Regulated enterprise AI
Build AI workflows where data residency, lineage, permissions, and audit evidence are central requirements.
Independent tool overview
Salt AI at a glance
Salt AI, now presented as Salt OS, is an enterprise AI orchestration platform for building governed pipelines and agents that run in a company's VPC, on-premises, or air-gapped environment.
Visit the official Salt AI site ↗
- Current product
- Salt OS enterprise AI orchestration
- Deployment
- VPC, private cloud, on-premises, or air-gapped
- Builder
- Visual typed pipeline canvas with pro-code options
- Models
- OpenAI, Anthropic, Gemini, Ollama, custom, and others
- Publishing
- APIs, web forms, agentic tools, and MCP
- Pricing
- Custom enterprise quote
Overview
What Salt AI is
Salt AI has evolved from the earlier getsalt.ai visual builder into Salt OS, an enterprise intelligence platform for regulated and security-sensitive organizations. The current product combines models, connected data, visual pipelines, custom agents, governance, and deployment inside the customer's infrastructure.
Teams build typed workflows on a node-based canvas, connect model providers and enterprise data, inspect run history and logs, then publish validated pipelines as APIs, internal web forms, or agent-callable tools. Ask Salt and custom agents add a conversational layer over those governed resources.
The defining proposition is deployment and control rather than a simple no-code automation subscription. Salt advertises VPC, on-premises, private-cloud, and air-gapped options; model-agnostic orchestration; zero-public-egress configurations; audit trails; and compliance-oriented controls for healthcare, financial services, life sciences, and energy.
Use cases
Who Salt AI is best for
The strongest fit depends on the job you need the product to complete, not the size of its feature list.
Private model orchestration
Run pipelines across commercial or private models inside controlled infrastructure.
Life-sciences pipelines
Connect domain data and specialist tools for research workflows with reproducible execution history.
Financial-services automation
Orchestrate research, risk, compliance, and reporting workflows with traceable inputs and outputs.
Governed internal agents
Give employees conversational access to approved models, knowledge, connections, and reusable pipelines.
Capabilities
Core Salt AI features
Visual pipeline builder
Connect typed nodes on a canvas, configure inputs, run dependencies, and organize complex workflows.
Model-agnostic runtime
Orchestrates major model providers and private models so individual steps can use different systems.
Private deployment
Supports customer VPC, on-premises, private-cloud, and air-gapped installations.
Connectors and credentials
Links models, databases, document stores, cloud files, communication tools, and enterprise services.
Ask Salt and custom agents
Provides a built-in platform agent and configurable agents with selected behavior, models, tools, and sources.
Agentic Tools
Publishes a tested pipeline as a controlled tool that approved custom agents can call.
Run history and auditability
Tracks executions, parameters, outputs, errors, and lineage for troubleshooting and review.
Salt MCP
Exposes authorized pipeline, run, node, data, and model operations to compatible clients through OAuth-scoped tools.
Process
How the Salt AI workflow works
Step 1
Define the control boundary
Document data classes, permitted models, residency rules, human approvals, side effects, and audit obligations.
Step 2
Choose deployment architecture
Select VPC, on-premises, private cloud, or air-gapped operation based on the threat and compliance model.
Step 3
Connect models and data
Configure scoped credentials for approved providers, knowledge sources, databases, APIs, and storage systems.
Step 4
Build a typed pipeline
Arrange nodes, validate compatible data edges, and keep sensitive actions behind explicit controls.
Step 5
Test and observe
Run representative and adversarial cases while inspecting outputs, logs, latency, cost, and failure behavior.
Step 6
Publish the right surface
Expose the workflow as an API, web form, agentic tool, or MCP capability with minimum required permissions.
Step 7
Operate under governance
Review audit trails, model changes, access, data lineage, incidents, and business outcomes on a defined cadence.
Cost
Salt AI pricing and free plan
Salt does not publish a current self-service Salt OS price card. The enterprise product is sold through a demo and custom quote shaped by deployment, infrastructure, models, integrations, support, and compliance scope. Older Free and $20 Individual pricing on the legacy developer portal should not be treated as current Salt OS procurement pricing.
Interactive product demo
Free to explore
A no-signup interactive walkthrough of the current Salt OS product.
- Product experience rather than a production workspace
- No sales call required for the demo
- Useful for initial capability screening
Enterprise Salt OS
Custom quote
Production access for organizations deploying governed AI in controlled infrastructure.
- VPC, private-cloud, on-premises, and air-gapped options
- Custom models and data connectors
- Security, governance, and compliance scope
- Onboarding and support negotiated with Salt
Infrastructure and models
Additional usage costs
Underlying compute, storage, model inference, networking, and connected services affect total cost.
- Cloud or on-premises infrastructure
- Commercial model-provider charges
- Private-model serving and GPU capacity
- Logging, backup, and data-retention requirements
Implementation
Scope-dependent
Enterprise deployment may require integration, validation, migration, security review, and training.
- Connector and custom-node development
- Identity and access integration
- Compliance validation and documentation
- Production rollout and staff enablement
Pricing checked . Check current pricing at the source ↗
Assessment
Salt AI strengths and limitations
Where it stands out
- Designed around private deployment and regulated-enterprise controls
- Visual pipelines serve business builders while custom nodes and APIs preserve engineering flexibility
- Model-agnostic design reduces dependence on one provider
- Typed edges make complex data flow easier to understand and validate
- Pipelines can be reused across APIs, forms, agents, and MCP clients
- Run history, logs, lineage, and access controls support operational review
- Custom agents can be constrained to selected models, sources, and approved tools
What to consider
- No public Salt OS price card makes budget comparison impossible without a sales process
- The current enterprise product is materially different from the earlier low-cost getsalt.ai visual builder
- Private deployment still requires internal infrastructure, identity, security, and operations ownership
- Connecting many models and data sources can increase governance and credential-management complexity
- Compliance-ready architecture does not automatically make a customer's specific workflow compliant
- Agentic tools with external side effects require separate approval, rollback, and incident controls
- Model-provider, compute, integration, and retention costs sit beyond the platform quote
- A proof of concept should verify actual connectors, latency, audit exports, RBAC, and air-gap behavior in the target environment
Compare
Salt AI alternatives
The right alternative depends on the specific output, workflow, controls and budget your project requires.
Business Operations
n8n
Choose n8n for broader application automation and a self-hostable workflow engine when regulated AI governance is not the primary requirement.
Explore n8n →Business Operations
Gumloop
Use Gumloop for a more accessible no-code AI automation experience aimed at business teams.
Explore Gumloop →Coding
LangChain
Consider LangChain when developers want a code-first agent framework and will assemble their own deployment and governance stack.
Explore LangChain →Agents
Zapier Agents
Evaluate Zapier Agents for agentic work across a large SaaS integration ecosystem with less infrastructure ownership.
Explore Zapier Agents →Questions
Salt AI FAQs
What is Salt AI now?
Salt AI is currently presented as Salt OS, an enterprise AI orchestration platform for models, data, visual pipelines, agents, governance, and private deployment.
Is Salt AI still a $20 visual workflow tool?
The primary product has moved to an enterprise Salt OS model with custom pricing. An older developer portal may still show Free and Individual plans, but that grid does not represent current Salt OS procurement.
Can Salt run on-premises?
Yes. Salt advertises deployment in a customer's VPC, private cloud, on-premises environment, or fully air-gapped infrastructure.
Which AI models does Salt support?
Official materials describe a model-agnostic platform with connectors for OpenAI, Anthropic, Gemini, Groq, Ollama, Stability AI, and custom models.
What can a Salt pipeline publish?
A validated pipeline can be exposed as a REST API, internal web form, or Agentic Tool. Salt MCP also lets authorized compatible clients work with platform resources.
How much does Salt OS cost?
Salt does not publish a current fixed price. Organizations request a custom quote based on deployment, integrations, infrastructure, models, support, and compliance needs.
What is Ask Salt?
Ask Salt is the conversational surface for the built-in Salt Agent and custom agents configured with selected models, behavior, tools, connections, and knowledge.
Is Salt automatically compliant?
No platform makes a use case compliant by itself. Salt supplies compliance-oriented deployment, audit, access, and lineage capabilities, but the organization must validate its configuration, controls, data handling, and process.
Bottom line
Our Salt AI verdict
Salt OS is most compelling for an organization that needs AI orchestration inside its own security and compliance boundary, not for a solo user seeking a cheap automation builder. Its visual pipelines, model flexibility, agents, reusable publishing surfaces, and MCP access form a serious enterprise platform. The tradeoff is a custom sales and implementation process. A paid proof of concept should be tied to one regulated workflow and must validate the exact deployment, connectors, RBAC, logs, lineage, failure handling, and total operating cost.
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