Best LLM Evaluation Platforms in 2026: 6 Compared
Gartner now tracks this category as AI evaluation and observability platforms. This guide compares six of them across evaluator depth, red teaming, production observability and whether the results become audit evidence — organized by buyer type, not a fake ranking, with every competitor claim cited to its own docs.
Alejo HernandezCTO, KosmoyAn LLM system reaches production on a demo and a handful of manual checks; then the questions start. Did quality regress when the model changed? Is the RAG answer grounded or invented? Can the assistant be talked into something it must never do? LLM evaluation platforms exist to answer those questions with numbers instead of vibes — running a system over datasets, scoring the outputs, and watching the same signals in production. In February 2026 Gartner named the category in its first Market Guide for AI Evaluation and Observability Platforms, defining it as tooling that automates evals to benchmark outputs on quality, fairness and accuracy, then feeds production observability back into those evals.
This guide compares six platforms that do that job, and it separates them on the line that decides architecture: whether a product is a place to author and run evaluations, or a platform where evaluation is one capability inside a broader system that also enforces the fix. The pure-play eval and observability specialists — Arize, LangSmith, Langfuse, Braintrust — own the first. They are the deepest evaluation tools on this page, and this guide says so plainly. Kosmoy sits in the second group: its evaluation and red-teaming suite is newer and does not out-tool the specialists on experiment workflow, but it is wired to the gateway and guardrails that run the system, and it produces the compliance evidence a regulated buyer needs.
One more thing the 2026 market forces you to weigh: independence. Since late 2025 the category has consolidated hard — Galileo into Cisco, Promptfoo into OpenAI, Langfuse into ClickHouse, Weights & Biases into CoreWeave, Lakera into Check Point. Gartner makes model-agnosticism a defining requirement of the category precisely to avoid lock-in; who owns your evaluation vendor is now part of the decision.
What counts as LLM evaluation platforms in 2026
What counts as an LLM evaluation platform in 2026? Gartner's category — AI evaluation and observability platforms — sets the bar: automated evals that benchmark outputs against quality, fairness and accuracy expectations, using deterministic scorers where a ground truth exists and LLM-as-a-judge where it does not; observability that captures logs, metrics and traces from a multi-step agent down to a single request; and a feedback loop that turns production traces into evaluation datasets. The strongest tools add dataset and experiment management, agent-trajectory evaluation and RAG-specific metrics such as groundedness.
Two capabilities separate the field. The first is red teaming — adversarial testing that measures not how well a system performs but whether it can be made to misbehave, mapped to the OWASP LLM Top 10 and expected by the EU AI Act (Articles 15 and 55) and the NIST AI RMF. Some platforms treat this as core (Giskard, Kosmoy); others do not attempt it (Braintrust, LangSmith, Langfuse). The second is enforcement: whether a finding stays a report or becomes a runtime control. Almost every specialist stops at the report — the fix is a ticket for another system. A gateway-native platform can close the loop.
Gartner also makes model-agnosticism a requirement, to keep the category from becoming a lock-in layer. That framing matters more in 2026 than it did in 2025, because several of these platforms are now owned by a hyperscaler, a model lab or a database company. Ownership is noted per vendor below where it bears on a recommendation.
How we scored the field
Every product is scored 0–10 on the same ten capability axes. A 10 is reserved for categorical architectural facts; specialists are expected to outscore platforms on their own spoke, and the scores show it.
Testing, Evals & Red-teaming
Evaluation and red-teaming depth: breadth of evaluators, LLM-as-judge, datasets and experiments, agent-trajectory and RAG evaluation, and adversarial testing mapped to OWASP. This is the primary axis for this guide.
Observability & FinOps
Production tracing and monitoring: capturing logs, metrics and traces from multi-step agents to single requests, online evaluation of live traffic, and the feedback loop back into datasets.
Guardrails & Runtime Safety
Whether evaluation connects to runtime protection — inline checks that block or regenerate on unsafe input or output — versus scoring alone.
Compliance & Audit
Whether evaluation results become audit evidence mapped to a framework (EU AI Act, ISO/IEC 42001, NIST AI RMF), scored above the vendor's own SOC 2 certificate.
Deployment Sovereignty
Where evaluation data and prompts live and who sees them. SaaS-only scores low; self-hostable open source and enterprise self-host score higher; no vendor control plane at all scores highest.
The field, scored
| Capability (0–10) | Kosmoy | Arize AI | LangSmith (LangChain) | Langfuse | Braintrust | Giskard |
|---|---|---|---|---|---|---|
| AI Inventory & Discovery | 9 | 1 | 2 | 0 | 1 | 1 |
| Security & Shadow AI | 8 | 3 | 3 | 1 | 1 | 6 |
| Observability & FinOps | 7 | 9 | 9 | 9 | 8 | 3 |
| Gateway & Policy Control | 8 | 1 | 5 | 0 | 3 | 0 |
| Guardrails & Runtime Safety | 8 | 6 | 4 | 1 | 1 | 2 |
| Agent Containment | 9 | 1 | 7 | 0 | 0 | 0 |
| Compliance & Audit | 9 | 3 | 4 | 3 | 3 | 5 |
| Testing, Evals & Red-teaming | 7 | 9 | 9 | 8 | 9 | 8 |
| Agent Building | 6 | 2 | 9 | 1 | 1 | 1 |
| Deployment Sovereignty | 10 | 8 | 9 | 9 | 7 | 8 |
Bold marks the highest score on each row. 10 is reserved for categorical architectural facts; specialists are expected to outscore platforms on their own spoke.
Capability shape, vendor by vendor
Each panel shows one vendor across the same ten axes. Read it as area: a specialist climbs on its own spoke and falls away on the rest; a platform holds the frontier. The dashed outline is Kosmoy for reference.
The vendors, by buyer type
No single 1-to-N ranking survives contact with a real shortlist — the right pick depends on who is buying. Each vendor below is labeled with the buyer it fits best.
Kosmoy
AI management platformBest when evaluation must enforce and prove control
A self-hosted control plane for enterprise AI: one inventory, one policy gateway, one audit trail and a containment sandbox for every model, agent and MCP server a company runs.
Kosmoy is the governance-led entry, and the trade is explicit. Its evaluation and red-teaming suite shipped in 2026 — 20 evaluators across six dimensions, offline regression and online production runs, agentic and RAG evaluation, and single- plus multi-turn red teaming with a policy-compliance judge, three scoring bands and per-case remediation. It is comprehensive, and it scores a 7 on our evals axis; the pure-play specialists above still lead on experiment tracking, annotation queues and prompt-playground depth, and this guide does not pretend otherwise.
What none of them do is what Kosmoy is built around: evaluation wired to enforcement. A red-team finding becomes an enforced guardrail on the same platform; online evaluation reads real traffic from the gateway with no separate instrumentation; and every run lands in the same evidence trail as the system's EU AI Act risk classification and NIST AI RMF bundle. It runs single-tenant in your own Kubernetes, air-gapped if needed — in production at Italy's central bank and banking regulator and Europe's largest defence and aerospace group. Buy a specialist for the engineering loop; buy Kosmoy when the requirement is control you can prove.
Strengths
- Four registries — AI systems, models, MCP servers and a master agent registry that pulls agents from Azure AI Foundry, Bedrock, Vertex, Salesforce and ServiceNow into one list.
- One OpenAI-compatible gateway enforcing guardrails, RBAC, budgets and logging on every LLM, MCP and A2A call.
- Action Capsule: kernel-enforced sandboxing for agents, MCP servers and private models, with per-task credentials and a kill switch.
Limits
- Evaluation and red teaming shipped in mid-2026 — the suite is comprehensive but newer than the pure-play eval platforms, which still lead on experiment tracking, annotation queues and prompt playgrounds.
- The agent builder covers governed internal use cases; dedicated agent-development platforms go deeper.
- No free or self-service tier — procurement runs through an enterprise sales process.
Arize AI
AI observability & evaluation platform (Arize AX + Phoenix OSS)Best enterprise eval + observability with self-hosting
Arize AI pairs the enterprise Arize AX platform (agent observability, evaluation and runtime guards, SaaS or self-hosted) with Phoenix, one of the most active open-source AI observability projects.
Arize is the enterprise scale player: the Arize AX platform (online evals, an evaluator hub with versioned LLM-as-judge evaluators, runtime Guards, and the Alyx copilot) paired with the Apache-2.0 Phoenix open-source project, which passed 2M monthly downloads. A $70M Series C (February 2025) and a Kubernetes-first self-hosted enterprise tier make it the observability specialist most credible in a large, mixed ML-and-LLM estate.
Its documented runtime Guards pull it slightly past pure observability, but it is still an SDK-instrumented tool, not a gateway: guards run inside the application, not at a traffic chokepoint with routing and RBAC. It documents no org-wide AI inventory, no agent containment and no EU AI Act / ISO 42001 / NIST AI RMF tooling as of July 2026 — it out-evaluates Kosmoy and Kosmoy out-governs it.
Strengths
- Dual offering few rivals match: enterprise Arize AX plus the open-source Phoenix project (ELv2, ~10.6k stars, 749 releases), still shipping weekly as of July 2026.
- Deep evaluation stack: an Evaluator Hub with commit-level versioning of LLM-as-a-judge evaluators, datasets and experiments, and online evals on production traffic (Observe 2026 launches).
- Documented runtime Guards — embedding-based and RAG LLM guards on inputs and outputs with block, default-response or regeneration actions (guardrails docs) — rare among observability specialists.
Limits
- No org-wide AI inventory or shadow-AI discovery documented as of July 15, 2026 — projects and spaces exist only inside the platform.
- No LLM gateway or central runtime policy point: guards intercept calls at SDK level inside the application, not at a traffic chokepoint with routing and RBAC.
- No agent containment — sandboxing, kill switch or scoped credentials are not documented as of July 15, 2026.
LangSmith (LangChain)
LLM observability, evals & agent engineering platformBest for evaluating and shipping agents
LangSmith is LangChain's commercial platform for agent engineering — tracing, evaluation, prompt management, agent deployment, sandboxes and a no-code agent builder, plus an LLM gateway in private beta — layered on the MIT-licensed LangChain and LangGraph frameworks.
LangSmith is the agent engineering platform from the LangChain team — observe, evaluate and deploy, with the deepest agent-trajectory evaluation in this comparison and an Insights Agent that auto-classifies production behavior. Backed by a $125M Series B at a $1.25B valuation (October 2025) and the gravitational pull of ~35% of the Fortune 500 using LangChain products, it is the default for teams already in that ecosystem.
The trade-offs are ecosystem gravity and pricing shape: per-seat plus per-trace billing scales awkwardly across non-engineering stakeholders, and like the other specialists it does no red teaming, has no gateway or guardrails, and produces no EU AI Act / ISO 42001 / NIST AI RMF evidence. It is the strongest agent-eval tool here, not a governance platform.
Strengths
- The deepest ecosystem gravity in the category: LangChain (~141.8k stars) and LangGraph (~37.3k stars) are MIT frameworks feeding the commercial platform, backed by a $125M Series B at a $1.25B valuation (October 2025).
- Category-leading evaluation tooling: datasets with splits, experiments and pairwise comparison, LLM-as-judge, code and composite evaluators, online and multi-turn thread evaluators, and annotation queues with rubrics (evaluation docs).
- Framework-agnostic observability with native OpenTelemetry ingestion, automatic token/cost tracking with per-model pricing, dashboards and alerts (observability docs).
Limits
- The LLM Gateway is private beta (waitlist) with a narrow policy surface — spend limits plus PII/secrets redaction across 7 providers; no routing, failover or fine-grained content policies documented as of July 15, 2026.
- No org-wide AI inventory or shadow-AI discovery — visibility covers applications instrumented with LangSmith or routed through its gateway.
- No EU AI Act, ISO/IEC 42001 or NIST AI RMF governance tooling documented as of July 15, 2026; the compliance story is security certifications (SOC 2 Type II, ISO 27001, HIPAA, GDPR) plus audit logs.
Langfuse
Open-source LLM engineering platform (tracing, evals, prompts)Best open-source, self-hostable eval platform
Langfuse is an open-source (MIT-core) LLM engineering platform for tracing, evaluation and prompt management, self-hostable or on Langfuse Cloud, acquired by ClickHouse in January 2026 with public commitments to keep the license, roadmap and self-hosting unchanged.
Langfuse is the open-source default: an MIT-core LLM engineering platform — OTel-native tracing, LLM-as-judge evals, prompt management, datasets and a playground — self-hostable in an afternoon, which is why it reports 26M+ monthly SDK installs and use across a majority of the Fortune 500. For teams that want data sovereignty on an open-source base, it is the first stop.
It is an engineering tool, not a governance or security one: no red teaming, no runtime guardrails, no gateway, no inventory and no compliance evidence beyond its own certifications. Note the ownership change — Langfuse was acquired by ClickHouse in January 2026 — which is a strength for scale and a consideration for teams that prized its independence.
Strengths
- One of the most widely adopted open-source LLM engineering platforms: MIT core with ~31.2k GitHub stars and daily active development as of July 2026.
- Self-hosting without asterisks on the core: the self-hosted build runs the exact same codebase as Langfuse Cloud with all core features and APIs unlimited, and the networking docs state it does not require internet access.
- Deep evaluation tooling: LLM-as-a-judge, code evaluators, datasets, first-class Experiments with CI/CD quality gates in GitHub Actions (May 2026), and human annotation queues (changelog).
Limits
- Observe-only: no gateway, runtime policy enforcement or guardrail blocking — Langfuse's own docs delegate runtime security to third-party libraries and position the product as ex-post evaluation.
- No org-wide AI inventory, shadow-AI discovery or agent containment documented as of July 15, 2026.
- Key governance features are Enterprise-licensed when self-hosting (audit logs, retention policies, project-level RBAC, SCIM, server-side masking), and EE license telemetry cannot be disabled.
Braintrust
Evals-first LLM engineering & observability platformBest eval-authoring workflow for AI product teams
Braintrust is an evaluation-centric platform for building AI products — eval suites, production trace logging on the purpose-built Brainstore, playgrounds and the Loop AI assistant — with a hybrid self-hosted data plane for enterprises.
Braintrust is evaluation as a first-class engineering discipline. Its code-first `Eval()` primitive, dataset and experiment management, prompt playground and production online scoring make it the tightest build-eval-ship loop on this page for teams shipping LLM products — the reason it counts Notion, Stripe and Zapier as customers and raised an $80M Series B at an $800M valuation in February 2026. On pure evaluation authoring it is as strong as anything here.
It is deliberately narrow: no red teaming, no runtime guardrails, no AI inventory and no compliance-evidence tooling, and it is a US-cloud SaaS with a free tier and self-serve pricing. If your requirement is the best place to write and run evals, shortlist it; if it is proving control of AI to a regulator, it is not that tool.
Strengths
- A deep evals-first workflow — Eval() SDK, autoevals, LLM-as-a-judge and code scorers, datasets, experiments and human review — adopted widely among AI-native product teams (Series B blog).
- Brainstore, a store purpose-built for querying millions of large nested agent traces, with online scoring and Topics pattern clustering across production runs (June 2026).
- A hybrid architecture that keeps all trace, eval and dataset content in the customer's environment while retaining a managed UI (architecture docs).
Limits
- No runtime guardrails or policy gateway: the AI Proxy unifies access but does not enforce safety or policy on traffic, and scorers run asynchronously rather than blocking.
- No org-wide AI inventory, shadow-AI discovery or compliance-framework tooling documented as of July 15, 2026.
- Self-hosting is data-plane only and gated to Enterprise; the control plane always runs at Braintrust, so there is no full on-prem or air-gapped deployment.
Giskard
Open-source AI testing & red-teaming (EU)Best EU-native evaluation and red teaming
Giskard is a French/EU open-source testing library plus the Giskard Hub (enterprise) for LLM evaluation and continuous red teaming — hallucinations, prompt injection, bias and data leakage — anchored by the multilingual Phare safety benchmark.
Giskard is the European answer: an Apache-2.0 testing library (hallucination, prompt injection, bias, data-leakage detection) plus the Giskard Hub for continuous red teaming, with an LLM vulnerability Scan that generates adversarial suites from a plain-language description and the multilingual Phare safety benchmark. Part-funded by the European Commission and Bpifrance, it is the most AI-Act-native evaluation vendor on this page.
Its gaps are the same as the other specialists': no gateway or runtime policy point, no org-wide inventory or shadow-AI discovery, no agent containment, and lighter production observability than the tracing-first tools. For EU teams it is a natural pairing with a governance platform rather than a rival to one — and a credible open-source on-ramp to red teaming.
Strengths
- An open-source testing library (Apache-2.0) that surfaces hallucination, prompt injection, bias and data leakage in LLM and RAG applications — the OSS on-ramp few EU-native rivals offer.
- An LLM vulnerability Scan that generates adversarial test suites automatically from a plain-language description of the model, turning red teaming into a few lines of setup.
- The Giskard Hub (enterprise) layers continuous red teaming, annotation and scheduled scans on top of the OSS core, turning one-off tests into an ongoing safety loop.
Limits
- No LLM gateway or runtime policy point: Giskard tests and red-teams models, it does not sit in the traffic path enforcing policy on live calls.
- No org-wide AI inventory or shadow-AI discovery — assets exist as projects inside the tool, not an enterprise registry — as of July 31, 2026.
- No agent containment: no sandbox, kill switch or scoped credentials documented as of July 31, 2026.
Questions buyers ask
What is an AI evaluation and observability platform?
It is the category Gartner named in its February 2, 2026 Market Guide: tooling that automates evaluations ('evals') to benchmark AI outputs against quality, fairness and accuracy, captures observability data (logs, metrics, traces) from production, and feeds that data back into the evals to improve reliability. In practice a platform in this category offers evaluators (deterministic and LLM-as-judge), dataset and experiment management, production tracing, and increasingly agent-trajectory and RAG evaluation. Gartner projects adoption by software engineering teams rising from 18% in 2025 to 60% by 2028.
Which LLM evaluation platform is best?
There is no single best — it depends on the job. For the tightest build-eval-ship loop, Braintrust and LangSmith lead (LangSmith for agents specifically). For enterprise scale with a self-hosted option, Arize. For an open-source, self-hostable core, Langfuse. For EU-native red teaming, Giskard. For evaluation that must be wired to runtime enforcement and produce EU AI Act / NIST AI RMF evidence, Kosmoy. The eval specialists are deeper evaluation tools; Kosmoy is a governance platform with evaluation built in. Many enterprises run one of each.
Do these platforms do LLM red teaming?
Only some. Giskard and Kosmoy treat adversarial testing as a core capability — generating attacks, running single- and multi-turn jailbreaks, and mapping findings to the OWASP LLM Top 10. Arize offers runtime guards but not a red-team engine. Braintrust, LangSmith and Langfuse focus on evaluation and observability and do not offer native red teaming; teams that need it pair them with a red-teaming tool. If red teaming is a requirement — and the EU AI Act's Article 15 and Article 55 increasingly make it one — shortlist a platform that does it natively.
How does LLM evaluation help with EU AI Act compliance?
The EU AI Act expects providers of high-risk systems to test for accuracy and robustness (Article 15) and to run lifecycle risk management (Article 9), and its GPAI rules (Article 55) call for documented adversarial testing. Evaluation and red-teaming runs are how you produce that evidence. Most eval platforms generate metrics but do not map them to the framework; Kosmoy is the entry on this page that files evaluation results as EU AI Act, ISO/IEC 42001 and NIST AI RMF evidence from the same platform that classifies the system's risk. Note the timeline: after the May 2026 Digital Omnibus, high-risk obligations land in December 2027, with transparency obligations from August 2026.
Does it matter that some of these platforms were acquired?
It can. Gartner makes model-agnosticism a defining requirement of the category to avoid lock-in, and in 2025–2026 much of the field was acquired: Langfuse by ClickHouse, Weights & Biases by CoreWeave, Galileo by Cisco, Promptfoo by OpenAI, Lakera by Check Point. Acquisition can mean more resources and integration, or it can mean a roadmap steered toward the parent's stack. For teams standardizing evaluation across many model providers, an independent, model-agnostic, self-hostable platform is worth weighing on that basis alone — one reason Kosmoy's single-tenant, self-hosted model is part of its pitch.
Can I self-host an LLM evaluation platform?
Several here can. Langfuse (MIT core) and Giskard (Apache-2.0 library) are open-source and fully self-hostable; Arize AX offers a Kubernetes-first self-hosted enterprise tier alongside the open-source Phoenix project; Kosmoy is self-hosted only, single-tenant in your own Kubernetes and air-gap capable. Braintrust and LangSmith are primarily SaaS with enterprise options. If evaluation data and prompts must not leave your perimeter — common in regulated industries — start with the self-hostable options and confirm whether air-gapped operation is documented.
Methodology
Each vendor was scored on the ten capability axes used across Kosmoy's comparison pages, from primary sources — vendor documentation, pricing pages, engineering blogs and repositories — checked in July 2026 and cited inline or in each vendor's profile. This guide weights the five evaluation-relevant axes above. Scores of 10 are reserved for categorical architectural facts, and a specialist always outscores Kosmoy on its own spoke: Arize, LangSmith and Braintrust each score 9 on evaluation, Langfuse and Giskard 8, and Kosmoy 7. Kosmoy does not top the axis this guide is named for, and that is stated plainly.
Numbers from vendors appear as attributed claims with citations, never as our measurements. Category framing follows Gartner's February 2, 2026 Market Guide for AI Evaluation and Observability Platforms; the 18%→60% adoption figure is Gartner's strategic planning assumption. Ownership changes that bear on a recommendation — Langfuse/ClickHouse, and the wider 2025–2026 consolidation of the category — are flagged where they matter.
Disclosure: Kosmoy publishes this guide. The mitigation is structural — Kosmoy wins exactly one of the five buyer picks, the eval specialists take the engineering segments outright, the rubric concedes that four of the six platforms out-evaluate Kosmoy on pure eval tooling, and the recommended pattern for most engineering teams is a specialist, not Kosmoy.
Sources
Every factual claim about another vendor on this page traces to that vendor's own published material or a named third-party source below.
- Gartner — AI Evaluation and Observability Platforms (Peer Insights market) — accessed July 31, 2026
- Arize — Phoenix open-source project — accessed July 31, 2026
- LangChain / LangSmith — Series B announcement — accessed July 31, 2026
- ClickHouse acquires Langfuse (January 2026) — accessed July 31, 2026
- Braintrust — Series B announcement — accessed July 31, 2026
- Giskard — Phare LLM safety benchmark — accessed July 31, 2026
- EU AI Act — Article 15 (accuracy, robustness, cybersecurity) — accessed July 31, 2026
- OWASP Top 10 for LLM Applications (2025) — accessed July 31, 2026
- Kosmoy Platform — accessed July 15, 2026
- Kosmoy AI Gateway — accessed July 15, 2026
- Kosmoy Action Capsule — accessed July 15, 2026
- Kosmoy AI Compliance — accessed July 15, 2026
- Kosmoy AI Evaluation & Red Teaming — accessed July 31, 2026
- Arize AX self-hosting docs — accessed July 15, 2026
- Arize AX guardrails docs — accessed July 15, 2026
- Observe 2026 / Arize AX launches (blog) — accessed July 15, 2026
- Arize AX release notes — accessed July 15, 2026
- Arize pricing — accessed July 15, 2026
- Series C press release ($70M) — accessed July 15, 2026
- LangSmith self-hosted overview (docs) — accessed July 15, 2026
- LangSmith self-hosted egress & air-gapped licensing (docs) — accessed July 15, 2026
- LangSmith LLM Gateway (docs, private beta) — accessed July 15, 2026
- LangSmith Sandboxes (docs) — accessed July 15, 2026
- LangSmith Fleet overview (docs) — accessed July 15, 2026
- LangSmith Deployment overview (docs) — accessed July 15, 2026
- Interrupt 2026 launches (LangChain blog) — accessed July 15, 2026
- LangSmith pricing — accessed July 15, 2026
- Fortune — LangChain raises $125M at $1.25B valuation — accessed July 15, 2026
- Langfuse GitHub repository (MIT core, stars) — accessed July 15, 2026
- Self-hosting overview (same codebase as Cloud) — accessed July 15, 2026
- Enterprise license key (EE feature list, MIT core unlimited) — accessed July 15, 2026
- Networking (no internet access required) — accessed July 15, 2026
- Telemetry (EE license telemetry cannot be disabled) — accessed July 15, 2026
- Security & guardrails doc (runtime blocking delegated to third parties) — accessed July 15, 2026
- Langfuse joins ClickHouse (acquisition announcement) — accessed July 15, 2026
- Langfuse pricing — accessed July 15, 2026
- Changelog (Experiments, CI/CD gates, Monitors & Alerts) — accessed July 15, 2026
- Platform architecture docs (hybrid data plane) — accessed July 15, 2026
- Plans and limits — accessed July 15, 2026
- AI Proxy repository (MIT) — accessed July 15, 2026
- SiliconANGLE — Braintrust $80M Series B (Feb 2026) — accessed July 15, 2026
- Braintrust pricing — accessed July 15, 2026
- Giskard website — accessed July 31, 2026
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