Unified observability, security and incident response for cloud-scale engineering teams
Unified observability, security and incident response for cloud-scale engineering teams
Fiddler AI records 44,204 monthly visits with -10.2% month-over-month growth and Contact for Pricing pricing. Oberhahn records 633 monthly visits with -70.9% growth and Free Trial, Paid, Contact for Pricing pricing.

Know exactly what your AI spend bought.
| Dimension / Metric | ![]() | ![]() |
|---|---|---|
| Official Domain | fiddler.ai | oberhahn.com |
| Pricing Model | Contact for Pricing | Free Trial, Paid, Contact for Pricing |
| Monthly Visits | 44.2K (44,204) | 633 (633) |
| MoM Traffic Delta | -5.04K (-10.2%) | -1.54K (-70.9%) |
| Primary Market & Share | 🇺🇸 United States (28.4%) | 🇵🇰 Pakistan (90.7%) |
| Direct Visits / mo | 9.13K | 259 |
| User Rating | 4.4 | 4.2 |
| Supported Platforms | Website | Website |

The control plane for trusted enterprise agents and models
See the performance of every agent and predictive model in a single pane of glass, with every action, token and dollar traceable by user and team.
Fiddler Centor Models power policy enforcement and continuous evaluation inside your network, avoiding external LLM API calls and their hidden costs.
Detects and redacts PII, PHI and secrets on the same request path — before a prompt reaches the model and before a response reaches the developer — with sub-80ms latency.
Keeps a living inventory of every connected agent and records each enforcement decision, with who triggered it and what happened, ready for GRC and AI regulation audits.

Know exactly what your AI spend bought.
Connect coding assistants, agents, trackers and orchestration layers once, then watch every usage event flow into a single normalized ledger with no manual reconciliation.
See projected month-end spend, cost per session, spend per user, context-window use and an efficiency score, broken out by the source driving each dollar.
Every session links to real Linear, Jira or GitHub issues, so you can read cost per estimate point and see what shipped versus what sank.
Oberhahn flags oversized models, cache misses, runaway sessions and duplicate workflows, each ranked by the spend it affects so you know what to investigate first.
Unified observability, security and incident response for cloud-scale engineering teams
Unified observability, security and incident response for cloud-scale engineering teams

Free / Open-sourceAgent observability, evaluation, and continuous improvement for production AI
Free / Open-sourceAgent observability, evaluation, and continuous improvement for production AI

Attack-led AI security testing and red teaming
Attack-led AI security testing and red teaming

Free / Open-sourceBuild, simulate, evaluate and monitor self-improving AI agents
Free / Open-sourceBuild, simulate, evaluate and monitor self-improving AI agents

Free / Open-sourceEvaluate, monitor, and govern your AI applications in production
Free / Open-sourceEvaluate, monitor, and govern your AI applications in production

Free / Open-sourceStandardize LLM evaluation, observability, and red teaming across your whole org
Free / Open-sourceStandardize LLM evaluation, observability, and red teaming across your whole org

AI-powered cloud DLP and security awareness training for SaaS environments
AI-powered cloud DLP and security awareness training for SaaS environments

AI workforce analytics that turn monitoring data into productivity and cost decisions.
AI workforce analytics that turn monitoring data into productivity and cost decisions.

AI value measurement platform connecting AI spend to real business outcomes
AI value measurement platform connecting AI spend to real business outcomes
AI governance, testing, and observability for regulated enterprises
AI governance, testing, and observability for regulated enterprises
Full-stack observability with an AI SRE agent that fixes issues
Full-stack observability with an AI SRE agent that fixes issues