The 5 best LLMOps platforms in 2025

In 2024, everyone raced to add AI features to their products. In 2025, AI features are ubiquitous. LLMs are incredibly powerful out of the box, but raw capability isn't enough. The teams winning now are those who've mastered monitoring, optimizing, and tuning their AI features in production. They use LLM monitoring tools to catch issues before users notice, AI evaluation platforms to systematically test improvements, and AI testing tools to prevent quality regressions.

This creates opportunity. While competitors ship AI features and hope they work, teams with strong LLMOps measure what's actually happening, iterate based on data, and improve continuously. Better AI operations means faster shipping, more user feedback, and better products. Early movers capture this advantage.

LLMOps manages large language models through their full lifecycle: prompt engineering, systematic evaluation, deployment, monitoring, and continuous improvement. It's the missing layer that turns AI prototypes into production systems customers trust.

What is LLMOps?

LLMOps manages the complete lifecycle of large language models in production: from prompt engineering and evaluation to deployment, monitoring, and continuous improvement. It's MLOps adapted for foundation models like GPT, Claude, and LLaMA.

The line between feature and category tool is clear. Basic logging and ad-hoc testing are features. AI evaluation platforms provide systematic testing workflows. LLM monitoring tools offer production observability with full trace search. AI testing tools enable collaborative prompt management with versioning and automated regression detection.

The difference shows in outcomes. Teams shipping one AI feature can get by with logging. Teams building AI-native products need platforms that make quality assurance systematic.

Three trends define 2025's LLMOps landscape:

Evaluation-first development replaces "ship and pray" with systematic testing. AI evaluation platforms that catch regressions before production help teams achieve 30%+ accuracy improvements.

Observability beyond logs means full tracing of multi-step agent workflows and token-level cost attribution. Modern LLM monitoring tools provide semantic search across millions of production traces to debug non-deterministic failures.

Unified development workflows integrate prompt management, evaluation, and monitoring in single platforms. AI testing tools that combine these capabilities enable teams to flow from analyzing production logs to creating test cases to running evaluations to deploying improvements, all in one environment. Result: 10× faster iteration cycles.

Who needs LLMOps (and when)?

Startup (Seed to Series A)

You've shipped an AI feature users love, but you're spending more time debugging than building. Prompt changes get rolled back manually. You have no systematic way to test improvements. When quality drops, you can't explain why.

LLMOps opportunity: implement basic observability and evaluation to ship with confidence.

Scaleup (Series B+)

Multiple teams ship AI features, but prompts live in Notion docs. Cross-team prompt conflicts emerge. You can't compare model performance systematically. Production incidents trace back to untested changes.

LLMOps opportunity: centralized platforms enable collaboration and prevent regressions through automated testing.

Enterprise

AI features power mission-critical workflows. Compliance requires audit trails. Security reviews block deployments because you can't trace decisions to specific prompts or prove data handling meets HIPAA or SOC2.

LLMOps opportunity: enterprise platforms provide governance, security, and scale without sacrificing velocity.

Strong LLMOps creates:

• Competitive advantage through faster AI iteration
• Cost optimization via intelligent model selection and caching
• Quality assurance through systematic evaluation
• Team collaboration enabling non-technical stakeholders to improve AI

How we chose the best LLMOps platforms

We evaluated platforms across seven dimensions:

Evaluation capabilities: Depth of automated and human-in-the-loop evaluation, dataset management, regression testing workflows. How well do these AI evaluation platforms handle systematic testing?

Observability & tracing: Full-stack visibility into prompts, responses, costs, latency, and multi-step workflows. Can these LLM monitoring tools search millions of traces quickly?

Integration ecosystem: Support for major frameworks (LangChain, OpenAI, Anthropic) with minimal instrumentation code.

Production readiness: Performance at scale, reliability under load, security certifications (SOC2, HIPAA), self-hosting options.

Collaboration features: UI/UX enabling non-technical stakeholders, prompt versioning with rollback, team workflows.

Cost efficiency: Transparent pricing, built-in cost tracking, appropriate tiers for different team sizes.

Developer experience: Time from signup to first value, documentation quality, API-first design.

The tradeoffs matter. Specialized AI evaluation platforms go deeper on systematic testing. All-in-one solutions integrate AI observability with product analytics. Framework-specific tools provide seamless integration. Open-source platforms offer flexibility at the cost of managed convenience.

The 5 best LLMOps platforms in 2025

1. Braintrust

Quick overview

Braintrust makes evaluation the centerpiece of AI development. Used by Notion, Stripe, Vercel, Airtable, and Instacart, the platform proves that systematic testing as your primary workflow builds dramatically better AI products than production firefighting.

Best for: Teams building production AI applications needing evaluation-driven development with unified workflows from experimentation to production.

Pros

Evaluation as core workflow: Built around systematic testing, not observability with evaluation bolted on. Create datasets from production logs, run automated scorers, catch regressions before users see them. Customers report 30%+ accuracy improvements within weeks.

Loop AI agent: AI assistance built into every workflow. Loop analyzes production failures, generates evaluation criteria, creates test datasets, and suggests prompt improvements automatically.

Unified development flow: Production logs to test cases to evaluation runs to deployment, all seamlessly. Bidirectional sync between UI and code. Engineers maintain programmatic control while product managers contribute through intuitive interfaces.

Framework-agnostic: Native support for 13+ frameworks including OpenTelemetry, Vercel AI SDK, LangChain, LangGraph, Instructor, Autogen, CrewAI, and Cloudflare. Works out of the box.

Playground: Test prompts, swap models, edit scorers, run evaluations in browser. Compare results side-by-side. Makes experimentation accessible to non-technical team members.

Cons

Proprietary platform: Closed-source limits customization. Self-hosting only on Enterprise plan.

Evaluation-centric learning curve: Teams accustomed to observability-first tools need mindset shift toward systematic testing.

Pricing

• Free: Unlimited users, 1M trace spans/month, 1GB processed data, 10K scores/month, 14 days retention
• Pro: $249/month for unlimited users, unlimited spans, 5GB processed data, 50K scores, 1 month retention
• Enterprise: Custom pricing with self-hosting, premium support, SSO/SAML

Why it stands out

Braintrust's evaluation-first approach changes how teams build AI products. The Brainstore database enables debugging production across millions of traces in seconds. Loop automates hours of manual work creating test datasets and evaluation criteria. The unified workflow means shipping improvements without tool-switching.

Customers report 30%+ accuracy improvements and 10× faster development velocity. Notion, Stripe, and Vercel use Braintrust for their critical AI applications.

The platform serves both engineers (comprehensive APIs) and non-technical stakeholders (intuitive playground), enabling collaboration across teams.

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2. PostHog

Quick overview

PostHog combines LLM observability with product analytics, session replay, feature flags, and A/B testing in one platform. See how AI features impact user behavior and conversion alongside token costs. Roughly 10× cheaper than specialized platforms with a generous free tier.

Best for: Product teams needing LLM insights integrated with user behavior analytics, or teams wanting observability without dedicated tool budgets.

Pros

Product context for AI: See LLM performance alongside session replays and user properties. Trace user interactions with AI features to specific generations and costs.

Built-in A/B testing: Test prompts and models with statistical significance testing. Use one tool for AI and product experiments.

Cost advantage: First 100K LLM events free monthly, then usage-based pricing. No per-seat charges.

Cons

Technical focus: Requires technical expertise for setup. Non-technical team members may struggle with the interface.

Basic LLM features: Observability covers the essentials but lacks depth of specialized platforms. No prompt playground or advanced evaluation workflows.

Pricing

• Free tier: 1M analytics events, 5K replays, 1M feature flags, 100K LLM events monthly
• Usage-based: Pay only beyond free tier
• Self-hosting: Open-source (4 vCPU, 16GB RAM minimum)

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3. LangSmith

Quick overview

LangSmith is the observability platform from the LangChain team. Integration typically requires one line of code, with tracing built specifically for LangChain/LangGraph workflows.

Best for: Teams using LangChain or LangGraph needing framework-native integration and agent tracing.

Pros

LangChain integration: Add tracing with one environment variable. Every LangChain and LangGraph run automatically traces to your dashboard.

Agent tracing: Visualization for multi-step agent workflows with tool calls, reasoning steps, and nested spans.

Flexible retention: Choose 14-day base traces for debugging or 400-day extended traces for long-term analysis.

Cons

LangChain-centric: Best experience requires using LangChain. Other frameworks require more setup.

Self-hosting limited: Cloud-only for lower tiers. Self-hosted deployment requires Enterprise plan.

Pricing

• Developer: Free, 1 seat, 5K base traces/month
• Plus: $39/seat/month, 10K base traces/month, additional traces $5/10K (14-day) or $45/10K (400-day)
• Enterprise: Custom pricing with self-hosting, advanced security

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4. Weights & Biases (W&B)

Quick overview

Weights & Biases extends its MLOps platform into LLMOps with W&B Weave and LLM workflow support.

Best for: ML teams with existing W&B workflows extending to LLM applications, or teams needing experiment tracking infrastructure.

Pros

Experiment tracking: Real-time metrics, hyperparameter sweeps, and interactive visualizations. Track traditional ML and LLM workflows together.

W&B Inference: Hosted access to open-source models (Llama 4, DeepSeek, Qwen3, Phi)

Artifacts: Version and track prompts, datasets, embeddings, and models with lineage tracking.

Cons

Developing LLMOps features: Experiment tracking is mature, but LLM-specific features are less developed than dedicated platforms.

Pricing model: Tracked hours billing can become expensive for intensive workloads. Pricing structure is more complex than competitors.

Pricing

• Free: Personal projects only, unlimited tracking, 100GB storage
• Pro: Starting $50/user/month, team features, increased storage
• Enterprise: $315-400/seat/month with HIPAA compliance, SSO/SAML, audit logs

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5. TrueFoundry

Quick overview

TrueFoundry is a Kubernetes-native platform for DevOps teams managing LLM infrastructure at scale. Built for teams that need to control the infrastructure layer directly, it provides GPU-optimized model serving, fine-tuning pipelines, and AI Gateway across AWS, GCP, Azure, on-premises, or air-gapped environments.

Best for: DevOps and infrastructure teams managing LLM deployments at scale across multiple environments.

Pros

Kubernetes-native architecture: Built on Kubernetes for teams already managing container orchestration. Direct control over infrastructure configuration and scaling policies.

GPU infrastructure management: Integration with vLLM and SGLang for model serving. Automatic GPU autoscaling and resource provisioning across clusters.

Multi-environment deployment: Deploy across cloud providers, private VPCs, on-premises data centers, or air-gapped environments with consistent tooling.

Cons

Kubernetes expertise required: Assumes familiarity with Kubernetes operations. Teams without K8s experience face steep learning curve.

Infrastructure-first approach: Focused on infrastructure management rather than application-level workflows. Teams wanting managed simplicity may prefer other platforms.

Pricing

• AI Gateway: Free tier available, then $49/month (50K logs), $10/100K additional requests
• Platform: Per-developer pricing for smaller teams, enterprise pricing for larger deployments
• Enterprise: Custom pricing based on infrastructure requirements

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Summary comparison

Upgrade your LLMOps workflow with Braintrust → Start free today

Why you should choose Braintrust for LLMOps

The 2025 LLMOps landscape offers powerful tools for every use case, but Braintrust's evaluation-first philosophy represents a fundamental shift. While competitors treat evaluation as a feature bolted onto observability platforms, Braintrust makes systematic testing the foundation everything else builds upon.

The results are clear: customers consistently report 30%+ accuracy improvements within weeks and development velocity increases up to 10×. These aren't vanity metrics - they're competitive advantages that compound. When Notion, Stripe, and Vercel choose your platform for critical AI applications, it validates the approach.

Braintrust's custom Brainstore database changes what's possible when debugging production issues across millions of traces. Loop's AI-powered automation eliminates hours of manual work creating datasets and evaluation criteria. The unified workflow means teams ship improvements without context-switching between tools.

For organizations serious about production AI, the question isn't whether to implement LLMOps - it's whether to adopt evaluation-driven development or continue debugging production failures reactively. The early-mover advantage goes to teams who make systematic quality assurance their competitive edge.

FAQs

What is LLMOps?

LLMOps manages the complete lifecycle of large language models in production: prompt engineering, evaluation, deployment, monitoring, and continuous improvement. Unlike traditional software where tests are deterministic, LLMOps requires AI evaluation platforms and LLM monitoring tools to assess semantic correctness, measure hallucination rates, and track model drift. This enables teams to ship AI features with confidence and maintain quality at scale.

How do I choose the right LLMOps platform?

Identify your primary need: evaluation depth, observability coverage, or infrastructure management. Evaluation-focused teams should prioritize AI evaluation platforms like Braintrust with systematic testing workflows, while product teams benefit from LLM monitoring tools like PostHog that integrate analytics. Consider team composition, deployment requirements, and cost structure before committing.

Is Braintrust better than LangSmith for LangChain applications?

Both excel but serve different priorities. LangSmith offers seamless LangChain integration with one environment variable and agent visualizations built for LangGraph, ideal for debugging LangChain apps quickly. Braintrust excels at systematic evaluation and quality assurance across any framework, delivering 30%+ accuracy improvements through rigorous testing regardless of framework choice.

What's the difference between LLMOps and MLOps?

LLMOps extends MLOps to address unique challenges of large language models. While MLOps handles structured data and deterministic testing, LLMOps adds prompt engineering as code, semantic evaluation beyond accuracy metrics, and ethical safety monitoring. LLMs' non-deterministic nature requires different approaches: you can't simply check if output equals expected value.

If I'm successful with traditional ML, should I invest in LLMOps?

Yes, LLMs require different operational approaches than traditional ML models. Your MLOps expertise provides foundations, but LLMs introduce new challenges: non-deterministic outputs that can't be unit tested, prompts functioning as code requiring version control, and ethical considerations like bias amplification. Treating LLMs like traditional models typically results in production quality issues that erode user trust.

How quickly can I see results from implementing LLMOps?

Basic observability provides immediate visibility within hours: token costs, latency, and traces. Teams using evaluation platforms like Braintrust report measurable accuracy improvements within 2-4 weeks as systematic testing identifies issues manual spot-checking missed. Full cultural adoption where non-technical stakeholders contribute to evaluation datasets takes 3-6 months.

What's the difference between observability platforms and evaluation platforms?

LLM monitoring tools focus on what happened: capturing traces, logging costs, monitoring latency, and alerting on anomalies. AI evaluation platforms focus on whether it's good: running systematic tests, comparing outputs, measuring quality improvements, and preventing regressions. Most teams need both types of AI testing tools for production monitoring and quality assurance.

What are the best alternatives to LangSmith?

For LangChain users, Braintrust provides deeper evaluation capabilities with strong LangChain support through native integrations. For open-source self-hosting, Langfuse offers MIT licensing and complete data control for regulated industries. For product teams, PostHog provides integrated session replay and funnel tracking alongside LLM observability.