Salt Security vs. Fiddler AI
Fiddler measures model performance. Attackers exploit the APIs models call.
Fiddler AI is an observability platform — built since 2018 to monitor LLM and ML model quality, detect hallucination and drift, and provide data scientists with visibility into how models behave. Salt Security was built for a different problem: securing the APIs, MCP servers, and downstream services that AI agents act on when the model runs. Those are not the same platform. They solve different problems for different buyers.
AI Observability and LLM Monitoring
Model performance, drift, hallucination
API Fabric and Action Layer Security
What agents do to your infrastructure
Agentic Security Graph
LLM, MCP, API, identity correlation
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What each platform was built to do
AI observability and agentic security
are different disciplines entirely
Salt Security: purpose-built agentic security
The full attack surface — LLM through API fabric
Salt Security was built from the ground up as a security platform, not an observability tool. The Agentic Security Graph maps and correlates every LLM connection, MCP server, API endpoint, identity, and data flow across code, cloud, and runtime — detecting multi-step attacks and business logic abuse in the infrastructure that Fiddler's observability layer never reaches.
- Agentic Security Graph across LLM, MCP, and API layers
- Behavioral attack detection in downstream enterprise APIs
- Identity-aware multi-step sequence correlation
- East-west and internal API coverage out-of-band
- Runtime-to-code remediation loop
- Salt Code governance in repositories before deployment
- Eight years of production API security research
Fiddler AI: MLOps Observability Platform
Model performance monitoring and LLM guardrails
Founded in 2018 as an ML model observability company, Fiddler has expanded into AI agent monitoring through its Trust Service — providing guardrails for LLM inputs and outputs, monitoring 100+ metrics including hallucination, toxicity, and drift. Its primary buyers are data science and ML engineering teams that need to operationalize AI reliably at scale.
- Deep LLM and ML model performance monitoring
- Fast Trust Service guardrails under 100ms response time
- 100+ metrics: hallucination, toxicity, PII, drift, bias
- No coverage of APIs and MCP servers agents act on
- No behavioral attack detection in downstream infrastructure
- No Agentic Security Graph — no cross-fabric threat correlation
- Observability-first — built for reliability teams, not security teams
Head-to-head
The agentic security capabilities
an observability platform cannot provide
Fiddler tells you if your model is hallucinating. Salt tells you if your APIs are being attacked. These are capabilities that require a security platform, not a monitoring dashboard.
| Feature | Description |  |  |
|---|---|---|---|
| Unified Agentic Discovery | Discovers APIs, MCP servers, and AI-driven assets across external exposure, cloud, code repositories, and runtime. |  |  |
| Agentic Security Graph | Correlates LLMs, MCP servers, APIs, identities, and sensitive data in one action-layer context. | | |
| Salt Code Governance | Governs API and MCP creation in repositories, pull requests, and developer workflows before risky logic reaches production. | | |
| Runtime-to-Code Remediation | Feeds runtime findings back into DevOps workflows and AI coding assistants to fix root causes. | | |
| Agent-Aware Sequence Correlation | Tracks unique agentic identities and multi-step intent across sessions, tools, and services. | | |
| Behavioral Action-Layer Protection | Detects machine-speed business-logic abuse beyond signatures, schemas, or prompt filters. | | |
| Internal & East-West Coverage | Protects internal APIs and downstream service interactions that edge-only and model-only tools miss. | | |
| Action-Layer Data Security | Maps sensitive data in motion across APIs, MCP servers, and agent actions. | | |
| Full API Fabric Security | Secures every API — internal, external, shadow, and third-party — regardless of which model or agent framework generated the call, with no model instrumentation required. | | |
| MCP Server Discovery and Governance | Discovers and governs MCP servers across code, cloud, and runtime — including rogue and shadow MCP servers created outside any monitored model application. | | |
Why it matters
Model reliability and API security
are not the same discipline
Fiddler measures whether your AI is performing. Salt detects whether it's being attacked.
Fiddler's 100+ metrics — hallucination rate, toxicity score, response latency, model drift — tell you how reliably your AI is performing for your users. That is a genuinely important operational discipline for any team running AI in production.
It is not the same as detecting that an attacker is using your AI agents to execute business logic abuse across your enterprise APIs at machine speed. Drift and hallucination metrics do not surface multi-step attack sequences. Model performance dashboards do not discover shadow MCP servers. Salt was built for the security problem. Fiddler was built for the reliability problem. Buying one does not replace the need for the other.
What Salt detects that Fiddler was not built to find
- Adversarial multi-step attacks across APIs — attacks that produce no observable model quality degradation
- Business logic abuse in downstream enterprise APIs called by agents after clean model outputs
- Shadow APIs and rogue MCP servers created entirely outside any monitored model application
- Internal east-west API traffic triggered by agent actions — infrastructure that produces no LLM telemetry
- Risky API and MCP logic in repositories before any model has been deployed to observe its behavior
Salt code
Security before any model has traffic to observe
Fiddler's observability model activates when a model is deployed and generating telemetry. Salt Code governs API and MCP creation at the repository level — scanning pull requests for risky integrations, shadow APIs, and unsafe agentic patterns before they ship. Runtime findings feed back into developer workflows automatically, so vulnerabilities are fixed at the source rather than observed in production dashboards indefinitely.
3
layers covered:
LLM, MCP, API
0
model instrumentation
required
11
security capabilities
observability can't deliver
8
years of production
API security research
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