Top AI Evaluation Tools for Enterprises in 2026

Enterprise AI adoption is outpacing evaluation maturity. Organizations deploy LLM-powered features across healthcare, finance, legal, and education, but most evaluation infrastructure was built for research benchmarks, not regulated production environments. The gap between “the model works” and “we can prove it works to our compliance team” is where enterprise AI projects stall.

The consequences are already visible. In mid-2025, Deloitte delivered an AU$440,000 government report that contained fabricated citations and non-existent court references, all generated by GPT-4o. A university researcher caught the errors, not Deloitte's own review process. Deloitte issued a partial refund. The incident illustrates a pattern now common across enterprises: AI output enters high-stakes workflows without domain expert review, and generic quality checks fail to catch domain-specific failures.

We reviewed seven platforms for enterprise AI evaluation in 2026. They vary in deployment flexibility, compliance posture, and whether they address what enterprises actually need evaluated. SSO and encryption are baseline expectations, but the harder question is whether the platform can encode your organization's quality standards into automated evaluation that domain experts, not just engineers, can define and trust.

What makes enterprise AI evaluation different

Standard LLM evaluation measures accuracy, relevance, and toxicity. Enterprise evaluation adds organizational requirements that most open-source frameworks were not designed for:

• Compliance evidence: reproducible evaluation results with audit trails for regulators and internal governance, not just pass/fail scores
• Domain expert involvement: the people who define quality (physicians, attorneys, compliance officers) are rarely the people writing evaluation code
• Deployment control: data residency requirements, self-hosting mandates, and air-gapped environments that rule out SaaS-only platforms
• Stakeholder alignment: legal, compliance, product, and engineering teams need shared quality definitions, not siloed metrics that only engineers understand

Most platforms handle deployment control well. Compliance evidence is improving. Domain expert involvement is where the gap persists.

Platform breakdowns

1. Truesight

Enterprise AI teams face a specific problem: the people who know what “good” looks like (clinicians, compliance officers, educators, financial analysts) rarely write evaluation code. Truesight bridges that gap. Domain experts define quality criteria through a guided, no-code interface, and those criteria deploy as automated evaluations running against production AI outputs. The platform is purpose-built for organizations where output quality is a regulatory or reputational concern, not a nice-to-have.

• Guided, no-code setup for non-technical domain experts
• Live API endpoints deploy evaluations to production pipelines
• Systematic error analysis to discover evaluation criteria from data
• Multi-model support: OpenAI, Anthropic, Google, any LiteLLM provider
• SME review queue with frozen config snapshots for audit provenance

Best for: Regulated industries (healthcare, finance, legal, education) where domain experts must define and validate quality standards.

2. Arize Phoenix

Built entirely on OpenTelemetry with no proprietary tracing layer, so instrumentation is portable and not vendor-locked. Backed by substantial funding (including a $70M Series C in 2025), with enterprise customers including Uber, Booking.com, PepsiCo, and Duolingo.

• Free self-hosting via Docker, Kubernetes, or AWS CloudFormation with no feature restrictions
• SOC 2 Type II, HIPAA, GDPR compliance with US/EU/CA data residency
• LDAP authentication with group-based role mapping and TLS encryption
• OpenTelemetry-native: vendor-agnostic, portable instrumentation
• Alyx AI copilot for trace troubleshooting and prompt optimization (AX tier)

Best for: Organizations that require fully self-hosted, air-gapped deployment with zero vendor lock-in.

3. LangSmith

A strong enterprise ecosystem for organizations using LangChain and LangGraph. Offers a wide range of deployment options: cloud SaaS, hybrid BYOC (data plane in customer VPC), and fully self-hosted via Kubernetes. Available on AWS Marketplace for streamlined enterprise procurement. The best experience remains with LangChain and LangGraph pipelines.

• Three deployment modes: cloud, hybrid BYOC, and fully self-hosted
• HIPAA, SOC 2 Type 2, GDPR compliance with SSO/SAML and SCIM provisioning
• AWS Marketplace availability for enterprise procurement workflows
• 400-day extended trace retention for audit and compliance evidence
• Polly AI assistant for in-app trace analysis and debugging

Best for: Large organizations already in the LangChain ecosystem that need F500-grade deployment flexibility.

4. Braintrust

A practical middle ground for data sovereignty. Braintrust's hybrid self-hosting model runs the data plane in the customer's AWS, GCP, or Azure environment while the control plane (UI, metadata) stays in Braintrust's cloud. The browser connects directly to the customer's data plane via CORS, so customer data never flows through Braintrust infrastructure. Per-organization pricing with unlimited users on all tiers.

• Hybrid self-hosting: customer data stays in customer VPC
• SOC 2 Type II and HIPAA compliance with AES-256 API key encryption
• Per-organization pricing with unlimited users (no per-seat scaling)
• AWS Marketplace listing for enterprise procurement
• Marquee customers: Stripe, Notion, Instacart, Zapier, Dropbox

Best for: Teams that need data sovereignty without the operational overhead of full self-hosting.

5. Weights & Biases Weave

The broadest compliance certification set on this list: SOC 2 Type II, ISO 27001, ISO 27017, ISO 27018, HIPAA, NIST 800-53, and GDPR alignment. Now under CoreWeave following the 2025 acquisition, which provides infrastructure backing but introduces product roadmap uncertainty. Dedicated single-tenant cloud is available across AWS, GCP, and Azure. A notable gap for some teams: Weave-specific BYOB constraints on some managed plans.

• SOC 2, ISO 27001/27017/27018, HIPAA, NIST 800-53, GDPR compliance
• Dedicated single-tenant cloud with IP allowlisting and private connectivity
• Self-managed deployment via Kubernetes with Helm charts (licensed)
• SSO via Google, GitHub, Okta, Azure AD with SCIM provisioning and PII redaction
• Widely adopted across enterprise and research AI teams

Best for: Organizations where compliance certification breadth is the primary vendor selection criterion.

6. Comet Opik

Apache 2.0 licensed with no restrictions, making it a permissive open-source option for enterprise deployment. Self-hostable via Docker (one-command setup) or production Kubernetes with Helm charts. Architecture uses ClickHouse for analytics, designed for high-volume trace workloads. All paid plans include unlimited team members, and the $19/month Pro tier is the cheapest on this list.

• SOC 2, ISO 27001, ISO 9001, HIPAA, and GDPR compliance
• Apache 2.0 license with no managed-service restrictions
• Production Kubernetes deployment with ClickHouse analytics backend
• 2-hour enterprise support response SLA
• Unlimited team members on all plans (no per-seat pricing)

Best for: Organizations that want permissive open-source licensing with enterprise compliance certifications.

7. DeepEval by Confident AI

One of the more metrics-dense frameworks on this list (50+ built-in evaluation metrics) with SOC 2 and HIPAA support on higher tiers. Enterprise tier includes dedicated on-premises deployment on AWS, Azure, or GCP with custom data residency options. The trade-off: Confident AI is a newer company with a shorter public enterprise track record compared to the more established platforms above. Python-only.

• SOC 2, HIPAA, and GDPR support on higher tiers
• Dedicated on-prem deployment on AWS, Azure, or GCP (enterprise tier)
• Custom data residency: US, EU, Canada, Australia, Japan
• 50+ evaluation metrics including 6 agent-specific and 5 RAG-specific
• Native Pytest integration for CI/CD evaluation pipelines

Best for: Python-first teams that need broad metric coverage with flexible data residency options.

How to choose

The right tool depends on your deployment constraints, who needs to be involved in defining quality, and where your organization sits on the build-vs-buy spectrum.

• Choose Truesight when domain experts in regulated industries need to define and validate quality standards, and evaluation must produce evidence for compliance and stakeholder review.
• Choose Arize Phoenix when fully self-hosted, air-gapped deployment with zero feature gates is a requirement and OpenTelemetry portability matters.
• Choose LangSmith when your organization needs F500-grade deployment flexibility across cloud, hybrid, and self-hosted models within the LangChain ecosystem.
• Choose Braintrust when data sovereignty is critical but you want to avoid the operational overhead of full self-hosting.
• Choose W&B Weave when compliance certification breadth (SOC 2, ISO, NIST, HIPAA) is the primary criterion for vendor selection.
• Choose Comet Opik when permissive open-source licensing and production-scale self-hosting at the lowest price point are priorities.
• Choose DeepEval when you need the broadest set of off-the-shelf metrics with flexible data residency options for a Python-first team.

Disclosure: Truesight is built by Goodeye Labs, the publisher of this article. We've aimed to provide a fair and accurate comparison based on each platform's documented capabilities as of February 2026.