SambaNova

hybrid · United States , Palo Alto · founded 2017

Employees: 201-1000 · Stage: Series D · Funding: $1,130,000,000

24.5

moderate

default-balanced
composite score

Enterprise AI company offering the SambaNova Suite, an integrated full-stack AI platform combining proprietary Reconfigurable Dataflow Unit (RDU) chips with pre-trained enterprise models. Focused on on-premise and private cloud deployments for regulated industries.

Analyst summary

SambaNova offers a full enterprise AI stack built on its RDU silicon, with strong on-premises deployment for sovereign workloads and solid SOC 2 Type II posture. The main caveats are the maturity of its cloud tier and heavy reliance on SoftBank-led funding.

Acceptable for on-premises and sovereign workloads; evaluate cloud tier carefully against hyperscaler alternatives.

Rating: acceptable

Compliance posture

SOC 2 Type II	Yes (report 2025-05-01)
ISO 27001	Yes
ISO 42001 (AI management system)	No / not disclosed
FedRAMP authorized	No / not disclosed
GDPR compliant	Yes
CCPA compliant	Yes
HIPAA compliant	Yes
NIST AI RMF aligned	Yes
CSA STAR certified	No / not disclosed
EU AI Act classification	general_purpose

Data handling

Trains on user data	no
Outputs feed model improvement	no
Data retention period	Customer-controlled; on-premise deployments retain nothing at SambaNova
Can delete user data on request	Yes (SLA 30 days)
Default data residency	customer-selected
Encryption at rest	Yes (AES-256 / TLS 1.3 / FIPS 140-2)
Encryption in transit	Yes
DPA available	Yes
Public subprocessor list	No / not disclosed
HIPAA BAA available	Yes

IP profile

User owns outputs	yes
Vendor claims output rights	No / not disclosed
Input IP protection	strong
Indemnification offered	Yes
Copyright shield program	No / not disclosed
Commercial use permitted	Yes
Training data provenance	partially_disclosed
Known IP lawsuits	No / not disclosed

Jurisdiction

Incorporation country	United States
Incorporation jurisdiction risk	low
Subject to US jurisdiction	Yes
Subject to EU jurisdiction	Yes
Subject to China jurisdiction	No / not disclosed
Subject to Russia jurisdiction	No / not disclosed
Government data access risk	low
Five Eyes aligned	Yes
Adequate privacy jurisdiction	No / not disclosed

Governance

Publishes model cards	Yes
Publishes transparency reports	No / not disclosed
Has AI ethics board	No / not disclosed
Safety testing disclosed	Yes
Red-teaming program	No / not disclosed
Government contracts	Yes
Responsible-AI policy	https://sambanova.ai/terms-of-use
Terms of service	https://sambanova.ai/terms-of-use
Privacy policy	https://sambanova.ai/privacy-policy

Incidents on record

No incidents on file.

OWASP LLM Top 10 cross-walk

TrustAtlas dimensions that materially address each OWASP risk. Use to translate this vendor's compliance posture and data-handling stance into the application-security vocabulary your security team already uses.

LLM01	Prompt Injection User-supplied prompts manipulate model behaviour to bypass intended controls. SecurityTransparencyDependency chain
LLM02	Sensitive Information Disclosure Models leak PII, PHI, secrets, or proprietary data through outputs. Data handlingIP exposureJurisdiction
LLM03	Supply Chain Risk propagates from upstream models, datasets, plug-ins, and vendors. Dependency chainBusiness stabilitySecurity
LLM04	Data and Model Poisoning Adversarial training data or fine-tuning input degrades model integrity. Data handlingTransparencySecurity
LLM05	Improper Output Handling Downstream systems blindly trust model output, enabling injection downstream. IP exposureTransparency
LLM06	Excessive Agency Agents granted overbroad tool, identity, or permission scopes cause harm. Dependency chainTransparencyJurisdiction
LLM07	System Prompt Leakage System prompts containing secrets or logic are extracted via crafted input. Data handlingTransparency
LLM08	Vector and Embedding Weaknesses Vector stores and RAG pipelines leak or contaminate retrieved context. Data handlingSecurity
LLM09	Misinformation Hallucinated, biased, or fabricated outputs treated as authoritative. TransparencyRegulatory complianceBusiness stability
LLM10	Unbounded Consumption Cost, denial-of-service, and resource-exhaustion attacks against LLM endpoints. SecurityBusiness stability

Full framework reference: https://trustatlas.pages.dev/framework/owasp-llm-top-10

NIST AI RMF cross-walk

How each NIST AI RMF function is supported by the dimensions TrustAtlas scores.

GOVERN	Govern Establish AI governance structure: policies, roles, accountability. Regulatory complianceJurisdictionTransparencyBusiness stability
MAP	Map Establish AI context: intended purpose, use cases, capabilities, and risks. TransparencyDependency chainData handlingIP exposure
MEASURE	Measure Quantitative + qualitative risk assessment: testing, benchmarks, monitoring. SecurityData handlingTransparency
MANAGE	Manage Treat identified risks: mitigation, controls, incident response, lifecycle. Regulatory complianceSecurityDependency chainBusiness stability

Full framework reference: https://trustatlas.pages.dev/framework/nist-ai-rmf

Cited sources

Field	Source
data_handling.data_residency_options	https://sambanova.ai/enterprise Verified 2026-04-19 by admin
data_handling.hipaa_baa_available	https://sambanova.ai/security Verified 2026-04-19 by admin
data_handling.trains_on_user_data	https://sambanova.ai/terms-of-service Verified 2026-04-19 by admin
governance.government_contracts	https://sambanova.ai/press-release/sambanova-argonne-national-laboratory Verified 2026-04-19 by admin
governance.strategic_investors	https://www.reuters.com/technology/sambanova-raises-676-million-series-d-funding-2021-04-13/ Verified 2026-04-19 by admin
jurisdiction_profiles.incorporation_country	https://sambanova.ai/about Verified 2026-04-19 by admin
security_compliance.fedramp_authorized	https://sambanova.ai/public-sector Verified 2026-04-19 by admin
security_compliance.soc2_type2	https://sambanova.ai/trust Verified 2026-04-19 by admin

Questions to ask before signing

Vendor-agnostic baseline. Send these to the vendor and require written answers before contract.

01. Provide your most recent SOC 2 Type II report (with bridge letter if applicable).
02. Describe your training-data provenance and customer opt-out mechanics in writing.
03. List all sub-processors and confirm notification policy for material additions.
04. Confirm BAA availability and signed-BAA process if we process PHI.
05. Describe rate-limiting, quota, and circuit-breaker controls protecting our usage.
06. Provide your model card or equivalent disclosure documenting intended use, limitations, and known failure modes.
07. Describe your prompt-injection defences and red-team posture against OWASP LLM Top 10 risks.
08. Confirm data residency options and which sub-regions our data may touch.
09. Provide incident-response SLAs, security-event notification timelines, and the most recent pen-test report summary.
10. Confirm output ownership terms and any indemnification or copyright-shield programs available.
11. Describe acquisition-risk safeguards and what happens to our data on a change of control.
12. List foundation-model dependencies and how upstream-model risk is mitigated.

Methodology + caveats

Composite scores use the default-balanced weight profile (25% data handling, 20% IP exposure, 15% jurisdiction, 15% security, 10% regulatory compliance, 8% transparency, 5% business stability, 2% dependency chain). All facts are sourced from the vendor's own public disclosures, public regulatory filings, or reputable secondary reporting — see the cited sources table above. This pack is decision-support material, not legal advice or audit evidence.