generate embeddings across large corpora on GPU, then keep them fresh as your data changes.
Fine-tuning
LoRA and full fine-tuning as queued jobs with checkpointing, then serve the adapted weights from the same control plane.
Multimodal
Vision, speech and document models alongside text — same deployment surface, same metering, same autoscaling.
Prompt & model routing
Route requests across models by cost, latency or quality, and switch without rewriting your application.
The super agent
SGAI is our orchestrating agent — a controller that plans a task, delegates to specialised sub-agents, runs them in parallel across GPU capacity, and reconciles their output into a single result. One interface over many agents, many tools and many models.
Plans and decomposes
Breaks a goal into steps, decides which specialist agent owns each, and sequences the dependencies between them.
Delegates in parallel
Fans work out to sub-agents running concurrently on separate accelerators, then merges results as they land.
Self-corrects
Evaluates intermediate output, retries failed steps and reroutes to a different model or tool when a path stalls.
Stays accountable
Every plan, delegation, tool call and token is traced — so you can see what it decided and what it cost.
Advanced agents, made simple to run
Agentic workloads need tools, memory, retries and GPUs that appear on demand. ShaSentra handles that plumbing so building an agent stays a modelling problem, not an infrastructure one.
Tool-using agents
Give agents access to APIs, databases and your own functions through a declarative tool interface — no bespoke glue for every integration.
Multi-step reasoning
Run long-horizon tasks as durable, queued workflows. Steps survive restarts, retry on failure and record what happened at each stage.
Retrieval & memory
Attach vector retrieval and persistent context so agents work against your data rather than a fixed prompt window.
Bring your own model
Run agents on hosted open models or your own fine-tuned weights, on GPUs that scale to zero between invocations.
Full observability
Trace every step, tool call and token. See where an agent spent its time and its budget, then tune from real data.
Guardrails & control
Set spend ceilings, rate limits and scoped credentials per agent, so autonomy never means unbounded cost or access.
Built for the people running the models
Whether you are one researcher with a notebook or a team shipping inference to production.
ML engineers
Push a model to a live endpoint without writing Kubernetes manifests or babysitting nodes. Versioned deployments and rollbacks included.
Researchers & students
Spin up a GPU notebook for an experiment, then let it scale to zero. Pay for the hours you actually compute, not for reserved capacity.
AI startups
Get to a working inference API without hiring a platform team. Predictable per-second costs that map cleanly onto your own unit economics.
Data & platform teams
Give your organisation self-serve GPU access with quotas, autoscaling policies and per-workload visibility into utilisation and spend.
Three steps from model to endpoint
No cluster setup, no YAML archaeology, no idle capacity to babysit.
Bring your model
Point us at a container, a repository or a framework checkpoint. We build a reproducible image and version it for you.
Choose your compute
Pick the accelerator and set autoscaling bounds — including a minimum of zero, so nothing runs when nothing is being served.
Deploy and observe
Get an endpoint in seconds, then watch GPU utilisation, latency and per-second spend from the same control plane.
What teams run on ShaSentra
Model inference
Serve LLMs, vision and speech models behind an autoscaling HTTP endpoint that drops to zero between requests.
Fine-tuning
Run fine-tuning and training jobs as queued pipelines, with checkpoints and per-job resource accounting.
Research notebooks
On-demand JupyterLab environments attached to real accelerators, torn down automatically when idle.
Batch workloads
Embedding generation, dataset processing and evaluation runs scheduled across available capacity.
Quantum simulation
GPU-accelerated quantum circuit simulation with CUDA-Q and cuQuantum — state-vector and tensor-network workloads that need serious accelerator memory and scale to zero when idle.
Scientific computing
HPC and simulation workloads that already speak CUDA — molecular dynamics, computational fluid dynamics, numerical solvers.
Move your workloads across in an afternoon
Standard containers, standard model formats, standard APIs. Nothing proprietary to rewrite on the way in — and nothing holding you hostage on the way out.
Drop-in API compatibility
Point your existing client at a new base URL. OpenAI-compatible inference endpoints mean most applications need a config change, not a rewrite.
Bring your containers
If it runs in Docker, it runs here. Existing images, entrypoints and dependencies move across unchanged.
Standard model formats
Load weights straight from Hugging Face, object storage or your registry — no conversion step, no proprietary packaging.
From any cloud or on-prem
Coming from AWS, GCP, Azure or your own racks, the migration path is the same: containerise, point, deploy.
Run side by side
Shift traffic gradually. Keep your existing deployment live while you validate latency and cost on ours before cutting over.
No lock-in, by design
Your images, weights and data stay portable and exportable. If you leave, you take everything with you.
Built on a modern, open stack
Engineered for horizontal scale, asynchronous provisioning and operational visibility from day one.
Building India's AI infrastructure layer
Making accelerated compute practical for teams that want to train and serve models without operating the underlying platform themselves.
Founded
2026 — early stage, platform in active development.
Headquarters
Tiruvannamalai, Tamil Nadu, India.
Focus
AI infrastructure, MLOps tooling and GPU workload orchestration.
Let's build something
For partnerships, early access or general enquiries, we would be glad to hear from you.
shathish@shasentralabs.comTiruvannamalai, Tamil Nadu, India