Galileo Alternatives in 2026: 5 LLM Eval Platforms Compared

You are probably here because Galileo already looks credible, especially if you ship into a regulated industry and care about Luna online scoring at production volume. The question is whether it is the right control plane for your next LLM release, or whether you need open-source deployment, lower observability cost, framework-native LangChain ergonomics, gateway-first request control, or pre-production simulation that Galileo does not own. This guide strips the category down to what a production team should verify: price shape, license, hosting model, eval depth, OTel fit, and what each vendor will not solve for you.

TL;DR: Best Galileo alternative per use case

If you only read one row: pick FutureAGI when you need the full reliability loop in open source, Langfuse when self-hosted observability is the hard constraint, and LangSmith when your application is already centered on LangChain or LangGraph.

Who Galileo is and where it falls short

Galileo positions itself as an observability, evaluation, and production guardrail platform for GenAI and agentic applications. The current surface, per galileo.ai and docs.galileo.ai, centers on Agent Reliability, Insights for failure analysis, Luna-2 evaluators for production-scale scoring, and Protect for real-time guardrails. Eval categories cover RAG, agent, safety, and security with custom evaluators. CI/CD integration, Python and TypeScript SDKs, and integrations across major LLM providers and agent frameworks are documented.

Be fair about what Galileo does well. Luna-2 is a real differentiator. Galileo lists Luna-2 at $0.02 per 1M tokens, 152 ms latency, 0.95 reported accuracy, and a 128k max token window on its evaluator benchmarks, with 10 to 20 metric heads scored in parallel under 200 ms on L4 GPUs. That math matters when you want online scoring on every production trace without paying frontier judge rates per call. None of the five alternatives in this comparison ships a first-party fleet of small evaluator models with the same depth.

Galileo’s enterprise positioning is also genuinely strong. Real-time guardrails, dedicated inference servers for Luna, VPC and on-prem deployment, SSO and RBAC, dedicated CSM, and forward-deployed engineering all sit on the Enterprise tier. The AutoTune feature shipped on April 2, 2026 for self-improving evaluators, plus OWASP-aligned agent security work published through April 2026, give Galileo a credible story for regulated buyers in financial services and healthcare. If your procurement requires that combination, do not switch lightly.

Pricing is easy to model at the small end. The Galileo pricing page lists Free at $0 per month with 5,000 traces and unlimited custom evals. Pro is $100 per month billed yearly with 50,000 traces, standard RBAC, advanced analytics, and dedicated Slack support. Enterprise is custom with unlimited traces, custom rate limits, deployment options, and 24/7 support.

Where teams look elsewhere is less about Galileo being weak and more about constraints. You may need an OSI open-source stack. You may need self-hosting outside the Enterprise tier. You may want simulated users and voice scenarios as part of pre-production. You may want an integrated gateway with budget and cache controls, not just an evaluator at the trace boundary. You may prefer OTel-first plumbing that sends spans to Datadog, Grafana, or Jaeger. Those are real reasons to compare alternatives.

The 5 Galileo alternatives compared

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

Open source. Self-hostable. Hosted cloud option.

Most tools in this list pick one job. Galileo does evaluation with Luna online scoring. Langfuse does observability. LangSmith does LangChain ergonomics. Helicone does request analytics. Phoenix does OTel-native tracing. 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, not in a separate dashboard. 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, you stitch this loop manually: export Luna scores, build a dataset, run an optimizer in a notebook, push the prompt, hope the next online eval still passes. Each step is a place teams drop the ball. The post-incident loop is what stops production failures from becoming next quarter’s same production failure.

Architecture: what closes, not what ships. The public repo is Apache 2.0 and self-hostable, and the platform is shaped so every handoff is a versioned object rather than a manual export. Simulate-to-eval: simulated traces against personas and edge cases are scored by the same evaluator that grades production, so a failed persona run becomes a labeled dataset row, not a screenshot. Eval-to-trace: 50+ metrics including groundedness and hallucination attach as span attributes, so the failure lives next to the bad retrieval or the wrong tool call. Trace-to-optimizer: failing spans feed six prompt-optimization algorithms with real production examples, not synthetic prompts. Optimizer-to-gate: the optimizer ships a versioned prompt that CI judges against the threshold the previous version held. Gate-to-deploy: only versions that hold the contract reach the OpenAI-compatible gateway, where 18+ runtime guardrails (PII, prompt injection, jailbreak, tool-call enforcement, output policy, refusal calibration, and more) and routing across 100+ providers enforce the same shape in production. Postgres, ClickHouse, Redis, object storage, workers, and Temporal are the plumbing; traceAI in Python, TypeScript, Java, and C# is the OTel surface.

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, and dashboards, 3 annotation queues, 3 monitors, and unlimited team members and projects. Usage after the free tier starts at $2 per GB storage (down to $1 per GB above 2 TB), $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 with HIPAA, and Enterprise starts at $2,000 per month with SOC 2 Type II.

Best for: Pick FutureAGI when production failures need to close back into pre-prod tests automatically. The buying signal is teams that have Galileo’s online scoring telling them something is wrong but no automated path from a failing Luna trace into a regression dataset, an optimized prompt, and a deploy gate that catches the same class next time. It is a good fit for RAG agents, voice agents, support automation, and BYOK LLM-as-judge teams that want to avoid platform markup on every judge call. If Luna-equivalent online scoring is the buying criterion, plug self-hosted small judge models behind the gateway and run them as evaluators on the same loop.

Skip if: Skip FutureAGI if your immediate need is a narrow SDK eval runner or a single tracing dashboard, or if Luna-2 grade online scoring at production traffic is the central buying criterion. The full open-source stack has more moving parts than LangSmith inside a LangChain app or Helicone for gateway logging. If you do not want to operate Docker Compose, ClickHouse, queues, and OTel pipelines, use the hosted cloud or pick a smaller point tool. Also be honest in procurement: FutureAGI’s enterprise reference list is smaller than Galileo’s regulated-industry roster, and our forward-deployed engineering motion is younger.

Turing models vs Luna-2

The honest head-to-head. FutureAGI ships its own family of managed judge models called Turing, exposed in the SDK as turing_flash, turing_small, and turing_large (see evaluation docs and the ai-evaluation repo). Turing Flash targets latency-sensitive screening for text and image inputs, Small balances cost and accuracy, and Large is the multimodal flagship that also handles audio and PDF.

Where Luna-2 wins: a flat $0.02 per 1M token price is genuinely cheaper than a per-call credit model once you exceed a few million daily judge calls, and 152 ms with parallel metric heads is hard to match for trace-by-trace online scoring. Galileo also publishes a 0.95 reported accuracy number against its own evaluator suite.

Where Turing is competitive: turing_flash hits 50-70 ms p95 on guardrail rails in our test suite, roughly 2-3x faster than Luna-2’s published 152 ms; multimodal coverage on turing_large is broader (audio and PDF, not just text); and the BYOK escape hatch means you are not locked into proprietary judge pricing. Published benchmarks are not directly comparable yet, so run a domain reproduction with your real traces before standardizing on either.

2. Langfuse: Best for self-hosted LLM observability

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

Langfuse is the strongest OSS-first Galileo alternative for teams that mainly need observability, prompt management, datasets, and evals, and want to inspect or operate the source. It has the deepest open-source mindshare in this list, strong docs, active releases, and a serious self-hosting story. If your CTO says “no closed-source SaaS for trace data,” Langfuse belongs in the first pass.

Architecture: Langfuse is an open-source LLM engineering platform for debugging, analyzing, and iterating on LLM applications. The product 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, and object storage. Integrations include Python and JavaScript SDKs, OpenTelemetry, LiteLLM proxy logging, LangChain, LlamaIndex, OpenAI, and other clients.

Pricing: Langfuse Cloud starts free on Hobby with 50,000 units per month, 30 days data access, and 2 users. Core is $29 per month with 100,000 units, $8 per additional 100,000 units (graduated down to $6 per 100,000 above 50M), 90 days data access, and unlimited users. Pro is $199 per month with 3 years data access, retention management, unlimited annotation queues, SOC 2 and ISO 27001 reports, and an optional Teams add-on at $300 per month. Enterprise is $2,499 per month with dedicated engineer and SLA. Self-hosting is free.

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 run the data plane. It is a strong pairing with existing CI eval harnesses and data warehouses where Langfuse becomes the LLM telemetry system of record without forcing the rest of the eval stack into one vendor.

Skip if: Skip Langfuse if your main gap is simulated users, voice evaluation, prompt optimization algorithms, or an integrated gateway and guardrail product. It can interoperate with adjacent tools, but you will stitch more. Read the license carefully before calling it “pure MIT” in procurement: the README states the repository is MIT licensed except for the ee folders, which are handled under separate enterprise terms.

3. Arize Phoenix: Best for OTel and OpenInference teams

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

Phoenix is a good Galileo alternative when your team wants open tracing standards, Arize credibility, and a path from local AI observability into a broader enterprise monitoring product. It is especially relevant if you already think in OpenTelemetry and OpenInference, or if you want traces, evals, datasets, experiments, and prompt iteration without buying the full Arize AX platform on day one.

Architecture: Phoenix is built on OpenTelemetry and OpenInference. The docs cover tracing, evaluation, prompt engineering, datasets, experiments, RBAC, API keys, data retention, and custom providers. It accepts traces over OTLP and ships auto-instrumentation for LlamaIndex, LangChain, DSPy, Mastra, Vercel AI SDK, OpenAI, Bedrock, Anthropic, Python, TypeScript, and Java. The Phoenix homepage describes it as fully self-hostable with no feature gates. Recent releases added provider tools in Playground and Prompts, REST API filter-based annotation deletion, TanStack AI middleware, named auth profiles in the CLI, and session annotation support.

Pricing: Arize lists Phoenix as free and open source for self-hosting, with trace spans, ingestion, projects, and retention user-managed. 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, and email support. AX Enterprise is custom with SOC 2, HIPAA, dedicated support, optional self-hosting, multi-region deployments, and Arize’s adb Data Fabric.

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 and want continuity. It is also a sensible lab for prompt and dataset workflows that need to stay close to Python and TypeScript client code, and for teams that want CLI-first ergonomics for prompts and traces.

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, real-time guardrail enforcement, simulated user testing across voice and text, or Luna-2 grade online scoring as a default. Those are not the center of gravity here.

4. 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. That matters if the production issue is provider routing, caching, p95 latency, cost attribution, user-level analytics, or fallback behavior, rather than dataset governance or Luna-grade online scoring.

Architecture: Helicone is an Apache 2.0 project for LLM observability and an AI Gateway. The docs describe 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, prompts, and prompt assembly.

Pricing: Helicone Hobby is free with 10,000 requests, 1 GB storage, 1 seat, and 7 days retention. Pro is $79 per month with unlimited seats, alerts, reports, HQL, 1-month retention, and usage-based pricing beyond included requests. Team is $799 per month with 5 organizations, SOC 2, HIPAA, and 3 months retention. Enterprise is custom with SAML SSO, on-prem deployment, custom MSA, 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. It is a strong first tool for teams that have live LLM traffic but no clean answer to “which users, prompts, models, and endpoints drove this p99 spike?” It pairs well with downstream eval tools when you do not want one platform owning everything.

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, not Luna-equivalent evaluator depth. On March 3, 2026, Helicone announced it had been acquired by Mintlify and that services would remain live in maintenance mode with security updates and bug fixes. Treat roadmap depth as something to verify before standardizing on it.

5. 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 Galileo 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 translating concepts into a new vendor model. As of March 19, 2026, Agent Builder is now LangSmith Fleet, signaling that LangChain is expanding from eval and observability into agent workflow products.

Architecture: LangSmith is framework-agnostic at the API layer, but its strongest path is inside the LangChain ecosystem. The docs cover observability, evaluation, prompt engineering, agent deployment, Fleet, Studio, CLI, and enterprise features. Enterprise hosting can be cloud, hybrid, or self-hosted, with data sitting in your VPC. Recent changelog entries cover baseline experiment pinning, Insights Agent scheduled reports, and Deep Agents v0.4 with pluggable sandbox support.

Pricing: LangSmith 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, and 50 Fleet runs. 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 (400-day retention) cost $5.00 per 1,000, additional Fleet runs cost $0.05 each, and deployment uptime is billed by the minute. LLM costs are billed separately by providers.

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. It pairs well with teams that already use LangGraph’s state model and need evals near the same developer workflow without rebuilding their agent abstraction.

Skip if: Skip LangSmith if open-source platform control is non-negotiable, if seat pricing makes cross-functional access expensive at the team or org level, or if your stack is a mix of custom agents, LiteLLM, direct provider SDKs, and non-LangChain orchestration. It can ingest non-LangChain traces, but the buying signal is strongest when LangChain is the runtime.

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 multiple point tools and still cannot reproduce production failures before release. Pairs with: OTel, OpenAI-compatible HTTP, BYOK judges, and self-hosted small judge models for online scoring.
• 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 own infrastructure. Pairs with: custom eval harnesses, LangChain, LlamaIndex, and data warehouse exports.
• Choose Phoenix if your dominant workload is OTel and OpenInference based tracing with eval and experiment workflows. Buying signal: you already use Arize, or your platform team cares about instrumentation standards more than vendor UI polish. Pairs with: Python and TypeScript eval code, Phoenix Cloud, and Arize AX.
• 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 everywhere. Pairs with: OpenAI-compatible clients, provider failover, and downstream eval platforms.
• 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. Pairs with: LangGraph deployment, Fleet, and Prompt Hub.

Common mistakes when picking a Galileo alternative

• Over-indexing on Luna parity. If 1% of traffic is high-stakes, you may not need Luna-2 grade scoring on 100% of traces. Sample, then escalate. Span sampling, async judges, and retrieval-aware spot checks often cover the same ground for less.
• Treating OSS and self-hostable as the same thing. FutureAGI, Langfuse, Helicone, and Phoenix all have self-hosted paths, but their licenses and operational footprints differ. Check license, telemetry posture, enterprise gates, and backup story before picking.
• 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 break observability quietly when nobody is updating instrumentation.
• Ignoring multi-step agent eval. Final-answer scoring misses tool selection, retries, retrieval misses, and conversation drift. Require trace-level, session-level, and tool-call-aware evaluation when your agent does more than one call.
• Pricing only the subscription. Real cost is subscription plus trace volume, score volume, judge tokens, test-time compute, retries, storage retention, annotation labor, and the infra team. A cheap plan with expensive judge calls is not a cheap plan.
• 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. If Galileo Insights or AutoTune is doing real work for you, plan for how that capability gets rebuilt.

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. If Luna parity is on your list, score the same traces with Luna-2, a frontier judge, and a self-hosted small judge behind a gateway, and compare agreement and cost. Do not accept a vendor demo dataset.

2. Measure reliability under load. Build a Reliability Decay Curve. The x-axis is concurrency or trace volume, the 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. Run it for at least one week of representative traffic, not a 10-minute load test.

3. Cost-adjust. Real cost equals platform price plus trace volume, token volume, test-time compute, judge sampling rate, retry rate, storage retention, and annotation hours. A cheap 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. A Luna-style cheap online score can win on cost and lose on agreement with your domain experts. Price all three together.

How FutureAGI implements the Galileo replacement loop

FutureAGI is the production-grade LLM evaluation platform built around the simulate-evaluate-observe-optimize loop this post tested every Galileo alternative against. The full stack runs on one Apache 2.0 self-hostable plane:

• Evaluation layer - 50+ first-party metrics (Groundedness, Answer Relevance, Tool Correctness, Hallucination, PII, Toxicity, Task Completion) ship as both span-attached scorers and CI gates. The Turing family covers the Luna-2 use case directly: turing_flash runs guardrail screening at 50 to 70 ms p95 and full eval templates at about 1 to 2 seconds, with BYOK on top so any LLM can sit behind the evaluator at zero platform fee.
• Tracing layer - traceAI is Apache 2.0 OTel-based and auto-instruments 35+ frameworks across Python, TypeScript, Java (LangChain4j, Spring AI), and C#. The trace tree carries the same Luna-style online scores Galileo attaches, plus tool-call accuracy, retrieval misses, and planner depth as first-class span attributes.
• Simulation layer - persona-driven synthetic users exercise voice and text agents against red-team and golden-path scenarios before live traffic ever sees them. Every simulated trace is scored by the same evaluator that judges production, so a failed persona run becomes a row in the dataset.
• Optimization layer - six prompt-optimization algorithms consume failing trajectories as labelled training data and ship versioned prompts that the CI gate evaluates against the same threshold the previous version held.

Beyond the four axes, FutureAGI also ships the Agent Command Center gateway across 100+ providers with BYOK routing, and 18+ runtime guardrails (PII, prompt injection, jailbreak, tool-call enforcement) on the same plane. Pricing starts free with a 50 GB tracing tier; Boost is $250 per month, Scale is $750 per month with HIPAA, and Enterprise from $2,000 per month with SOC 2 Type II.

Most teams comparing Galileo alternatives end up running three or four tools in production: one for evals, one for traces, one for the gateway, one for guardrails. FutureAGI is the recommended pick because the Luna-style judge layer, the simulation layer, the gateway, and the guardrails all live on one self-hostable runtime; the loop closes without stitching.

Sources

• Galileo pricing
• Galileo product homepage
• Galileo Luna evaluators
• Galileo docs
• FutureAGI pricing
• FutureAGI GitHub repo
• FutureAGI changelog
• Langfuse pricing
• Langfuse GitHub repo
• Arize pricing
• Phoenix release notes
• Phoenix GitHub repo
• Helicone pricing
• Helicone GitHub repo
• LangSmith pricing
• LangChain changelog

Series cross-link

Related reading: FutureAGI vs Galileo for LLM evaluation, Braintrust Alternatives, Arize AI Alternatives