Agentic Index
Fiddler AI vs Langfuse (2026)
Both vendors last verified
Both watch AI in production, and they sell to different buyers. That verdict is the Agentic Index coverage score, graded from each vendor's own published materials.
Langfuse is engineering first: open source tracing, prompt management and evaluation, self hostable, free for 50,000 units a month and $29 a month after that. Fiddler is an enterprise observability and security control plane spanning agents, LLM applications and machine learning models, with real time guardrails, explainability and governance, with a free guardrails plan and a Developer plan at $0.002 per trace. Langfuse is what your engineers will adopt this afternoon, and Fiddler is what your risk function will ask for when the models start making decisions that matter.
On the Agentic Index agent infrastructure ranking, Fiddler AI and Langfuse both clear the bar: each documents all five production contract capabilities in full. 33 of the 186 vendors in the lane clear it. See the agent infrastructure ranking
This comparison is published by Agentic Index, an independent agentic AI vendor research platform. Fiddler AI and Langfuse are each graded against the same 14 capability Agentic Index taxonomy, from the vendor's own public materials under the Agentic Index verification standard, alongside 1,084 researched vendors. No vendor pays for placement and no vendor has reviewed this page. How this evidence is graded
Choose Fiddler AI if
- Explainability and governance are requirements from your risk function, not your engineers.
- Traditional machine learning models alongside agents and LLM applications are in scope.
- Real time guardrails are a control you need in the path, not a dashboard.
Choose Langfuse if
- Open source and self hostable is a requirement.
- Your engineers, not your risk function, are the ones choosing the tool.
- Prompt management alongside tracing is the combination your engineers want.
| Feature | F Fiddler AI |
L Langfuse |
|---|---|---|
| Action & orchestration | ||
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Integrations & Tool Calling Ability to connect agents to real systems through native integrations, OAuth-authenticated actions, custom tools, APIs, webhooks, or MCP-compatible tools. |
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Fiddler AIIntegrations & Tool Calling Fiddler takes in traces from agent frameworks, OpenTelemetry, S3 and AI gateways (Kong, AgentGateway, LiteLLM), reads model data from Snowflake, S3, SageMaker, Databricks and MLflow, and sends alerts to Datadog and PagerDuty. These move telemetry in and alerts out, and there are no connectors that let an agent take authenticated actions in outside systems. SourceFiddler, docs.fiddler.ai llms.txtread 2026-09-21 |
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LangfuseIntegrations & Tool Calling More than 100 integrations with agent frameworks, model providers and gateways send traces into Langfuse, data exports to PostHog, Mixpanel and blob storage, and prompt changes send notices through webhooks and Slack. These move telemetry in and data out, and there are no connectors that let an agent take authenticated actions in outside systems. SourceLangfuse, langfuse.com homepage and pricingread 2026-09-21 |
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Workflow Orchestration Ability to sequence, branch, retry, route, and combine deterministic workflow nodes with autonomous agent steps. |
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Fiddler AIWorkflow Orchestration There are no workflows that sequence, branch or retry an agent's steps. Fiddler observes, evaluates and guards agents that run elsewhere. SourceFiddler, fiddler.ai llms.txtread 2026-09-21 |
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LangfuseWorkflow Orchestration For agents that run elsewhere, Langfuse provides tracing, evaluation and prompt management, and prompt composability links prompts together. There are no workflows that sequence, branch or retry an agent's steps, or that mix deterministic nodes with agent steps. SourceLangfuse, langfuse.com homepage and pricingread 2026-09-21 |
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Triggers & Channel Coverage How agents wake up and where they work: schedules, webhooks, message events, CRM events, inbox events, chat, email, voice, and collaboration tools. |
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Fiddler AITriggers & Channel Coverage Guardrails evaluate every agent request and response inline as it passes, continuous evaluations score production traces, and alert rules fire on monitored metrics, so Fiddler's evaluation and enforcement start from each event with no person initiating them. SourceFiddler, fiddler.ai llms.txt and homepage, and docs.fiddler.ai llms.txtread 2026-09-21 |
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LangfuseTriggers & Channel Coverage Online evaluation runs LLM as a judge and code evaluators on production observations as they arrive, and monitors and alerts fire on metric and evaluator thresholds. Evaluation work therefore starts from incoming telemetry with no person starting each run, and prompt changes also emit webhooks. SourceLangfuse, langfuse.com homepage and pricingread 2026-09-21 |
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| Knowledge & context | ||
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Knowledge Grounding & RAG Ability to ground agent behavior in company data through document ingestion, retrieval, external knowledge APIs, semantic search, or RAG layers. |
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Fiddler AIKnowledge Grounding & RAG There is no retrieval structure over the customer's knowledge that grounds an agent's answers. Fiddler evaluates RAG applications built elsewhere, including checks for faithfulness and hallucination. SourceFiddler, docs.fiddler.ai llms.txtread 2026-09-21 |
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LangfuseKnowledge Grounding & RAG Traces, prompts and evaluation datasets are stored, but there is no retrieval structure over the customer's documents or knowledge that grounds an agent's answers. Langfuse evaluates the grounding of RAG systems built elsewhere instead of providing one. SourceLangfuse, langfuse.com pricing and llms.txtread 2026-09-21 |
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Memory & State Persistence Ability to persist context across a run, conversation, workflow, user, team, or longer-term memory layer. |
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Fiddler AIMemory & State Persistence There is no session, conversation or long term memory that an agent reads back as context. Fiddler records sessions and traces for observability. SourceFiddler, fiddler.ai llms.txtread 2026-09-21 |
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LangfuseMemory & State Persistence Sessions, users and traces are recorded for inspection, and there is no session, conversation or long term memory that an agent reads back as context. SourceLangfuse, langfuse.com pricing and homepageread 2026-09-21 |
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| Control & trust | ||
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Human Oversight & Guardrails Approval steps, consent checkpoints, escalation rules, structured guardrails, policy constraints, and pause/resume controls. |
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Fiddler AIHuman Oversight & Guardrails Guardrails enforce policy inline on an agent's request and response path in under 100 milliseconds, detecting hallucinations, toxicity, PII and PHI leakage, prompt injection and jailbreaks. For coding agents they redact PII, PHI and secrets inline through the customer's existing LLM gateway, and enforceable policy is one of the platform's five pillars. SourceFiddler, fiddler.ai llms.txt and homepage, and docs.fiddler.ai llms.txt (AI gateway guardrail enforcement)read 2026-09-21 |
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LangfuseHuman Oversight & Guardrails Protected deployment labels restrict who can promote a prompt version to production, and CI checks can require an approved baseline before a change ships, so changes to an agent's behavior pass a release gate that people control. There is no approval step, escalation rule or pause before an agent acts at run time. SourceLangfuse, langfuse.com pricing and llms.txtread 2026-09-21 |
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Security, Identity & Governance RBAC, SSO, auditability, encryption, least-privilege tool access, compliance posture, and data handling policy. |
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Fiddler AISecurity, Identity & Governance The SOC 2 Type II report covers security, confidentiality and availability, is audited every year and is available under NDA, and Fiddler states HIPAA compliance. Customers sign in through SSO providers and grant granular permissions with role based access control, data is encrypted with AES-256 at rest and TLS 1.2 or higher in transit, and customer data is kept and deleted under a retention policy. SourceFiddler, fiddler.ai/securityread 2026-09-21 |
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LangfuseSecurity, Identity & Governance Reports for the SOC 2 Type II and ISO 27001 attestations are available from the Pro plan, and there is a region ready for HIPAA and a GDPR DPA. Access control covers RBAC at organization and project level, sign in with Google, Azure AD or GitHub, Enterprise SSO with Okta or Entra ID and SSO enforcement on the Teams add-on, SCIM provisioning, audit logs, data masking on the client side and data retention management. SourceLangfuse, langfuse.com pricing and homepageread 2026-09-21 |
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Observability & Auditability Traces, logs, execution histories, metrics, audit events, and debugging detail for production agent behavior. |
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Fiddler AIObservability & Auditability Visibility runs from application to session, agent, trace and span. There are native SDKs for LangGraph, LangChain, Google ADK and Strands, OpenTelemetry ingestion, tracing of LLM calls and tool use for coding agents through Claude Code and existing gateways, alerts routed to Datadog and PagerDuty, and customer data kept under a stated retention policy. SourceFiddler, fiddler.ai llms.txt, docs.fiddler.ai llms.txt and fiddler.ai/securityread 2026-09-21 |
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LangfuseObservability & Auditability Hierarchical traces capture every LLM call, tool invocation and retrieval step of an agent run, so each run's steps and tool calls are visible. They appear as trace trees and agent graphs with session and user tracking, token cost and latency, and can be filtered by user, session, cost, latency or metadata. Audit logs are kept separately on Enterprise, data can be exported in batches or on a schedule to blob storage and to PostHog or Mixpanel, and historical data access runs 30 days on Hobby, 90 days on Core and 3 years on Pro. SourceLangfuse, langfuse.com homepage and pricingread 2026-09-21 |
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Deployment & Data Residency Deployment modes and options, including SaaS, dedicated cloud, VPC, on-prem, hybrid, local runtime, and self-hosting. |
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Fiddler AIDeployment & Data Residency Deployment runs as SaaS, in the customer's VPC, on premises, in AWS GovCloud or air-gapped, and as a Partner AI App inside the customer's Amazon SageMaker, with Fiddler's Centor evaluation models running entirely in the customer's environment. SourceFiddler, fiddler.ai homepage and llms.txt, and docs.fiddler.ai llms.txtread 2026-09-21 |
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LangfuseDeployment & Data Residency Customers of Langfuse Cloud choose US, EU or JP data regions, and there is a region ready for HIPAA and AWS PrivateLink. The MIT licensed platform self hosts with Docker Compose, Kubernetes via Helm, or Terraform on AWS, GCP and Azure, with paid Enterprise additions for self hosted deployments. SourceLangfuse, langfuse.com pricing, homepage and llms.txtread 2026-09-21 |
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| Solution readiness | ||
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Prebuilt Agents, Templates & Packs Ready-made workflows, packaged employees, templates, blueprints, industry solutions, and role-specific agents that reduce time-to-value. |
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Fiddler AIPrebuilt Agents, Templates & Packs There are no ready made agents, workflows or templates a customer adopts and runs. Fiddler ships prebuilt evaluators, a metrics library and cookbooks for common uses. SourceFiddler, docs.fiddler.ai llms.txt and fiddler.ai llms.txtread 2026-09-21 |
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LangfusePrebuilt Agents, Templates & Packs The Langfuse Assistant in the app is the one prebuilt agent, and a Langfuse skill packages best practice workflows for instrumentation, prompt management and API access into a customer's coding agent. Beyond those two, there is no set of prebuilt agents or templates for a customer to choose from. SourceLangfuse, langfuse.com llms.txt and pricingread 2026-09-21 |
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| Platform extensibility | ||
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Model Flexibility & Routing Ability to work across multiple foundation models, route tasks to different models, or let buyers bring their own providers and keys. |
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Fiddler AIModel Flexibility & Routing Custom prompts and a customer choice of judge model drive Fiddler's LLM-as-a-judge evaluators, beside its own Centor Models that run in the customer's environment, so the customer chooses the model behind Fiddler's own evaluation features. Fiddler does not publish which judge models are supported. SourceFiddler, docs.fiddler.ai llms.txt (Advanced Prompt Specs) and fiddler.ai llms.txtread 2026-09-21 |
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LangfuseModel Flexibility & Routing Customers configure their own LLM connections, which the Playground uses to test prompts on real production inputs and compare models side by side, and which power the LLM-as-a-judge evaluators the customer sets up. The customer brings its own providers and chooses the models Langfuse's own LLM features run on. SourceLangfuse, langfuse.com homepage, pricing and llms.txt (LLM Connections)read 2026-09-21 |
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APIs, SDKs & MCP Extensibility Composability layer: stable APIs, SDKs, MCP tool consumption/serving, custom tools, and integration into internal systems. |
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Fiddler AIAPIs, SDKs & MCP Extensibility Native SDKs include Fiddler's OpenTelemetry SDK plus LangGraph, LangChain, Google ADK, Strands and Evals SDKs, alongside a Python client for alerts and other resources and a Fiddler MCP server that lets assistants that speak MCP query applications, traces and evaluator results. SourceFiddler, docs.fiddler.ai llms.txtread 2026-09-21 |
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LangfuseAPIs, SDKs & MCP Extensibility An extensive public API has published rate limits by plan, alongside native Python and TypeScript SDKs, OpenTelemetry ingestion for Java, Go and other languages, a CLI, and a platform MCP server that lets IDE agents manage prompts and query traces. Stable APIs based on IDs manage evaluators, and there is an agent skill. SourceLangfuse, langfuse.com homepage, pricing and llms.txtread 2026-09-21 |
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Testing, Debugging & Optimization Testing, debugging, scoring, retries, fallbacks, quality gates, and optimization loops for improving agent workflows before and after deployment. |
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Fiddler AITesting, Debugging & Optimization The Fiddler Evals SDK runs experiments on LLM applications with pre-built evaluators and custom metrics, datasets with golden labels and side-by-side comparison, custom LLM-as-a-judge evaluators, and continuous evaluation in production over more than 100 metrics, scored by Fiddler's Centor Models in the customer's environment. SourceFiddler, docs.fiddler.ai llms.txt and fiddler.ai llms.txtread 2026-09-21 |
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LangfuseTesting, Debugging & Optimization Evaluation of the customer's application runs online and offline. It covers datasets, experiments from the SDK or UI compared side by side against baselines, LLM as a judge and code evaluators managed by API, custom scores, user feedback and human annotation queues, alerts on evaluator results, and CI checks that block regressions against thresholds and approved baselines. The customer's agent is tested with datasets before production, and output quality is scored over time. SourceLangfuse, langfuse.com llms.txt, homepage and pricingread 2026-09-21 |
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| Specialist automation | ||
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Browser & Computer Use Browser, desktop, or remote/local computer control for workflows that cannot be handled through stable APIs alone. |
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Fiddler AIBrowser & Computer Use Agents get no browser, desktop or computer control, since Fiddler observes and guards agents. SourceFiddler, fiddler.ai llms.txtread 2026-09-21 |
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LangfuseBrowser & Computer Use Langfuse observes and evaluates agents, and there is no browser, desktop or computer control by an agent. SourceLangfuse, langfuse.com homepageread 2026-09-21 |
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Pricing snapshot
Sourced from the Index pricing dataset · open each vendor's profile for full detail.
| Pricing | F Fiddler AI |
L Langfuse |
|---|---|---|
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Entry price Lowest public entry point |
Free plan (guardrails) · Developer $0.002 per trace · Enterprise custom | Free Hobby (50K units/mo) · Core $29/mo · open source self host |
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Pricing confidence How public the numbers are |
Public, partial | Public, exact |
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Billing Primary billing axis |
Usage-based on the Developer plan, at $0.002 per trace; Enterprise quoted. | usage |
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Variable cost Workload / overage exposure |
High variable cost | Medium variable cost |
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Free tier / trial Try before you buy |
Free tier
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Free tier
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Buying motion Self-serve vs sales call |
Mixed | Self-serve |
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Pricing verified When this pricing was last checked |
Product updates
Newest first, each linked to its source.
Fiddler AI
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· MCP / tool calling / API
Evals datasets in Fiddler can now be edited through the REST API in Release 26.25, with every add, update or delete saved as a new numbered version that can still be read later.
Source -
· Human approval / guardrails
Release 26.24 adds secret detection to Fiddler's Sensitive Information guardrail, flagging API keys, tokens, credentials and high entropy strings across more than 35 known secret formats as a new entity type in the PII categories.
Source -
· MCP / tool calling / API
Fiddler shipped a Fiddler MCP Server with OAuth 2.0 authentication for clients such as Claude Code, along with trace routing across multiple applications, in releases 26.15 to 26.19.
Source
Langfuse
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· Deployment / data residency
FIPS mode is now available in self hosted Government deployments of Langfuse from v4.71.0.
Source -
· Security / enterprise
Project API keys in Langfuse can now be created with a permission level: Full access, Viewer, Ingestion (create traces, scores and media only) or Scores ingestion.
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· MCP / tool calling / API
Langfuse introduced stable, ID-based public APIs for managing LLM-as-a-Judge and code evaluators.
Source
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