Opik Alternatives in 2026: 6 LLM Eval and Observability Tools

You are probably here because Opik already covers the eval and observability surface, and you want to compare alternatives on price, OSS posture, gateway support, simulation, and ops footprint. This guide walks through six platforms that move teams off Opik in 2026, with honest tradeoffs on license, judge depth, and ops cost.

TL;DR: Best Opik alternative per use case

If you only read one row: pick FutureAGI when you need one stack, Langfuse when self-hosted observability is the main requirement, and DeepEval when your eval workflow lives in pytest. For deeper reads: see our LLM Evaluation Tools 2026, the evaluation platform docs, and the traceAI tracing layer.

Who Opik is and where it stops

Opik is Comet’s open-source LLM evaluation and observability tool, Apache 2.0, with Python and TypeScript SDKs and a docker-compose self-host. The interesting attribute is the built-in library of LLM-as-judge metrics with carefully written prompts. The product covers tracing, evaluations, datasets, experiments, prompt management, online scoring, a CLI, and integrations with LangChain, LlamaIndex, Bedrock, OpenAI, and OTel. Comet hosts a managed plane separate from the broader Comet MLOps suite.

Opik pricing is two-track. The OSS edition is free and self-hosted via docker compose. Opik Cloud has a free tier and an Opik Pro Cloud plan at $19 per month with higher limits. Comet MLOps for experiment tracking and model registry is a separate product with its own pricing. The pricing page has both surfaces; verify the current plan structure before signing.

Be fair about what Opik does well. The judge prompts are carefully written, the trace UI is clean, datasets and experiments map cleanly to Comet, and the docker-compose self-host is genuinely lightweight compared to ClickHouse-based alternatives. If your data science team already uses Comet, Opik fits in the same plane.

The honest gap is product maturity and scope. Opik now ships an Opik LLM Gateway (beta) and self-hosted Guardrails covering PII, topic, and custom checks, but the production-gateway breadth, simulated-user product, and routing/cache/provider depth are not at the level of a unified production stack. The OpenTelemetry path exists but is not as deep as Phoenix or FutureAGI traceAI. Adoption is strongest where Comet is already in use; outside that, the SDK and concept model can feel disconnected from the rest of the stack.

The 6 Opik alternatives compared

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

Open source. Self-hostable. Hosted cloud option.

Opik is a tracing-and-eval workbench. FutureAGI extends that scope to simulation, the gateway, prompt optimization, and guardrails inside one Apache 2.0 platform. The pitch is a single trace tree, a single eval contract, a single prompt registry, and a single policy engine, with an opinionated handoff between simulation, evals, observability, and CI gates. The repo is Apache 2.0.

Architecture: traceAI is the OSS instrumentation library that emits OpenTelemetry GenAI semantic-convention spans across Python, TypeScript, Java, and C#. The eval engine attaches scores as span attributes. The Agent Command Center gateway emits its own spans. Simulated runs against synthetic personas use the same evaluator that judges production. Plumbing under it (Django, React/Vite, Postgres, ClickHouse, Redis, object storage, workers, Temporal) supports the eval-and-tracing layer plus the gateway and the guardrail surface.

Pricing: FutureAGI starts at $0/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, unlimited prompts, unlimited dashboards, 3 annotation queues, 3 monitors, unlimited team members, and unlimited projects. Usage after the free tier starts at $2/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, and Enterprise starts at $2,000 per month.

Best for: Pick FutureAGI when the eval workbench should share a product surface with simulation, gateway, and guardrails. The buying signal is teams running Opik for evals plus Langfuse or Phoenix for traces plus a separate gateway, watching the three drift in production.

Skip if: Skip FutureAGI if your immediate need is the lightest docker-compose eval workbench with no other moving parts. Opik is closer to that shape. FutureAGI also has more services to self-host.

2. Langfuse: Best for OSS-first observability with prompts and datasets

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

Langfuse is the strongest OSS-first Opik alternative when the requirement is observability with prompt management, datasets, evals, and human annotation in the same UI. The trade is licensing precision: most code is MIT, but enterprise directories ship under a separate commercial license.

Architecture: Langfuse covers tracing, prompt management, evaluation, datasets, playgrounds, human annotation, public APIs, and OTel ingestion. The self-hosting docs require Postgres, ClickHouse, Redis or Valkey, object storage, workers, and application services.

Pricing: Langfuse Cloud Hobby is free with 50,000 units. Core is $29 per month with 100,000 units, $8 per additional 100,000 units. Pro is $199 per month. Enterprise is $2,499 per month.

Best for: Pick Langfuse if you need self-hosted tracing, prompt versioning, datasets, eval scores, annotation queues, and OTel compatibility, and your data science team is fine working outside Comet.

Skip if: Skip Langfuse if your eval workflow depends on the specific judge prompts in Opik or your platform team treats Comet as the system of record.

3. 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 your platform team treats OpenTelemetry and OpenInference as first-class. Trace inspection is the focus, and the OpenInference semantic conventions are documented in detail.

Architecture: Phoenix is built on OpenTelemetry and OpenInference. It accepts OTLP and ships auto-instrumentation for LlamaIndex, LangChain, DSPy, Mastra, Vercel AI SDK, OpenAI, Bedrock, Anthropic, Python, TypeScript, and Java. The repo uses Elastic License 2.0.

Pricing: Phoenix self-hosted is free. AX Free includes 25,000 spans per month. AX Pro is $50 per month with 50,000 spans. AX Enterprise is custom.

Best for: Pick Phoenix if your OTel and OpenInference path matters more than Comet integration. It pairs well with Python and TypeScript eval code.

Skip if: The catch is licensing. Phoenix uses Elastic License 2.0; in a security review, list it as source available, not OSI open source.

4. Braintrust: Best for hosted closed-loop eval

Hosted closed-source platform. Enterprise hosted and on-prem options.

Braintrust is the closest hosted alternative when your Opik usage is mostly evals, prompts, datasets, online scoring, and CI gates, and you do not need source-level backend control.

Architecture: Braintrust covers tracing, logs, dashboards, human review, datasets, prompt management, playgrounds, experiments, remote evals, online scoring, functions, the Braintrust gateway, monitoring, automations, and self-hosting for enterprise buyers. Recent changelog work includes Java auto-instrumentation in May 2026.

Pricing: Starter is $0 per month. Pro is $249 per month. Enterprise is custom.

Best for: Pick Braintrust if your biggest problem is closing the loop from production traces to datasets, scorer runs, prompt changes, and CI checks without owning a self-hosted stack.

Skip if: Skip Braintrust if open-source backend control is a hard requirement.

5. LangSmith: Best if your runtime is LangChain

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

LangSmith is the lowest-friction Opik alternative for LangChain and LangGraph teams.

Architecture: LangSmith covers Observability, Evaluation, Deployment through Agent Servers, Prompt Engineering, Fleet, Studio, and CLI. The January 16, 2026 self-hosted v0.13 release added the Insights Agent, revamped Experiments, IAM auth, mTLS for external Postgres, Redis, and ClickHouse, KEDA autoscaling, and IngestQueues.

Pricing: Developer is $0 per seat per month with 5,000 base traces. Plus is $39 per seat per month with 10,000 base traces.

Best for: Pick LangSmith if you use LangChain or LangGraph heavily.

Skip if: Skip LangSmith if open-source backend control is non-negotiable.

6. DeepEval: Best for pytest-style LLM unit tests

Open source. Confident AI hosting paid.

DeepEval is Apache 2.0 and ships a Python framework for unit-testing LLM apps with G-Eval, DAG, RAG, agent, and conversational metrics. If your eval workflow lives inside pytest and CI, DeepEval is closer to that shape than Opik.

Architecture: DeepEval is a pytest-style framework with Faithfulness, Answer Relevance, Knowledge Retention, Role Adherence, Tool Correctness, and Conversation Completeness metrics. Confident AI is the hosted plane for dataset management, run history, and team workflows.

Pricing: DeepEval OSS is free. Confident AI hosting is paid; verify the current plan model on the docs site.

Best for: Pick DeepEval if your evals run in pytest and CI is the source of truth.

Skip if: Skip DeepEval if you need a UI dashboard for non-engineering reviewers, or if you need a gateway, simulation, or guardrails in the same product.

Decision framework: Choose X if…

• Choose FutureAGI if your dominant workload is unified eval, observability, simulation, gateway, and guardrails. Buying signal: multiple point tools drift. Pairs with: OTel, OpenAI-compatible HTTP, BYOK judges.
• Choose Langfuse if your dominant workload is OSS observability with prompt management. Pairs with: custom scorers and CI eval jobs.
• Choose Phoenix if your dominant workload is OTel and OpenInference based tracing. Pairs with: Python and TypeScript eval code.
• Choose Braintrust if your dominant workload is hosted closed-loop eval. Pairs with: prompt playgrounds and CI gates.
• Choose LangSmith if your dominant workload is LangChain or LangGraph. Pairs with: LangGraph deployment and Fleet.
• Choose DeepEval if your dominant workload is pytest-style LLM tests. Pairs with: GitHub Actions and Confident AI hosting.

Common mistakes when picking an Opik alternative

• Treating “judge metric library” as a fixed feature. Each vendor’s library covers slightly different failure modes. Validate parity by running the same dataset against each candidate.
• Overlooking license differences. Opik is Apache 2.0; Phoenix is Elastic License 2.0; LangSmith and Braintrust are closed. The license affects security review.
• Ignoring trace contract drift. If you split tracing and evals across two platforms, lock attribute names, span IDs, timing fields, and cost fields.
• Pricing only the platform fee. Real cost is platform fee plus seats plus judge tokens plus storage plus on-call hours.
• Skipping the multi-step eval. Final-answer scoring misses tool selection, retries, retrieval misses, and loop behavior.

What changed in the LLM eval landscape in 2026

How to actually evaluate this for production

1. Run a domain reproduction. Export real traces with failures, long-tail prompts, tool calls, retrieval misses, and hand-labeled outcomes.

2. Lock the trace contract. Trace IDs, span IDs, OpenTelemetry GenAI semantic-convention attributes, span IDs, attribute names, and cost fields must agree across candidate and source.

3. Cost-adjust for your span volume. Real cost is span volume times retention times seats times judge sampling rate times storage times on-call hours.

How FutureAGI implements LLM evaluation and observability

FutureAGI is the production-grade evaluation-plus-observability platform built around the closed reliability loop that Comet Opik alternatives stitch together by hand. The full stack runs on one Apache 2.0 self-hostable plane:

• Tracing, traceAI (Apache 2.0) auto-instruments 35+ frameworks across Python, TypeScript, Java (LangChain4j and Spring AI), and a C# core, with OpenInference-shaped spans landing in ClickHouse-backed storage.
• Evals, 50+ first-party metrics (Faithfulness, Hallucination, Tool Correctness, Task Completion, Plan Adherence, Conversation Relevancy) attach as span attributes; BYOK lets any LLM serve as the judge at zero platform fee, and turing_flash runs the same rubrics at 50 to 70 ms p95 with full templates at about 1 to 2 seconds.
• Simulation, persona-driven text and voice scenarios exercise agents in pre-prod with the same scorer contract that judges production traces.
• Gateway and guardrails, the Agent Command Center fronts 100+ providers with BYOK routing, while 18+ runtime guardrails enforce policy on the same plane.

Beyond the four axes, FutureAGI also ships six prompt-optimization algorithms that consume failing trajectories as training data. 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 Comet Opik alternatives end up running three or four tools in production: one for traces, one for evals, one for the gateway, one for guardrails. FutureAGI is the recommended pick because tracing, evals, simulation, gateway, and guardrails all live on one self-hostable runtime; the loop closes without stitching.

Sources

• Comet Opik repo
• Comet pricing
• Opik docs
• FutureAGI pricing
• traceAI repo
• Langfuse pricing
• Langfuse self-hosting docs
• Phoenix docs
• Phoenix repo
• Braintrust pricing
• Braintrust changelog
• LangSmith pricing
• LangSmith Self-Hosted v0.13
• DeepEval repo
• DeepEval site

Series cross-link

Next: Langfuse Alternatives, Best LLM Evaluation Tools 2026, DeepEval Alternatives