Both trace agents, run experiments and score production traffic with LLM judges and code. Fiddler suits enterprises that want classic ML monitoring, agent tracing and content guardrails from one vendor; LangSmith suits builders who want prompt versioning, review queues and the deepest LangChain integration.[1][4]
Fiddler is an enterprise platform that monitors classic ML models, LLM apps and agents in one place and adds real-time content guardrails, while LangSmith is LangChain's platform for the agent development loop, with prompt versioning, review queues and deployment. Fiddler has no prompt versioning; LangSmith has no ML model monitoring. Both are independent and both offer self-hosting on Enterprise.
| Decision factor | Fiddler | LangSmith |
|---|---|---|
| Ownership | Independent; $30M Series C in January 2026 | LangChain, independent; $125M Series B in October 2025 |
| Coverage | Classic ML models, LLM apps and agents | LLM apps and agents |
| Run-time controls | Partial: content guardrails scored in real time; inline blocking via a third-party AI gateway | Partial: LLM Gateway (beta) caps spend, restricts models and redacts model calls |
| Prompt versioning | None documented | Prompt and Context Hub with commit-based versions, plus a playground |
| Human review | Span annotations (public preview); no review queue | Annotation queues with rubrics and assigned reviewers |
| Native agent SDKs | Python only; other languages via OpenTelemetry | Python, TypeScript, Go and Java |
| Self-hosting | VPC or on-premises on Enterprise; SageMaker Partner AI App | Enterprise add-on on Kubernetes; BYOC also Enterprise |
| Entry pricing | Developer: $0.002 per trace; free plan covers guardrails only, by demo request | Developer: free, 1 seat, 5k base traces; Plus $39 per seat a month |
| Capability | Fiddler | LangSmith | Prefactor |
|---|---|---|---|
| Observe | |||
| LLM call tracing | ✓ | ✓ | ✓ |
| Multi-step agent tracing (tool calls, nested spans) | ✓ | ✓ | ✓ |
| Cost tracking | Partial | ✓ | ✓ |
| OpenTelemetry support | ✓ | ✓ | ✓ |
| Evaluate | |||
| Offline evals (datasets, experiments) | ✓ | ✓ | ✓ |
| Online evals on production traffic | ✓ | ✓ | ✓ |
| LLM-as-a-judge scoring | ✓ | ✓ | ✓ |
| Code-based evaluators | ✓ | ✓ | ✓ |
| Human review and annotation | Partial | ✓ | ✓ |
| Prompt versioning and playground | — | ✓ | — |
| Act on findings | |||
| Runtime controls on live agent actions | Partial | Partial | ✓ |
| Gateway or proxy in the request path | — | — | — |
| Licence and deployment | |||
| Open source | Partial | Partial | Partial |
| Self-hosting | ✓ | ✓ | Partial |
Fiddler grew from monitoring traditional ML models for drift, performance, explainability and fairness, and now covers LLM applications and agents on the same platform. It integrates with SageMaker, Databricks, MLflow, Snowflake and BigQuery. For an enterprise, often in a regulated sector, that runs credit or fraud models alongside new agents, one vendor for both is the main reason to choose it.
LangSmith does not monitor classic ML models. It is built for the people writing agents: tracing, datasets, experiments, a playground, Engine for drafting fixes from production traces, and LangSmith Deployment for running LangGraph agents. If your work is building and shipping agents rather than overseeing a model portfolio, LangSmith covers more of the daily loop.
Fiddler raised a $30M Series C in January 2026 and has since described itself as a control plane for AI agents. Several recent features, including annotations, an evaluator cost API and a Claude Code plugin, are in public or private preview, so check what is generally available before you rely on it.
Fiddler Guardrails score requests and responses in real time for harmful content across 11 dimensions, faithfulness, personal and health data, and secrets, using its own Centor Models. Your application decides what to do with the score, or Fiddler blocks and redacts inline when it is plugged into a third-party gateway such as Kong, AgentGateway, LiteLLM or Portkey. The scope is content checks; Fiddler documents no approval step or policy engine for an agent's tool calls.
LangSmith's LLM Gateway, in public beta since 30 July 2026, sits in the model-call path and can cap spend, restrict which models are used and redact personal data or secrets, with custom regex rules added in October 2026. It covers model calls only; tool and MCP gateways are described as planned. Fiddler's content scoring goes further on what text is allowed; LangSmith's gateway goes further on cost and model access.
This is where LangSmith is clearly ahead for builders. It has a Prompt and Context Hub with commit-based versions and tags, a playground, and annotation queues with rubrics, required reviewer counts and assigned reviewers. Fiddler documents no prompt versioning or playground; its Prompt Specs are templates for judge evaluators. Its span annotations are in public preview and there is no review queue.
Fiddler's native agent SDKs cover LangGraph, LangChain, Strands Agents and Google ADK in Python. Other languages go through OpenTelemetry or the REST API. LangSmith ships SDKs for Python, TypeScript, Go and Java.
For evaluation itself the two are closer. Fiddler's Evals SDK ships 13 pre-built evaluators, custom judges that can run on Fiddler-hosted models, and Python functions as evaluators, and its Evaluator Rules score new production spans with optional backfill. LangSmith runs LLM-as-judge, pairwise and code evaluators offline and online through automation rules.
As of 20 October 2026, Fiddler's Developer plan is $0.002 per trace on SaaS, which adds observability, experiments and custom evaluators. A free plan covers guardrails only and starts with a demo request, and VPC or on-premises deployment needs an Enterprise contract. 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.
Fiddler cost follows traces and ignores seats; LangSmith cost follows seats as well as traces. Check Fiddler's pricing and LangSmith's pricing against your own volumes.
On deployment, Fiddler can also run inside your own AWS account as an Amazon SageMaker Partner AI App, which some teams find easier to approve than a new vendor. LangSmith's self-hosted add-on runs on Kubernetes, and its BYOC option keeps the data plane in your cloud while LangChain runs the control plane.
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. Fiddler's guardrails check content and LangSmith's gateway controls model calls; neither documents approval holds or policies on tool calls. Prefactor does not monitor classic ML models, has no prompt playground or prompt registry, and is a hosted platform. 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.