stackcontrolai vs LangSmith.
LangSmith traces what your agents did. stackcontrolai governs what they're allowed to do across every provider.
LangSmith is a strong observability and eval layer for LangChain-style apps. It does not govern model traffic, enforce policy, or attribute cost across providers — which is where stackcontrolai lives.
LLM tracing and offline evals for teams on the LangChain ecosystem
- · Deep traces and prompt versioning
- · Mature eval and dataset tooling
- · Tight LangChain / LangGraph fit
- · No inline policy or RBAC on live traffic
- · Ties best to one SDK ecosystem
- · No cross-vendor cost or approval chains
Centralized AI control + governance
- · Policy + audit enforced on every model call
- · Provider-agnostic routing and cost ledger
- · Approval chains wired to the audit log
Keep LangSmith for prompt debugging and eval runs. Put stackcontrolai in the request path so every call your agents make is governed, audited, and cost-attributed — regardless of which SDK fired it.
stackcontrolai vs LangSmith, row by row.
| Capability | stackcontrolai | LangSmith |
|---|---|---|
| Multi-vendor model routing | full | partial |
| Inline policy enforcement | full | none |
| Approval workflows | full | none |
| Tamper-proof audit log | full | partial |
| SOC 2 / ISO / EU AI Act mapping | full | none |
| Cross-stack cost attribution | full | partial |
| Secure deploy (VPC · self-host) | full | none |
| Granular RBAC + ABAC | full | partial |
Five things a control plane has to do.
Centralized control plane
One plane for every model, agent, and workflow — across every provider.
Cross-vendor governance
Policy enforced inline on OpenAI, Anthropic, Google, xAI, Mistral, Bedrock, vLLM.
Policy-as-code + approvals
Versioned policy DSL with human-in-the-loop approval chains wired to audit.
Tamper-proof audit + compliance
Every prompt, output, decision logged and mapped to SOC 2, ISO 27001, EU AI Act.
Cost + reliability across stacks
Token, dollar, and SLO accounting per team, app, and feature — every provider.
Do I have to replace LangSmith?expand
No. LangSmith is a tracing and eval surface; stackcontrolai is the control plane in front of model traffic. Most teams run both and forward traces from stackcontrolai into LangSmith if that's already their home.
Does stackcontrolai do prompt evals?expand
Yes, but in production-flavored form: scoring on live traffic, replay against alternate models, and SLO-style aggregates. LangSmith remains stronger for dataset-driven offline evals.
Where does the audit log live?expand
stackcontrolai's audit log is tamper-evident, pre-mapped to SOC 2 / ISO 27001 / EU AI Act, and covers actor, model, prompt, output, and policy decision — not just trace metadata.
We're not on LangChain. Does this matter?expand
Not for stackcontrolai. It sits at the request boundary and works with any SDK or raw HTTP. LangSmith is most valuable inside a LangChain footprint.
Traces, evals, drift, and SLOs for every model call. The observability platform built for production AI traffic.
Compare AI observability tools for end-to-end traces, evals, drift detection, and SLOs across every model call.
One plane for governance, routing, audit, and cost.
Replace stitched-together vendors with a single control plane — without ripping out the tools your teams already use.