Vellum Alternatives in 2026: 6 LLM Eval and Agent Platforms Compared

You are probably here because Vellum’s IDE-first model worked for the prompt-orchestration phase of the project, and now the team needs richer evals, span-level traces, agent-native ergonomics, gateway control, or open-source self-host. This guide is for teams looking past the visual prompt builder to the rest of the stack: where Vellum fits, where it falls short, and which alternatives close the gap. Each section is fair to Vellum where Vellum is good, and direct about where it is not.

TL;DR: Best Vellum alternative per use case

If you only read one row: pick FutureAGI when you need the agent reliability loop, Braintrust when hosted closed-loop evals are the constraint, and LangSmith when LangChain is the runtime. For deeper reads, see our Braintrust alternatives, LangSmith alternatives, and Langfuse alternatives for adjacent decisions.

Who Vellum is and where it falls short

Vellum is a prompt-orchestration platform that combines a visual prompt and workflow IDE with eval, deployment, and observability features. The product page lists prompt engineering, workflows, evaluations, deployment, and observability under one roof. The team has shipped enterprise features including agent workflows, RAG features, and a Python SDK. It is closed source.

The strengths are real:

• Visual prompt and workflow IDE. For product teams that prefer drag-and-build orchestration over code-first, Vellum is one of the cleanest IDEs in the category.
• Built-in deployment story. Prompts and workflows go from build to production without a separate deploy pipeline.
• Eval and dataset surfaces. Vellum supports eval runs against datasets, with a dashboard for results.
• Enterprise focus. SOC 2, HIPAA, and similar postures are well-covered.

Where teams start looking elsewhere:

• Closed source. Procurement teams that require OSI open-source self-host cannot use Vellum. The SDKs are open; the platform is not.
• Eval depth. The eval surface is workable but lighter than purpose-built tools like Braintrust, Phoenix, FutureAGI, or Langfuse. Span-attached scoring and dataset-centric workflows are deeper elsewhere.
• Agent-native ergonomics. As agents replaced single-prompt apps, the IDE-first model became less flexible than code-first patterns. LangChain, LangGraph, and custom agent runtimes often want trace-first observability, not workflow-first.
• Gateway and guardrail layer. Vellum is not a gateway. Gateway routing, caching, and inline guardrails live elsewhere.
• Cost shape at scale. Vellum’s public pricing docs describe prepaid Vellum Credits with top-ups; verify plan-level pricing in-app or with sales before standardizing on it.
• OTel and OpenInference. Vellum supports observability features but is not OTel-native in the way Phoenix and FutureAGI are.

Each gap is fixable, but each is a real reason to compare alternatives.

The 6 Vellum alternatives compared

1. FutureAGI: Best for unified eval + observe + simulate + optimize + gateway + guard

Open source. Self-hostable. Hosted cloud option.

Most tools in this list pick one job. Vellum does prompt orchestration. Braintrust does evals. Langfuse does observability. LangSmith does LangChain ergonomics. Phoenix does OTel-native tracing. Helicone does request analytics. FutureAGI does the loop. The loop runs in four stages. First, simulate against synthetic personas and replay real production traces in pre-production. Second, evaluate every output with span-attached scores so failures live on the trace. Third, observe live traffic with the same eval contract you used in pre-prod. Fourth, every failing trace is a candidate dataset for prompt optimization, the optimizer ships a versioned prompt, the gate enforces the new threshold, and the trace shape does not change. The closure matters because in every other architecture, including Vellum plus a separate observability tool plus a notebook, you stitch this loop manually. Each stitch is a place teams drop the ball.

Architecture: what closes, not what ships. The public repo is Apache 2.0 and self-hostable. Simulate-to-eval, eval-to-trace, trace-to-optimizer, optimizer-to-gate, gate-to-deploy: each handoff is a versioned object, not a manual export. The plumbing under it (Django, React/Vite, the Go-based Agent Command Center gateway, traceAI, Postgres, ClickHouse, Redis, object storage, workers, Temporal, OTel across Python, TypeScript, Java, and C#) exists so the five handoffs do not require glue code.

Pricing: FutureAGI starts at $0 per month. The free tier includes 50 GB tracing and storage, 2,000 AI credits, 100,000 gateway requests, 100,000 cache hits, 1 million text simulation tokens, 60 voice simulation minutes, unlimited datasets, prompts, dashboards, 3 annotation queues, 3 monitors, and unlimited team members. Usage after the free tier is $2 per GB storage, $10 per 1,000 AI credits, $5 per 100,000 gateway requests, $1 per 100,000 cache hits, $2 per 1 million text simulation tokens, and $0.08 per voice minute. Boost is $250 per month, Scale is $750 per month, Enterprise starts at $2,000 per month.

Best for: Pick FutureAGI when production failures need to close back into pre-prod tests. The buying signal is teams that have stitched Vellum with multiple point tools and watched the same incident class repeat because handoffs lost fidelity.

Skip if: Skip FutureAGI if your immediate need is a visual drag-and-drop prompt IDE for non-engineering team members. The full stack is code-first and SDK-first, with dashboards rather than a workflow builder. If a visual IDE is the constraint, keep Vellum and pair it with FutureAGI for the eval and observability layer.

2. Braintrust: Best for hosted closed-loop eval and prompt iteration

Closed platform. Hosted SaaS with Enterprise self-host.

Braintrust is the right alternative when the team wants a productized closed-loop eval workflow without operating the infrastructure. Its current docs list tracing, logs, topics, dashboards, human review, datasets, prompt management, playgrounds, experiments, remote evals, online scoring, functions, the Braintrust gateway, monitoring, automations, and self-hosting as part of the product surface.

Architecture: Braintrust ships a hosted eval and observability platform with strong dataset, scorer, and CI ergonomics. Tracing is OTel-compatible. The Loop AI assistant helps generate scorers and prompt improvements. Recent changelog entries show active work on Java auto-instrumentation, dataset snapshots, dataset environments, trace translation, cloud storage export, full-text search, subqueries, and sandboxed agent evals.

Pricing: Starter is $0 per month with 1 GB processed data, 10,000 scores, 14 days retention, and unlimited users. Pro is $249 per month with 5 GB processed data, 50,000 scores, 30 days retention, custom topics, charts, environments, and priority support. Overage is $4/GB and $2.50 per 1,000 scores on Starter, then $3/GB and $1.50 per 1,000 scores on Pro. Enterprise is custom and adds on-prem or hosted deployment.

Best for: Pick Braintrust when hosted closed-loop evals with dataset and CI ergonomics is the priority. Strong fit for teams that want a polished eval workflow and do not need open-source self-host.

Skip if: Skip Braintrust if open-source platform control is non-negotiable, if simulated voice users or an integrated guardrail product are required, or if your team has already standardized on a different observability backend.

3. Langfuse: Best for self-hosted observability with prompt management

Open source core. Self-hostable. Hosted cloud option.

Langfuse is the strongest OSS-first alternative for teams that want observability, prompt management, datasets, and evals together. It has the deepest open-source mindshare in this list, strong docs, active releases, and a serious self-hosting story.

Architecture: Langfuse covers observability, prompt management, evaluation, metrics, datasets, playgrounds, human annotation, and public APIs. The self-hosted architecture uses application containers, Postgres, ClickHouse, Redis or Valkey, object storage, and an optional LLM API or gateway. SDKs are Python and JavaScript, with OpenTelemetry, LiteLLM proxy logging, LangChain, LlamaIndex, and OpenAI integrations.

Pricing: Hobby is free with 50,000 units per month, 30 days data access, 2 users, and community support. Core is $29 per month with 100,000 units. Pro is $199 per month with 3 years data access, retention management, and SOC 2 and ISO 27001 reports. Enterprise is $2,499 per month.

Best for: Pick Langfuse if you need self-hosted tracing, prompt versioning, datasets, eval scores, human annotation, and OTel compatibility, and your platform team can operate the data plane.

Skip if: Skip Langfuse if your main gap is simulated users, voice evaluation, optimization algorithms, or an integrated gateway and guardrail product.

4. LangSmith: Best if you are already on LangChain

Closed platform. Open-source SDKs and frameworks around it. Cloud, hybrid, and Enterprise self-hosting.

LangSmith is the lowest-friction alternative for LangChain and LangGraph teams. If every agent run is already a LangGraph execution, LangSmith gives you native tracing, evals, prompts, deployment, and Fleet workflows without forcing the team to translate concepts into a new vendor model.

Architecture: LangSmith is framework-agnostic but strongest inside the LangChain ecosystem. Its docs cover observability, evaluation, prompt engineering, agent deployment, platform setup, Fleet, Studio, CLI, and enterprise features. Enterprise hosting can be cloud, hybrid, or self-hosted, with self-hosted data in your VPC.

Pricing: Developer is $0 per seat per month with up to 5,000 base traces per month, online and offline evals, Prompt Hub, Playground, Canvas, annotation queues, monitoring, alerting, 1 Fleet agent, 50 Fleet runs, and 1 seat. Plus is $39 per seat per month with up to 10,000 base traces, one dev-sized deployment, unlimited Fleet agents, 500 Fleet runs, and up to 3 workspaces. Base traces cost $2.50 per 1,000 after included usage; extended traces cost $5.00 per 1,000 with 400-day retention.

Best for: Pick LangSmith if you use LangChain or LangGraph heavily, want framework-native trace semantics, and plan to deploy or manage agents through LangChain products.

Skip if: Skip LangSmith if open-source platform control is non-negotiable, if seat pricing makes cross-functional access expensive, or if your stack is a mix of custom agents, LiteLLM, direct provider SDKs, and non-LangChain orchestration.

5. Arize Phoenix: Best for OTel and OpenInference teams

Source available. Self-hostable. Phoenix Cloud and Arize AX paths exist.

Phoenix is the right alternative when the team wants open tracing standards and a path from local AI observability into a broader Arize platform. It is especially relevant for teams already in OpenTelemetry and OpenInference, or teams that want traces, evals, datasets, experiments, and prompt iteration without buying the full Arize AX platform first.

Architecture: Phoenix is built on OpenTelemetry and OpenInference. Its docs cover tracing, evaluation, prompt engineering, datasets, experiments, RBAC, API keys, data retention, and custom providers. It accepts traces over OTLP and has auto-instrumentation for LlamaIndex, LangChain, DSPy, Mastra, Vercel AI SDK, OpenAI, Bedrock, Anthropic, Python, TypeScript, and Java. The Phoenix home page says it is fully self-hostable with no feature gates or restrictions.

Pricing: Phoenix is free to self-host and source-available under Elastic License 2.0. Arize markets Phoenix as open source; legal teams using OSI definitions will treat ELv2 as source available, not OSI open source. Arize AX Free includes 25,000 spans per month, 1 GB ingestion, and 15 days retention. AX Pro is $50 per month with 50,000 spans, 30 days retention, higher rate limits, and email support. AX Enterprise is custom.

Best for: Pick Phoenix if you want an OTel-native trace and eval workbench, you value open standards, or you already use Arize for ML observability.

Skip if: The catch is licensing and scope. Phoenix uses Elastic License 2.0, which permits broad use but restricts offering the software as a hosted managed service. Call it source available if your legal team uses OSI definitions. Also skip Phoenix if your main requirement is gateway-first provider control or simulated user testing.

6. Helicone: Best for gateway-first observability

Open source. Self-hostable. Hosted cloud option.

Helicone is the right alternative when the fastest path to value is changing the base URL, seeing every request, and controlling cost. It is gateway-first rather than eval-first.

Architecture: Helicone is an Apache 2.0 project for LLM observability and an AI Gateway. The docs show an OpenAI-compatible gateway across 100+ models, with provider routing, caching, rate limits, LLM security, sessions, user metrics, cost tracking, datasets, alerts, reports, HQL, eval scores, user feedback, prompts, and prompt assembly.

Pricing: Hobby is free with 10,000 requests, 1 GB storage, 1 seat, and 1 organization. Pro is $79 per month with unlimited seats, alerts, reports, and HQL. Team is $799 per month with 5 organizations, SOC 2, HIPAA, and a dedicated Slack channel. Enterprise is custom and includes SAML SSO, on-prem deployment, and bulk cloud discounts.

Best for: Pick Helicone if you want request analytics, user-level spend, model cost tracking, caching, fallbacks, prompt management, and a low-friction gateway.

Skip if: Helicone will not replace a deep eval platform by itself. It has eval scores, datasets, and feedback, but the center of gravity is gateway observability. On March 3, 2026, Helicone said it had been acquired by Mintlify and that services would remain live in maintenance mode with security updates, new models, bug fixes, and performance fixes. Treat roadmap depth as something to verify directly.

Decision framework: choose X if…

• Choose FutureAGI if your dominant workload is agent reliability across simulation, evals, traces, gateway routing, guardrails, and prompt optimization. Buying signal: your team has stitched Vellum with multiple point tools and still cannot reproduce production failures before release.
• Choose Braintrust if your dominant workload is hosted closed-loop eval and prompt iteration. Buying signal: your team wants a polished eval workflow without operating the infrastructure.
• Choose Langfuse if your dominant workload is LLM observability and prompt management under self-hosting constraints. Buying signal: you want to inspect the source, operate the stack, and keep trace data in your infrastructure.
• Choose LangSmith if your dominant workload is LangChain or LangGraph agent development. Buying signal: your team already debugs chains, graphs, prompts, and deployments in the LangChain mental model.
• Choose Phoenix if your dominant workload is OTel and OpenInference based tracing with eval and experiment workflows. Buying signal: your platform team cares about instrumentation standards more than vendor UI polish.
• Choose Helicone if your dominant workload is request logging, provider routing, caching, and cost analytics. Buying signal: your application has traffic now and changing the gateway URL is easier than adding SDK instrumentation.

Common mistakes when picking a Vellum alternative

• Confusing IDE coverage with eval coverage. A visual prompt builder is not an eval system. Pair the right tools.
• Treating OSS and self-hostable as the same thing. Phoenix is source available under Elastic 2.0. Langfuse non-enterprise paths are MIT. FutureAGI and Helicone are Apache 2.0. Procurement reads these differently.
• Picking by integration logos. Verify active maintenance for the exact framework version you use. LangChain v1, OpenAI Responses, Claude tool use, OTel semantic conventions, and provider SDK changes can break observability quietly.
• Ignoring multi-step agent eval. Final-answer scoring misses tool selection, retries, retrieval misses, loop behavior, and conversation drift. Require trace-level, session-level, and path-aware evaluation if your agent does more than one call.
• Pricing only the platform subscription. Real cost is subscription plus trace volume, score volume, judge tokens, test-time compute, retries, storage retention, annotation labor, and the infra team that runs self-hosted services.
• Assuming migration is just tracing. The hard parts are datasets, scorer semantics, prompt version history, human review queues, CI gates, and production-to-eval workflows.

What changed in the eval landscape in 2026

How to actually evaluate this for production

1. Run a domain reproduction. Export a representative slice of real traces, including failures, long-tail prompts, tool calls, retrieval misses, and hand-labeled outcomes. Instrument each candidate with your harness, your OTel payload shape, your prompt versions, and your judge model. Do not accept a demo dataset.
2. Measure reliability under load. Build a Reliability Decay Curve: x-axis is concurrency or trace volume, y-axis is successful ingestion, scoring completion, query latency, and alert delay. Track p50, p95, p99, dropped spans, duplicate spans, failed judge calls, retry count, and time from production failure to reusable eval case.
3. Cost-adjust. Real cost equals platform price times trace volume, token volume, test-time compute, judge sampling rate, retry rate, storage retention, and annotation hours. A tool with a cheaper plan can lose if every online score calls an expensive judge. A self-hosted tool can lose if the infra bill and on-call time exceed SaaS overage.

Sources

• Vellum docs
• Vellum pricing
• FutureAGI pricing
• FutureAGI GitHub repo
• Braintrust pricing
• Braintrust changelog
• Langfuse pricing
• Langfuse repo
• LangSmith pricing
• LangSmith repo
• Phoenix docs
• Phoenix repo
• Helicone pricing
• Helicone repo

Series cross-link

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