Both trace agents, run offline and online evals, run annotation queues and version prompts. LangSmith is the natural fit for LangChain, LangGraph and Deep Agents, with deployment and a model-call gateway from the same vendor; Confident AI is the framework-neutral choice with more ready metrics, red teaming and simulation. Your framework and your team size both move the answer.[1][4]
LangSmith is LangChain's platform for tracing, evaluation, prompt versioning and agent deployment, priced per seat; Confident AI is an independent evaluation platform built on the open-source DeepEval framework, priced per organisation. LangSmith adds a model-call gateway in beta that can cap spend and redact data; Confident AI adds red teaming and conversation simulation. Teams on LangChain or LangGraph lean LangSmith; teams that want a deep metric library in CI lean Confident AI.
| Decision factor | Confident AI | LangSmith |
|---|---|---|
| Ownership | Independent; Y Combinator Winter 2025 company | LangChain, independent; $125M Series B in October 2025 |
| Open-source pieces | DeepEval and confident-trace (Apache-2.0) | Client SDKs (MIT); LangChain and LangGraph frameworks |
| Built-in metrics | 40+ DeepEval metrics, red teaming, simulation | LLM-as-judge, pairwise and code evaluators you define |
| Run-time checks | None; threshold checks run on recorded data | Partial: LLM Gateway (beta) caps spend, restricts models and redacts data in model calls |
| Agent deployment | Not offered | LangSmith Deployment, formerly LangGraph Platform |
| Self-hosting | Enterprise only | Enterprise add-on, or BYOC |
| Free tier | 2 seats, 1 GB-month of trace data, 5 test runs a week | Developer: 1 seat, 5k base traces a month |
| First paid tier | Starter: $200 a month per organisation, unlimited seats | Plus: $39 per seat a month, 10k base traces |
| Capability | Confident AI | LangSmith | Prefactor |
|---|---|---|---|
| Observe | |||
| LLM call tracing | ✓ | ✓ | ✓ |
| Multi-step agent tracing (tool calls, nested spans) | ✓ | ✓ | ✓ |
| Cost tracking | ✓ | ✓ | ✓ |
| OpenTelemetry support | ✓ | ✓ | ✓ |
| Evaluate | |||
| Offline evals (datasets, experiments) | ✓ | ✓ | ✓ |
| Online evals on production traffic | ✓ | ✓ | ✓ |
| LLM-as-a-judge scoring | ✓ | ✓ | ✓ |
| Code-based evaluators | ✓ | ✓ | ✓ |
| Human review and annotation | ✓ | ✓ | ✓ |
| Prompt versioning and playground | ✓ | ✓ | — |
| Act on findings | |||
| Runtime controls on live agent actions | — | Partial | ✓ |
| Gateway or proxy in the request path | — | — | — |
| Licence and deployment | |||
| Open source | Partial | Partial | Partial |
| Self-hosting | Partial | ✓ | Partial |
LangSmith comes from the team behind LangChain, LangGraph and Deep Agents, and its tracing, evaluation and Trajectory view are built around those frameworks first. It is also more than an evaluation tool: LangSmith Deployment runs agents, Engine reads production traces and drafts fixes, Fleet builds no-code agents, and the LLM Gateway sits in the model-call path. If you are already on LangGraph, much of this comes with little setup.
Confident AI is framework-neutral. It lists integrations for LangChain, LangGraph and Deep Agents alongside OpenAI Agents, Claude Agent SDK, CrewAI, Pydantic AI, Google ADK, Mastra and voice frameworks such as LiveKit and Pipecat. It does not deploy agents or route model calls; it focuses on testing and scoring them.
LangSmith's cloud runs in US, EU and APAC regions, and on Enterprise it adds BYOC, hybrid, self-hosted Kubernetes and VPC options. Base traces are kept for 14 days, and extended retention is capped at 180 days on SaaS. Confident AI hosts in the US or EU on every plan, offers custom residency on request, and self-hosts on Enterprise on AWS, GCP or Azure Kubernetes.
Both infer cost automatically for mainstream providers: LangSmith for most OpenAI, Anthropic and Gemini models, Confident AI for OpenAI, Anthropic and Gemini. LangSmith also lets you attach manual costs to any run, including tool calls; Confident AI uses project-level cost rules for other models. One housekeeping note for LangSmith users: the combined langsmith-sdk repository was archived on 16 October 2026, and the Python and JavaScript SDKs now live in separate repositories.
LangSmith gives you a strong loop from trace to dataset to experiment, with LLM-as-judge, pairwise and code evaluators offline and online, annotation queues with pairwise review, and the Prompt and Context Hub with commit-based versioning. You define most evaluators yourself.
Confident AI brings ready-made judgement. DeepEval's 40+ metrics cover agents, multi-turn conversations, RAG, MCP and multimodal output, run locally or in CI, and the platform adds red teaming, multi-turn and voice simulation and tool failure analytics. Its online evals run only referenceless metrics during tracing. If you want metrics ready to run, the library matters; if you already know what to measure, LangSmith's plumbing may be all you need.
LangSmith's LLM Gateway, in public beta since 30 July 2026, can cap spend by returning an error, restrict which models are used, and redact personal data or secrets, with custom regex added in October 2026. It covers model calls only; LangChain describes tool and MCP gateways as planned. Confident AI has no gateway and documents no inline blocking; its threshold checks run on recorded data over a trailing 24-hour window. If spend caps on model calls are a requirement, LangSmith covers that today.
The gateway is in beta, so check its limits before you depend on it in production. LangSmith's automation rules, like Confident AI's threshold checks, act on recorded traces and do not stop an agent mid-run. For tool calls, such as a refund or a record update, neither product provides a check today.
As of 20 October 2026, LangSmith's Developer tier is free for one seat and 5k base traces a month, Plus is $39 per seat a month with 10k base traces, and the per-trace overage rate is not published. Confident AI's Free plan covers 2 seats and 1 GB-month of trace data, and Starter is $200 a month per organisation with unlimited seats and 5 GB-months; Team is $2,000 a month.
On seat cost alone, six LangSmith Plus seats cost $234 a month, more than Confident AI's Starter, while a team of five or fewer pays less on LangSmith. Trace allowances are measured differently, so check volume too, against Confident AI's pricing and LangSmith's pricing.
We sell the layer this section describes. Read it with that in mind.
Stated plainly, since Prefactor publishes this page: Prefactor is an agent evaluation platform that scores each step of a run and acts on what it finds through runtime policies on tool calls and data access, approval holds for risky actions, and a kill switch for a run, an agent or a team. LangSmith's gateway covers model calls but not tool calls, and Confident AI checks recorded data after the fact. Prefactor is not tied to LangChain, but it has no prompt playground or prompt registry and its platform is hosted, so for prompt iteration LangSmith or Confident AI is the better pick. See the full comparison of 15 platforms.
Reviewed against public product and documentation pages on October 20, 2026. If a vendor has changed a feature, product name, or positioning since then, send a correction and we will update it. Numbered source links in the page body point to the ordered sources below.
Prefactor scores each step of every agent run and can hold or stop an action when a score says it should not happen. Start on the free tier: 5,000 spans a month, no credit card required.
Prefactor helps teams observe, evaluate, and improve their AI agents in production, across every framework and provider.