Self-hosted private AI platform
Run open-weight models, your knowledge, and every prompt, tool, and policy decision inside your own perimeter. Your models, your data, your audit trail — portable and exportable, with no external token meter and no vendor lock-in.
Security, finance & ops approved
Run inference, company knowledge, admin, and governance inside your own perimeter — so company-aware AI reaches production without routing a single prompt through an external token meter.
Private inference engine
vLLM on GPU and host-native MLX on Apple Silicon, with LiteLLM routing, serve open-weight models on your own hardware. The model underneath is a commodity you can change at will — your knowledge, policies, and audit trail stay yours, never trapped in one vendor’s weights.
Governed knowledge base
Bring your documents and data into a private, governed knowledge base — your team gets answers grounded in your context, never the public internet, with every source and every access checked and audited. Your data never leaves your perimeter.
Governance plane
One gateway is the single chokepoint: identity, role, per-role model access, and rate limit on every request, written to a hash-chained audit log your security team can verify.
Honest about maturity
Private inference, the governed knowledge base, admin, and audit are built and run in production today. Governed intelligence that compounds as your teams use it is what we're building next — funded by customers, never pitched as current capability.
srasta platform path live Free Community Edition
Community Edition is the adoption wedge: Apple Silicon MLX, Linux CPU, or single NVIDIA GPU beta. It uses the same Srasta-Agent registration backbone and gives you private inference, admin basics, governance basics, memory, tools, and audit evidence without sending customer content to Srasta.
We learn install stage, hardware fit, runtime health, model compatibility, and upgrade interest. We never collect prompts, responses, documents, embeddings, retrieved context, secrets, or audit-log payloads.
What Srasta does
One platform: private inference, install and recovery, users and access, and audit evidence — all in your environment. Not a chat app with governance bolted on, and not a thin model proxy.
Host open-weight models on customer-controlled GPUs and route requests through an OpenAI-compatible path that security and finance can reason about.
Use guided topology, preflight checks, smoke verification, upgrade, reset, rollback, backup, and recovery workflows instead of brittle deployment scripts.
Give admins a plane for users, teams, roles, licenses, model access, onboarding, runtime health, and operational handoff.
Capture model, prompt, memory, tool, policy, and admin events so security teams can review evidence instead of trusting screenshots.
Why now
Regulated and security-conscious teams want AI in production, but public token-metered inference, scattered admin surfaces, role-blind access, and disconnected audit tooling create a stack security, finance, and platform teams cannot approve.
The platform thesis
Srasta productizes the whole operating layer: private inference, install control, admin operations, a governed knowledge base, policy-controlled tools, and compliance evidence — all owned by you and portable across whatever open-weight model runs underneath. The model is a commodity you can swap; the control, the knowledge, and the audit trail are yours to keep.
The model isn't the moat
The most credible voices in enterprise AI — including the company most invested in frontier models, and the incumbent we're most compared to — are converging on the same point: models are commoditizing, and the durable value is the layer you own around them.
“The models are getting commoditized… OpenAI is not a model company, it's a product company that happens to have fantastic models.”
Palantir's CEO called the token-based AI model “completely wrong,” arguing enterprises should own their own data, models, and business logic — not rent intelligence by the token.
Their diagnosis is now consensus. The difference: Srasta makes that ownership real for a regulated mid-size team — self-hosted, self-serve, no per-token meter, no lock-in — not a multi-million-dollar, services-heavy program.
The product
Srasta runs in the customer environment, from one Linux node to multi-host and Kubernetes deployments.
Run open-weight models on customer-controlled GPUs, route through an OpenAI-compatible gateway, and replace runaway external token bills with capacity planning.
Scoped retrieval, reranking, and context controls so AI answers with your company's knowledge — not the public internet — with memory behavior you can evaluate.
Install, inventory, topology placement, preflight checks, smoke verification, release identity, reset, rollback, upgrade, backup, and recovery workflows.
Onboard users and teams, assign roles, manage model access, configure licenses, monitor runtime health, and operate the platform without shell folklore.
Audit auth, inference, memory, tools, and admin actions; enforce RBAC and policy; produce evidence for compliance and security review.
See prompt quality, routing decisions, policy outcomes, and runtime health — the operational truth behind every governed response.
Platform layers
Srasta is not a chat UI, a thin model proxy, or an installer. It is the runtime, admin surface, and governance layer around private enterprise AI: every request is scoped, routed, observed, and recoverable.
View deployment guideEvidence, not slideware
Not a staging demo — a real customer operates on the released code path, upgraded canary-first on every tag. The same platform installs in customer-controlled infrastructure, routes private inference through a governed gateway, onboards users and roles, and produces audit evidence for security review.
For buyers, the first motion is a paid design-partner pilot around one workflow with clear governance, deployment, and cost-control outcomes.
Single-node Compose, guided multi-host Compose, Kubernetes and Helm — with hardware probing, placement, smoke verification, rollback, reset, and cosign-signed, SBOM-attested release bundles.
vLLM on GPU, host-native MLX on Apple Silicon, LiteLLM routing, on-box embeddings on arm64, and a curated model catalog with hardware-aware fit.
OIDC + RBAC, forwarded signed identity, a per-role model whitelist, rate limiting, the governed tool gateway, and a hash-chained (SHA-256) audit log with a verify step.
Config history, runtime overview, ingest management, hardware inventory, user onboarding, role grants, backups, upgrades, rollback, and release verification hooks.
Best-fit buyers
The broad market is any enterprise that needs private, governed, company-aware AI. The strongest early buyers have enough compliance pressure to block unmanaged AI, enough cost pressure to question token-metered usage, and enough urgency to run a focused pilot.
Pilot narrative
The strongest pilot proves that Srasta can run a real request through private inference, role-aware access, governed memory, policy-controlled tool execution, and an audit trail an operator can review.
Product roadmap
Srasta is intentionally transparent about maturity. The public roadmap separates the shipped product baseline from the install-plane, proof-gated pilot, operations, and enterprise milestones we are building next.
Private inference, admin, audit feed, model policy, and security review packet.
CompletedSrasta-Agent registration, Deployment Charter, catalog/license gates, and receipts.
In progressPrompt-to-audit handover, paid pilot package, and SOC 2 readiness path.
PendingCustomer funnel
The website should drive the same motion as the pitch deck: qualify the private AI need, prove one customer-controlled pilot, then convert successful evidence into an annual platform relationship.
6-8 weeks to validate one governed workflow, deployment profile, admin path, and cost-control case.
Validate runtime profile, release bundle, security evidence, install verification, and handover path.
Turn successful evidence into platform ARR, support, and expansion across teams.
Product boundaries
Technical confidence
Srasta keeps the top-level site buyer-focused, but the proof is still visible: security posture, deployment confidence, architecture, operator controls, and implementation-backed documentation.
Contact
Use the diagnostic if you need the governance, deployment, and cost-control plan first. Use the pilot path if you already have a sponsor, workflow, and environment to evaluate.
