Most teams discover blind spots in LLM observability during the first real production incident, not from unit tests. Working across different tech companies, we keep seeing the same patterns: missing OpenTelemetry spans through multi hop agents, no regression tests with LLM as a judge for prompts and RAG, and zero cost tracking on reasoning tokens. The stakes are rising. Gartner expects the broader observability market to hit $14.2 billion by 2028, which signals heavy buyer interest in visibility platforms that now include LLM use cases, according to the 2025 Magic Quadrant coverage by ITPro. Our take: trace first, evaluate second, ship with human feedback in the loop.
The short list focuses on open source foundations, clear evaluation workflows, and real deployment options. You will learn when to pick code first evaluation, when to choose a UI led suite, how to budget, and which deployment pattern fits regulated teams. As context, enterprise AI investments are accelerating, with worldwide AI spending projected at $2.52 trillion in 2026 per Gartner.
Evidently AI
Open source framework for AI evaluation and observability with 100 plus metrics, from data drift to LLM judges. Strong Python first workflow for reports, test suites, and monitoring.
Best for: Data science and ML teams that want code driven evaluations and open source flexibility.
Key Features:
- 100 plus built in metrics for ML and LLM evaluation, custom metrics support
- Report generation and test suites for CI, including LLM as a judge patterns
- Optional monitoring UI and OpenTelemetry based tracing via a sister package
- Integrates with common ML stacks and notebooks
Why we like it: A lean way to stand up reliable evals quickly, then grow into monitoring without switching tools.
Notable Limitations: Community tooling means do it yourself setup, fewer turnkey real time dashboards than commercial suites, and fewer third party enterprise reviews compared to older MLOps products.
Pricing: Open source is free under Apache 2.0. Cloud and enterprise pricing not publicly listed. Contact Evidently AI for a custom quote.
Confident AI
Evaluation first platform powered by the DeepEval open source framework, plus observability, tracing, A/B testing, and human feedback.
Best for: Teams that want production grade, metrics driven evals with real time grading, then add tracing and experiments.
Key Features:
- Real time evaluations in production powered by DeepEval
- Tracing, monitoring, and A/B testing of prompts and models
- Feedback collection from annotators or end users
- Framework integrations for LangChain, LlamaIndex, and API level setups
Why we like it: Strong evaluation depth out of the box, with a clear path from offline tests to online grading and incident alerts.
Notable Limitations: Young ecosystem with limited independent reviews, on premises deployment typically reserved for higher tiers, and fewer public references than long running APM vendors.
Pricing: Free tier available. Published third party listings show Starter from $29.99 per user per month and Premium from $79.99 per user per month, higher tiers by quote, see this summary on Creati.ai.
LangWatch
All in one LLM observability and evaluation with OpenTelemetry native tracing, cost tracking, agent simulations, and human in the loop workflows.
Best for: Product teams building agentic features that need tracing, evals, and annotation in one place.
Key Features:
- OpenTelemetry based tracing across prompts, tools, and sessions
- Token and cost tracking, alerts, and dashboards
- Evaluation library plus agent simulations and dataset management
- Human annotation queues and feedback collection
Why we like it: A pragmatic UI that connects traces, evals, and optimization, helpful for multi team collaboration.
Notable Limitations: Newer vendor with sparse third party reviews, evolving documentation, and pricing details that vary by listing.
Pricing: Third party listings show Launch at €59 per month and higher tiers up to €199 per month with enterprise by quote, see profiles on SaaSworthy and Alphabase Marketplace.
Langfuse
Open source LLM engineering platform, covering tracing, prompt management, evaluations, datasets, and analytics.
Best for: Engineering led teams that prefer open source, self hosting, and ClickHouse friendly analytics.
Key Features:
- End to end tracing with metrics and analytics
- Prompt management with versions and releases
- Evaluation workflows and datasets for experiments
- Open source core with cloud and self hosted options
Why we like it: Clear developer experience, fast path to self host, and strong ecosystem momentum.
Notable Limitations: Acquired by ClickHouse in January 2026, so roadmap and packaging may change, some advanced enterprise controls require configuration, and pricing varies across cloud versus marketplace contracts.
Pricing: Public third party sources list Core at $29 per month and Pro at $199 per month with enterprise by quote, see a summary on FitGap. Enterprise contracts are listed on AWS Marketplace, Cloud Enterprise at $60,000 per year and Self hosted Enterprise Edition at $20,000 per month, per AWS Marketplace listings. The acquisition was announced by Orrick on January 16, 2026, see Orrick's transaction note.
LLM Observability & Evaluation Tools Comparison: Quick Overview
| Tool | Best For | Pricing Model | Highlights |
|---|---|---|---|
| Evidently AI | Code first evaluations and reports | OSS plus enterprise | 100 plus metrics, test suites, optional monitoring |
| Confident AI | Evaluation led workflows with online grading | Freemium SaaS | DeepEval powered metrics, tracing, A/B tests |
| LangWatch | UI led tracing, evals, and agent simulations | SaaS, self host options | OpenTelemetry native, cost tracking, annotation |
| Langfuse | Open source tracing plus prompt and eval stack | OSS, SaaS, marketplace | Self host at scale, datasets, analytics |
LLM Observability & Evaluation Platform Comparison: Key Features at a Glance
| Tool | Feature 1 | Feature 2 | Feature 3 |
|---|---|---|---|
| Evidently AI | LLM and ML eval metrics library | Test suites for CI | Optional monitoring UI |
| Confident AI | Real time evals in production | Tracing and A/B testing | Human feedback collection |
| LangWatch | OpenTelemetry tracing | Token and cost tracking | Agent simulations and datasets |
| Langfuse | Tracing and analytics | Prompt management | Evaluations and datasets |
LLM Observability & Evaluation Deployment Options
| Tool | Cloud API | Self Hosted | Integration Complexity |
|---|---|---|---|
| Evidently AI | Available via managed offering | Self host OSS | Low for code first Python setups |
| Confident AI | Yes | Enterprise tier | Low to medium via SDKs and APIs |
| LangWatch | Yes | Enterprise or self hosted | Medium, OpenTelemetry plus SDKs |
| Langfuse | Yes and marketplace | Self host OSS and enterprise | Medium, OpenTelemetry and SDKs |
LLM Observability & Evaluation Strategic Decision Framework
| Critical Question | Why It Matters | What to Evaluate |
|---|---|---|
| Do you need online, real time grading or offline batch evals | Online grading catches regressions before users see them | Native online evals, alerting, latency overhead |
| How will you trace multi step agents | Missing spans hide root causes and costs | OpenTelemetry support, span linking, tool call visibility |
| Can you track and control token and reasoning costs | Hidden reasoning tokens can drive bills | Token and cost tracking, pricing tier logic, dashboards |
| What deployment fits data governance | Regulated data often needs VPC or self host | Self host, data residency, SLAs |
LLM Observability & Evaluation Solutions Comparison: Pricing & Capabilities Overview
| Organization Size | Recommended Setup | Monthly Cost |
|---|---|---|
| Startup, lean team | Evidently AI OSS for evals plus basic tracing, or LangWatch Launch for UI first teams | €59 for LangWatch Launch, OSS free for Evidently |
| Product team, growing usage | Confident AI Starter or Premium for evals plus tracing | $29.99 to $79.99 per user |
| Engineering led mid market | Langfuse Pro for cloud or self host OSS, add enterprise controls as needed | $199 for Pro, enterprise varies |
| Regulated enterprise | Self hosted Langfuse Enterprise or LangWatch enterprise deployment | $20,000 per month example for self hosted Enterprise |
Notes: Pricing reflects third party listings as of February 2026 and can change. Validate terms with vendors.
Problems & Solutions
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Problem: Production hallucinations in RAG chatbots cause wrong answers and compliance risk.
What the research says: LLMs can hallucinate more often as models advance, which raises accuracy and trust issues in sensitive domains, per a July 2025 analysis by LiveScience. Evaluation methods like LLM as a judge and rubric based scoring are active research areas, see YESciEval on arXiv.
How tools help:- Evidently AI, broad LLM evaluation metrics and test suites help teams regression test prompts and RAG quality before release, backed by its open source metrics library on GitHub.
- Confident AI, real time evaluations powered by DeepEval grade live responses and trigger alerts, with the DeepEval framework maintained on GitHub.
- LangWatch, evaluation library plus agent simulations and human annotation help close the loop from production traces to golden datasets, features documented across its open source repos like LangEvals.
- Langfuse, evaluation workflows and datasets let teams compare releases and run experiments at scale, described in Y Combinator's profile which also notes the January 2026 acquisition disclosure (YC profile).
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Problem: Token bills spike without visibility into hidden reasoning tokens or long context tiers.
What the research says: Reasoning tokens are often invisible in commercial APIs and can dominate cost, which creates a transparency gap, per the CoIn auditing study on arXiv.
How tools help:- LangWatch, token and cost tracking with dashboards and alerts helps teams catch spend regressions, see feature sets summarized on its public repositories such as the LangWatch monorepo.
- Langfuse, cost tracking and pricing tier support have been documented in release notes and are available alongside enterprise procurement options on AWS Marketplace.
- Confident AI, tracing plus evaluation analytics gives per run visibility, and pricing transparency on lower tiers can help test the waters before scaling.
- Evidently AI, code first reports make cost and quality checks part of CI so regressions are caught before deployment.
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Problem: Prompt injection and jailbreaks bypass guardrails within minutes.
What the research and news say: Security leaders warn that jailbreaking corporate AI can happen quickly and expose sensitive data, see reporting in The Australian. Agent observability and boundary tracing research is emerging, for example AgentSight.
How tools help:- Confident AI, real time evals with human feedback allow fast detection and triage of unsafe outputs.
- LangWatch, built in safeguards and annotation workflows help teams detect injection patterns in traces and enforce quality gates.
- Evidently AI, adversarial and safety checks can be encoded as tests in code and scheduled.
- Langfuse, full trace context and datasets make it easier to reproduce, label, and roll out safer prompt versions at release time.
Choosing With Confidence
Here is the bottom line. If your team is code first and wants fast, transparent evals, start with Evidently AI. If you need evaluation depth with online grading, Confident AI is compelling. If you prefer a UI that connects tracing, evals, simulations, and annotation, LangWatch is a strong pick. If you want an open source platform with self hosting and marketplace procurement, Langfuse is proven, with the recent ClickHouse acquisition noted by Orrick. The broader observability tailwind is real, with Gartner projecting $14.2 billion by 2028 per ITPro. Match deployment to your data rules, make cost a first class signal, and wire evaluation into CI and production.
