DeepInspect vs Vellum AI: LLM App Development and Runtime Enforcement

Vellum is where a team ships an LLM application. A Y Combinator company from the W23 batch, it gives engineers a prompt playground to test prompts side by side across OpenAI, Anthropic, Google, and self-hosted models, an evaluation framework with dataset-backed test suites and custom metrics, a visual workflow builder with replayable traces for multi-step agents, and deployment, versioning, and production monitoring with model routing and fallback. It raised a $20 million Series A after a $5 million seed, and it markets itself as the platform for the full develop-deploy-monitor loop.

That is LLMOps, and Vellum covers a wide arc of it in one place. Someone has to build the prompts, prove they work, wire the workflow, and watch it after release.

Teams comparing Vellum to DeepInspect are usually holding a development platform next to a security control point. These are not competing purchases. I want to make the layers explicit.

TL;DR

Vellum is an LLMOps platform: prompt engineering, evaluation, workflow orchestration, deployment, and production monitoring for LLM apps and agents. DeepInspect sits inline between authenticated callers and model providers, decides whether each request proceeds based on identity and data, and commits a per-decision audit record. One builds, tests, and runs the AI feature. The other authorizes each live call and records the ruling.

Vellum: build, evaluate, orchestrate, monitor

Vellum's core is the development platform. The playground lets a team compare prompts and models against each other, the evaluation suites score outputs against datasets and custom metrics before a change ships, and the workflow builder assembles multi-step chains and agents with traces you can replay to debug them. Deployment and versioning move a tested workflow into production, and monitoring watches it there, with routing and fallback across providers.

That arc is the daily work of building an AI product, and having the playground, the evals, the workflow, and the monitoring in one platform is the reason teams reach for it. It shortens the loop between an idea and a shipped, measured feature.

The platform orchestrates and measures the application. It decides how a workflow is structured, whether a build passed its evals, and how the shipped system is performing. That is application and quality work, distinct from whether a specific caller was authorized to make a specific call with specific data.

Building the application versus governing its traffic

DeepInspect is a stateless proxy sitting between authenticated users or agents and any LLM endpoint. Every live call traverses it, and the decision happens before the provider sees the request.

The distinction is layer. Vellum builds and runs the application, and its monitoring tells the team how the feature performs. DeepInspect governs the traffic of a shipped system and decides, per request, whether this identity may proceed. A passing eval and a healthy monitoring dashboard say the application works. They say nothing about the analyst who this afternoon sends regulated data through a workflow that never appeared in any test, and refusing that call belongs to an enforcement layer. The reasoning is in why AI security must be inline.

Identity is the second gap. Vellum's workflow and monitoring know which workflow ran and how it performed. They do not carry a verified statement of who was calling in production, under which role, with which entitlements, and whether policy permitted it. DeepInspect treats that identity as the primary key of every decision, described in identity-aware AI gateway architecture.

Feature comparison

| Capability | Vellum AI | DeepInspect | |---|---|---| | Prompt playground and versioning | Yes, core | No | | Evaluation and dataset test suites | Yes, core | No | | Visual workflow and agent orchestration | Yes, core | No | | Deployment and production monitoring | Yes | Decision records only | | Model routing and fallback | Yes | No | | Identity-bound per-request policy | No | Core | | Inline blocking of a request on policy | No | Yes | | Application-independent audit record | No | Core, tamper-evident | | Data classification against custom classes | No | Yes | | Primary buyer | Product and ML engineering | Security and compliance |

The overlap is thin because the two live at different layers. Vellum builds and runs the AI feature. DeepInspect governs what the shipped feature is allowed to do and produces the evidence.

Where Vellum AI is the better answer

If the work is building an LLM application, comparing prompts and models, gating changes on evals, wiring an agent workflow, and monitoring it in production, Vellum covers that arc and a policy proxy has nothing to add. For a team that wants the development and operations loop in one platform, it is a strong fit, and dropping a security proxy in place of a workflow builder would be a category error.

Building and measuring the feature comes before governing who may call it. Vellum owns the first job.

Where the gap opens

Two requirements move a team past what a development platform provides.

The first is control over live traffic. Production sees prompts and callers no dataset or workflow test anticipated. A monitoring dashboard records that traffic; it cannot refuse a request. An inline policy layer can, keyed to the caller's identity and the data in the prompt, which is the post-authentication gap.

The second is audit evidence. A passing eval and a monitoring log are development artifacts. A regulator under the EU AI Act asks for a per-decision record of what was authorized in production, under which policy, on storage the application cannot rewrite. That distinction is developed in signed audit logs for AI requests.

Pick Vellum AI if

• You are building and operating LLM applications and agent workflows.
• You want a prompt playground, evals, and workflow orchestration in one place.
• Deployment, versioning, and production monitoring are the immediate need.
• Model routing and fallback across providers matter to you.
• Nobody is asking you for per-decision compliance evidence from production.

Pick DeepInspect if

• You need live model calls refused, not workflows tested.
• An auditor or enterprise customer wants per-decision records tied to an identity.
• Policy must differ by role and be enforced on production traffic.
• You need an audit record on storage the application cannot alter.
• You operate under the EU AI Act, DORA, HIPAA, or a comparable mandate.

DeepInspect

This is exactly what DeepInspect does. DeepInspect sits inline between your users or agents and the LLM APIs they call. For every request and response, it evaluates identity, data classification, model authorization, and organizational policy, then makes a pass or block decision before the traffic reaches the model.

The record is the second half of the product. Every decision writes a structured entry containing the authenticated identity, the resolved role, the data classes found in the prompt, the policy version in force, and the outcome. That entry commits before the response returns, on storage the application cannot write to, which is what lets it serve as evidence rather than as a monitoring artifact. Enforcement adds under 50 ms in internal testing against inference times of 500 ms to several seconds. A development platform and an enforcement layer belong in the same program, at different layers.

If you are facing the August deadline, let's talk.