Ranked comparison

AI Ops Platform: Production-Grade LLM Operations

Running LLMs in production requires more than an API call. You need routing, failover, cost tracking, and performance monitoring. Here are the best AI ops platforms ranked.

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Free preview, Starter for the Auto lane, Teams for manual GPT, Claude, and Gemini Pro access. Add-on credits kick in after included plan tokens are used.

Start on cheap auto-routed models first, then move up only when your workload truly needs premium manual control.

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Curated cheap model pool with no manual premium-model selection.
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Add-on credits only extend usage after included plan tokens are exhausted.
Evaluation criteria
Routing & failoverCost trackingLatency monitoringModel managementAlerting
1
LLMWiseLLMWise

Routing, failover, cost tracking, and multi-model orchestration in one API. Most teams end up stitching together 3-4 tools to get what LLMWise does out of the box. The tradeoff is less customization than a fully self-managed stack, but for most teams that is a good trade.

Automatic failover: 3 failures trigger rerouting to healthy providers near-instantlyReserve-and-settle cost tracking with per-request cost attributionOptimization engine analyzes historical data and recommends routing changes
2
HeliconeHelicone

Excellent observability layer for LLM traffic. Strong logging, cost tracking, and dashboard analytics. Does not do routing or failover - it observes, not orchestrate.

One-line proxy integration - minimal code changesDetailed request logging with latency and cost breakdownsGood alerting on cost spikes and error rate anomalies
3
PortkeyPortkey

AI gateway with routing, caching, and guardrails. Closer to an orchestration layer than pure observability. Lacks ensemble modes (blend, judge) and data-driven optimization.

Virtual keys for team-level API key managementSemantic caching reduces redundant LLM callsGuardrails for content filtering and compliance
4
Weights & BiasesW&B

The gold standard for ML experiment tracking, now expanding into LLM ops with Weave. Best for teams already in the W&B ecosystem who want to add LLM tracing alongside traditional ML workflows.

Deep integration with ML training pipelinesTrace visualization for multi-step LLM chainsStrong team collaboration features
5
LangSmithLangChain

Purpose-built for LangChain applications. Excellent tracing for complex chains and agents. Less useful if you are not in the LangChain ecosystem.

First-class LangChain and LangGraph integrationDataset management for evaluation and testingPrompt versioning and A/B testing
6
BraintrustBraintrust

Strong evaluation and scoring platform. Focuses on output quality measurement rather than operational routing. Good complement to an orchestration layer, not a replacement.

Automated scoring with custom evaluation functionsPrompt playground with version comparisonCI/CD integration for regression testing on prompt changes
Evidence snapshot

AI Ops Platform: Production-Grade LLM Operations scoring method

Ranking evidence from practical criteria teams use for real production traffic.

Criteria
5
evaluation dimensions used
Models ranked
6
candidates evaluated
Top pick
LLMWise
current #1 recommendation
FAQ coverage
5
selection objections addressed
Our recommendation

If you need routing, failover, AND observability in one tool, LLMWise is the only option that does all three. If you already have routing handled and just need observability, Helicone is the lightest integration. Portkey sits in between - good routing with some observability. W&B and LangSmith are best when you need deep tracing for complex agent workflows. Braintrust is the pick for teams focused on evaluation and quality scoring.

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Common questions

What is AI ops?
AI ops (or LLMOps) is the practice of managing LLMs in production: routing requests to the right model, handling failures, tracking costs, monitoring latency, and optimizing performance over time. Think DevOps, but for AI model infrastructure.
How is AI ops different from MLOps?
MLOps covers the full ML lifecycle - training, versioning, deployment, monitoring. AI ops focuses specifically on the operational layer for pre-trained LLMs: routing, failover, cost management, and quality monitoring. You typically do not train the models yourself in AI ops.
What should an LLM operations platform include?
At minimum: multi-model routing, automatic failover, per-request cost tracking, latency monitoring, and error alerting. Advanced platforms add optimization recommendations, replay testing, and multi-model orchestration modes like blend and judge.
Do I need a separate AI ops tool?
If you are calling one model from one provider, probably not. The moment you use multiple models, need failover, or want cost visibility across providers, a dedicated AI ops layer saves engineering time and prevents outages.
What is the best AI ops tool in 2026?
LLMWise for teams that need routing + failover + observability in one tool. Helicone for pure observability. Portkey for gateway-style routing with guardrails. The right choice depends on whether you need orchestration or just monitoring.

Start on Auto, move up only when you need it

Free preview, Starter for the Auto lane, Teams for manual GPT, Claude, and Gemini Pro access. Add-on credits kick in after included plan tokens are used.

Start on cheap auto-routed models first, then move up only when your workload truly needs premium manual control.

Starter Auto laneTeams premium manual accessPlan tokens + add-ons
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