Last updated: 2026-08-19
Dynamo AI is an enterprise AI security platform launched in 2021 that combines real-time LLM guardrails, pre-production red-teaming, and agent-tool risk mapping for regulated industries. Guardrail policies are written in plain English instead of custom classifier code, unlike most rival AI-security platforms.
About Dynamo AI
Dynamo AI is an enterprise AI security and compliance platform founded in 2021 by MIT PhDs Christian Lau and Vaikkunth Mugunthan, and it went through Y Combinator's Winter 2022 batch. The company has raised $15.1 million in a Series A round announced in August 2023 and runs a team of roughly 40 people out of San Francisco. Its pitch is narrow: give enterprises deploying generative and agentic AI a way to prove, in an audit, that the system is secure and compliant before and after it ships. The product suite splits pre-production and post-production risk into two tools plus an agent-focused third. DynamoEval runs automated red-team attacks against an AI system before launch, probing for hallucinations, PII leakage, and prompt injection, and produces a report a compliance team can hand to an auditor. DynamoGuard is the runtime layer: it sits in front of the model and blocks jailbreaks, prompt injection, PII leakage, and policy violations in real time, using guardrail policies that legal or risk staff write in plain English rather than code. AgentWarden, the newest of the three, is built specifically for agent security: it evaluates which combinations of tools an agent can chain together (what Dynamo calls the 'Lethal Trifecta': untrusted input, private data access, and an external communication channel) and enforces allow, deny, or approval decisions at the tool boundary. DynamoGuard can also run on-device on NPU-equipped AI PCs, so guardrails keep working in offline or air-gapped environments in addition to the standard VPC and cloud deployment options. That flexibility matters most to Dynamo's customer base, which skews toward regulated and defense buyers rather than self-serve startups. Dynamo AI does not publish pricing; every engagement is a custom quote through direct sales. Its named customers include Intel, Lenovo, Experian, and Qualcomm, plus a U.S. Army contract for AI risk management announced in December 2025. It holds SOC 2 and ISO 27001 attestations and deploys into customer VPCs or on-premises environments by default so sensitive data does not have to leave the customer's own infrastructure.
Pricing
No public pricing. Each of the three products carries its own custom quote, set after a sales scoping call and shaped by deployment mode (VPC, on-premises, or on-device) plus the volume of agents or policies covered.
Key Features
- DynamoGuard real-time guardrails: Blocks jailbreaks, prompt injection, PII leakage, and toxic or sexual content across 15+ categories in real time at the model endpoint.
- DynamoEval pre-production red-teaming: Runs automated attacks against an AI system before launch to surface hallucinations, PII exposure, and prompt injection risk, then generates an auditable report.
- AgentWarden agent-tool risk mapping: Evaluates agent-accessible tool combinations under a 'Lethal Trifecta' model and eliminated 97% of a tested deployment's attack surface across 6 MCP servers and 154 tools in under 5 minutes.
- Natural-language policy authoring: Compliance, legal, and risk staff write guardrail policies in plain English instead of code, with deployment measured in minutes rather than sprints.
- On-device NPU deployment: DynamoGuard multiplexes over 100 policy guardrails on Intel Core Ultra NPU hardware, running without an active internet connection for edge or air-gapped use cases.
- MCP client and server coverage: AgentWarden covers 12 named MCP integrations: five clients (Cursor, Claude Code, Windsurf, Azure AI Agents, VS Code) and seven servers (Atlassian, GitHub, GitLab, Salesforce, ServiceNow, Slack, Notion).
Pros
- In Dynamo's own published benchmark, evaluating tool combinations instead of single tools found far more attack paths across a six-server, 154-tool test environment than a per-tool scanner would catch.
- Guardrail policies are authored in plain English by compliance or legal staff rather than ML engineers, a workflow that skips the custom classifier training most rival guardrail tools still require.
- Its customer list already includes a Fortune 500 semiconductor maker, a PC OEM, and a national credit bureau, alongside a U.S. federal defense contract, evidence of enterprise-grade due diligence a company this size does not clear without real audits.
Cons
- No public pricing exists; every plan requires a custom sales quote, which slows self-serve evaluation for smaller teams compared to usage-based competitors.
- No independent third-party review score (G2, Trustpilot, Capterra) is published yet, so buyers must rely on Dynamo's own reported detection and attack-surface figures.
- At roughly 40 employees, the support organization is smaller than that of larger rivals in the space, which can matter for enterprises expecting round-the-clock dedicated support.
Frequently Asked Questions
What are Dynamo AI's pricing plans in 2026?
None of Dynamo AI's three products list a price. DynamoGuard, DynamoEval, and AgentWarden are each priced as a custom quote, so prospective buyers contact the company directly rather than choosing from a published tier. Team size, agent count, and how many policies a customer runs all factor into that number.
Is Dynamo AI free to use?
No, Dynamo AI has no free tier or self-serve trial. Every deployment goes through a sales conversation and a custom contract, so evaluating the platform requires contacting the company directly rather than signing up online.
What are the best alternatives to Dynamo AI?
HiddenLayer is a closer fit if the priority is scanning ML model files and supply-chain artifacts across 35+ formats rather than runtime LLM guardrails. Cyera fits better for agentless data discovery and DSPM/DLP classification across cloud and on-premises stores. Openlayer is the alternative for teams that want a broad general-purpose AI evaluation and observability library rather than Dynamo's agent-security and on-device focus.
Dynamo AI or HiddenLayer: which should you pick?
Choose HiddenLayer when the risk is a poisoned or backdoored model file entering your pipeline, since that is what it scans for. Choose Dynamo AI when the risk is runtime behavior: prompt injection, PII leakage, policy violations, or an agent chaining MCP tools into an unsafe path. The two solve adjacent but different problems and some enterprises run both.
What does it take to start using Dynamo AI?
Start by contacting Dynamo AI's sales team to scope which product (DynamoGuard, DynamoEval, or AgentWarden) and deployment mode fits, since none of the three offer self-serve signup. For DynamoGuard, integration means pointing an existing LLM endpoint at Dynamo's chat or analyze endpoint, which supports streaming over web sockets. AgentWarden onboarding involves connecting it to your MCP clients and servers so it can run its initial risk-discovery pass.
Top Alternatives
- HiddenLayer: HiddenLayer's strength is scanning model files and supply-chain artifacts for backdoors before they ever load; Dynamo AI's strength is watching what the model does once it is answering live traffic.
- Cyera: Cyera specializes in discovering and classifying sensitive data across cloud and on-prem stores before it reaches an AI system; Dynamo AI picks up after that, guarding the AI system itself.
- Openlayer: Openlayer suits teams that want a broad evaluation-test library spanning many AI use cases; Dynamo AI suits teams narrowly focused on compliance-grade guardrails and MCP agent security.