We need to monitor token cost and latency across several models — what should we use?

To monitor token costs and latency across multiple AI models effectively, you generally choose between two architectures depending on your needs: AI Gateways (which sit inline to actively manage, route, and cache traffic) or LLM Observability Platforms (which ingest telemetry for deep debugging, tracing, and analytics).

Top tools categorized by how they handle cost and latency tracking include:

• LiteLLM : Excellent for routing, load balancing, fallbacks, and real-time cost/latency tracking across 100+ providers via a unified OpenAI-compatible proxy. Check it out on LiteLLM.
• Portkey : A production-grade AI gateway featuring robust per-request latency tracking, granular cost analytics, automatic retries, and semantic caching to slash repeat expenses. Explore features on Portkey.
• Helicone : Open-source-friendly and purpose-built for LLM logging. It provides incredible real-time dashboards for cost-per-user, token counts, and latency bottlenecks with minimal setup. Learn more at Helicone.
• Cloudflare AI Gateway : Best if you want zero-fuss edge caching and rate limiting. It lets you proxy requests globally to drastically cut latency and repeated token costs on identical prompts. Visit Cloudflare AI Gateway.
• Datadog LLM Observability : Ideal if your engineering organization already lives in Datadog. It correlates token spend spikes and latency directly with your core infrastructure metrics, though it comes at a steeper enterprise price. Review details on Datadog LLM Observability.

To help narrow down the best fit, tell me:

• Do you need an inline proxy/gateway (to handle caching, load balancing, or failovers) or purely a monitoring/observability dashboard?
• Are you routing mostly via API providers (OpenAI, Anthropic) or self-hosted/open-source models?

If you’re comparing several LLM providers/models, I’d use a dedicated LLM observability layer rather than building this entirely from provider dashboards.

My default recommendation: Langfuse

langfuse.com is a strong default because it gives you:

• Per-request token counts — input, output, cached/reasoning tokens where available
• Cost by model/provider
• Latency — including request duration and trace-level timing
• Model comparisons across the same application/workflow
• Tracing for multi-step agents
• Prompt/version tracking and evaluations
• OpenTelemetry support and a self-hostable option

Recent comparisons still put Langfuse among the strongest general-purpose choices, particularly if you want observability plus evaluations rather than just a proxy that logs requests.

Other good choices

The important distinction is that observability answers “what happened?”, while evals answer “was the result good?”. For production model selection, you eventually want both.

What I'd instrument

For every model call, record at least:

provider
model
timestamp
request_id / trace_id
input_tokens
output_tokens
cached_input_tokens
reasoning_tokens (if applicable)
estimated_cost
time_to_first_token
total_latency
tokens_per_second
status / error

Then build dashboards around P50/P95/P99 latency, cost per request, cost per successful task, and quality score. Don't rely on average latency—OpenAI itself recommends percentile-based latency analysis and separating models/projects/tiers when diagnosing performance.

One particularly useful metric is cost per successful task, rather than simply cost per million tokens. A cheaper model that requires substantially more tokens or produces worse results may actually be more expensive operationally.

If you're only using OpenAI, you can start without another platform: the API returns token usage per request, and OpenAI's Usage Dashboard/API can break usage down by model and other dimensions.

So, for a multi-model stack: I'd start with Langfuse + OpenTelemetry, and add an eval framework/workflow once you need automated quality comparisons.

For monitoring token cost and latency across multiple LLMs, you’ll usually want an LLM observability layer rather than only traditional APM. The right choice depends on whether you need just cost/latency dashboards or deeper tracing, evals, and debugging.

Good options

LushBinary StackBriefly## My usual recommendation

For a multi-model stack (for example OpenAI + Anthropic + Google + self-hosted models):

1. Start with Langfuse or Phoenix

• Capture every model call: - model name/version
• input/output tokens
• estimated cost
• time-to-first-token (TTFT)
• total latency
• retries/errors
• prompt version
• user/application metadata

2. Use OpenTelemetry where possible

• This avoids locking your telemetry into one vendor.
• You can route traces into different backends later.

3. Add dashboards/alerts Track:

• Cost per request
• Cost per customer/team
• Tokens per workflow
• p50/p95/p99 latency
• Provider/model failure rates
• Unexpected model changes
• Prompt versions causing cost spikes

Minimal architecture

Your application
       |
       v
LLM instrumentation SDK
       |
       v
OpenTelemetry / tracing layer
       |
       +--> Langfuse / Phoenix / LangSmith
       |
       +--> Grafana / Datadog / existing dashboards

If you only care about cost + latency

Use a gateway approach:

• Put all model calls through a single LLM gateway/proxy.
• Record: - request ID
• provider
• model
• tokens
• latency
• cost

This is the quickest route to visibility. Helicone is designed around this style, while Langfuse and Phoenix provide broader tracing capabilities.

If you are running at scale

I would avoid tracking only aggregate metrics. You’ll eventually need per-trace visibility:

Example:

Customer request
 └── RAG retrieval       120 ms
 └── reranker            80 ms
 └── GPT-5.5 call        900 ms
      ├── input tokens   4,200
      ├── output tokens  850
      └── cost           $0.04
 └── tool call           300 ms

That lets you answer “why did this request cost 5× more?” instead of only “costs went up.”

A practical default:

• Small team: Langfuse
• Enterprise with OpenTelemetry/ML platform: Arize Phoenix
• Need visibility today with minimal code changes: Helicone
• Heavy agent workflows: LangSmith or Langfuse + eval tooling

Customer request
 └── RAG retrieval       120 ms
 └── reranker            80 ms
 └── GPT-5.5 call        900 ms
      ├── input tokens   4,200
      ├── output tokens  850
      └── cost           $0.04
 └── tool call           300 ms

That lets you answer “why did this request cost 5× more?” instead of only “costs went up.”

A practical default:

• Small team: Langfuse
• Enterprise with OpenTelemetry/ML platform: Arize Phoenix
• Need visibility today with minimal code changes: Helicone
• Heavy agent workflows: LangSmith or Langfuse + eval tooling

To monitor token cost, token counts (input/output/cached), and latency across multiple models, you should use an LLM Observability or Gateway platform.

Key Features to Look For

• Granular Token Breakdowns: Ensure the tool splits usage into prompt (input), completion (output), and cache-read tokens, as pricing and optimization strategies differ significantly for each.
• Span-Level Attribution: Look for tools that trace individual steps or agent tool-calls so you can see if latency or high token consumption stems from the LLM or a slow vector database search.
• Automated Cost Calculation: The platform should automatically map token counts against up-to-date provider pricing tables (OpenAI, Anthropic, Mistral, etc.) rather than requiring manual math.

If you can share whether you prefer an open-source/self-hosted tool or a managed cloud service , and whether you also need active routing/fallbacks or just monitoring/dashboards , I can recommend the exact platform that fits your stack.

To monitor token cost and latency across multiple LLM models, you should use an LLM Observability platform or an LLM Proxy/Gateway . These tools sit between your application and providers (like OpenAI, Anthropic, or AWS Bedrock) to automatically capture usage data, break down streaming metrics, and calculate multi-model costs in real-time.

🌟 Top Tool Recommendations

1. Dedicated LLM Observability (Best for Deep Insights)

These tools give you a full visual dashboard of cost, latency, and token splits per user, tenant, or workflow.

• Langfuse : An open-source, highly popular option. It breaks down costs by model, tracks tokens (including prompt caching), and monitors Time to First Token (TTFT) for streaming responses.
• Maxim AI : Great for agentic workflows. It has built-in cost management tables and lets you easily compare model efficiency across vendors.
• Arize Phoenix or LangSmith : Excellent for enterprise-grade tracing, letting you drill down into exactly which step or agent loop caused a spike in token costs.

2. LLM Gateways (Best for Real-Time Budget Control)

If you want to enforce strict budget caps and prevent runaway costs before they happen, use a central gateway.

• LiteLLM : A lightweight, open-source proxy that standardizes input/output formats for 100+ models. It includes a built-in management dashboard to set budget limits and track spend per API key.
• Portkey : A production-grade AI gateway designed to route requests across models while tracking latency, cost, and custom user metadata in real-time.

3. Standard APM Integration (Best for Existing Infrastructure)

• OpenTelemetry (OTel) : If you already use tools like Datadog, New Relic, or Dynatrace, you can configure OpenTelemetry LLM semantic conventions. This tracks token counts, prompt costs, and specific latency breakdowns on your existing engineering dashboards.

📊 Key Metrics You Need to Track

When evaluating your models, ensure your dashboards capture these three layers:

💡 Pro-Tips for Implementation

• Implement Streaming : Always track TTFT instead of just overall execution time. A model might take 5 seconds to finish, but if it streams the first word in 200ms, the user perceived experience is excellent.
• Don't Rely Solely on Vendor Billing : Dashboards from OpenAI or Anthropic reflect usage after a request completes. Real-time system controls (like LiteLLM or an observability SDK) are required to catch infinite agent loops before they empty your wallet.

To help narrow down the best setup for your team, please let me know:

• What programming language or framework is your application built with?
• Which specific models/providers (e.g., OpenAI, Anthropic, Bedrock, self-hosted) are you using?
• Do you prefer a managed SaaS solution, or do you need something open-source/self-hosted?

If you’re monitoring multiple LLMs/providers, I’d use an LLM observability layer rather than building this entirely from provider dashboards.

My pick: Langfuse

Langfuse is a strong fit for this use case because it tracks:

• Input/output tokens (and cached tokens where applicable)
• Cost per request/model
• Latency, with traces down to individual LLM calls
• Model/provider comparisons
• Dashboards and spend alerts
• Custom metadata such as customer, endpoint, feature, environment, etc.

It can infer costs from model pricing or accept usage/cost directly from the provider response, which is preferable for accuracy.

What I'd instrument

For every model call, record something like:

For latency, P50/P75/P95 are much more useful than averages. OpenAI's current guidance specifically recommends looking at TTFT, request time, and token velocity separately.

If you're only using OpenAI

You can start with OpenAI's native Usage Dashboard + API usage data. Token usage is available directly in API responses, and the dashboard supports granular usage analysis.

For a multi-model application, though, I'd still centralize the telemetry in Langfuse so you can answer questions like:

“Is model A actually cheaper once we account for its longer outputs and latency?”

rather than comparing provider dashboards separately.

Recommended stack:
Your app → model/provider → Langfuse → dashboards/alerts

If you tell me which models/providers you're comparing (e.g. OpenAI + Anthropic + Gemini), I can recommend the best monitoring stack and show the instrumentation architecture.

If you’re comparing several LLMs in production, I’d use an LLM observability layer rather than tracking this manually.

My default recommendation: Langfuse + OpenTelemetry

Langfuse is a strong fit because it can track:

• Input/output tokens, including cached and other token categories
• Cost per request, model, user, feature, or tag
• Latency and traces across multi-step LLM calls
• Model/provider comparisons
• Aggregated usage and cost through its Metrics API
• OpenTelemetry-based instrumentation, so you aren't locked into one vendor

I'd instrument every model call with roughly:

Important: don't compare models using average latency alone. Track P50/P95/P99 and TTFT, and correlate latency with output-token count; OpenAI's current guidance specifically recommends percentile-based analysis.

If you're already using Datadog

Keep Datadog for infrastructure/APM and send the same OpenTelemetry traces to Langfuse for LLM-specific analysis. That gives you one trace from HTTP request → application → retrieval/tools → model calls, while retaining your normal infrastructure monitoring.

If you're only using OpenAI

Start with the OpenAI Usage Dashboard + usage data in API responses; token usage is returned directly by the API, and the dashboard provides historical usage.

But once you're comparing OpenAI vs. Anthropic vs. Google vs. other providers, I'd move to a provider-neutral layer such as Langfuse/OTel so the metrics are normalized.

Bottom line: for a multi-model system, I'd choose OpenTelemetry → Langfuse, optionally fanning the same telemetry into Datadog. It gives you the cleanest way to answer “which model is cheapest, fastest, and actually performs best for each workload?”

To monitor token cost and latency across multiple LLM models, you should use an LLM Observability platform or an LLM Proxy/Gateway . These tools sit between your application and providers (like OpenAI, Anthropic, or AWS Bedrock) to automatically capture usage data, break down streaming metrics, and calculate multi-model costs in real-time.

🌟 Top Tool Recommendations

1. Dedicated LLM Observability (Best for Deep Insights)

These tools give you a full visual dashboard of cost, latency, and token splits per user, tenant, or workflow.

• Langfuse : An open-source, highly popular option. It breaks down costs by model, tracks tokens (including prompt caching), and monitors Time to First Token (TTFT) for streaming responses.
• Maxim AI : Great for agentic workflows. It has built-in cost management tables and lets you easily compare model efficiency across vendors.
• Arize Phoenix or LangSmith : Excellent for enterprise-grade tracing, letting you drill down into exactly which step or agent loop caused a spike in token costs.

2. LLM Gateways (Best for Real-Time Budget Control)

If you want to enforce strict budget caps and prevent runaway costs before they happen, use a central gateway.

• LiteLLM : A lightweight, open-source proxy that standardizes input/output formats for 100+ models. It includes a built-in management dashboard to set budget limits and track spend per API key.
• Portkey : A production-grade AI gateway designed to route requests across models while tracking latency, cost, and custom user metadata in real-time.

3. Standard APM Integration (Best for Existing Infrastructure)

• OpenTelemetry (OTel) : If you already use tools like Datadog, New Relic, or Dynatrace, you can configure OpenTelemetry LLM semantic conventions. This tracks token counts, prompt costs, and specific latency breakdowns on your existing engineering dashboards.

📊 Key Metrics You Need to Track

When evaluating your models, ensure your dashboards capture these three layers:

💡 Pro-Tips for Implementation

• Implement Streaming : Always track TTFT instead of just overall execution time. A model might take 5 seconds to finish, but if it streams the first word in 200ms, the user perceived experience is excellent.
• Don't Rely Solely on Vendor Billing : Dashboards from OpenAI or Anthropic reflect usage after a request completes. Real-time system controls (like LiteLLM or an observability SDK) are required to catch infinite agent loops before they empty your wallet.

To help narrow down the best setup for your team, please let me know:

• What programming language or framework is your application built with?
• Which specific models/providers (e.g., OpenAI, Anthropic, Bedrock, self-hosted) are you using?
• Do you prefer a managed SaaS solution, or do you need something open-source/self-hosted?

For monitoring token cost + latency across multiple LLM providers/models, you’ll usually want an LLM observability layer rather than only traditional APM. The key is to capture each model call with:

• model/provider name
• input/output tokens
• estimated and actual dollar cost
• time-to-first-token (TTFT)
• total latency
• throughput (tokens/sec)
• errors/retries
• user/app/workflow tags
• prompt/version metadata

Common choices:

A practical architecture I’d recommend:

Your app
  |
  |-- OpenTelemetry / LLM SDK instrumentation
  |
  +--> LLM observability backend
          |
          +--> dashboards
          +--> cost alerts
          +--> latency percentiles
          +--> per-model comparisons
          +--> trace debugging

For model comparisons specifically, create dashboards like:

Cost

• $ / 1K requests
• $ / user / day
• cost by feature
• cost by model
• input vs output token spend

Latency

• p50 / p95 / p99 latency
• TTFT
• generation tokens/sec
• provider error rate

Quality vs cost

• success rate
• human feedback score
• eval score
• cost per successful task

My default picks:

• Small/medium team, multiple providers: Langfuse + OpenTelemetry
• Already using LangChain: LangSmith
• Enterprise already running Datadog: Datadog LLM Observability
• Need portability: OpenTelemetry-first, then choose a backend

Also consider putting a gateway in front of models (for example, routing through a model proxy) if you want centralized accounting, fallback routing, and rate limits. That makes “which model cost us what?” much easier to answer.

To monitor token costs, latency, and performance across multiple AI models simultaneously, you should implement either an LLM Observability Platform or an AI Gateway.

These specialized tools sit between your application and your model providers, capturing token counts, exact spend, and timing metrics in real time.

Top Tools for Multi-Model Monitoring

Critical Metrics Your Setup Must Track

Ensure that whichever solution you choose logs these four operational data points for every single request:

• Token Breakdown : Separate input (prompt) tokens from output (completion) tokens, as output tokens are significantly more expensive across nearly all providers.
• Time-to-First-Token (TTFT) : Crucial for streaming responses to measure the perceived speed for your users.
• Total Request Latency : The end-to-end duration of the model call.
• Custom Metadata Tags : Unique identifiers to attribute costs by feature, user ID, environment (prod vs. dev), or team.

How to Choose Your Approach

1. Choose LiteLLM + Langfuse if you want an open-source, highly customized setup. LiteLLM acts as a single gateway wrapper, and Langfuse provides clean, out-of-the-box dashboards for costs and latency.
2. Choose Portkey or TrueFoundry if you want a fully managed, enterprise-ready AI gateway that can actively throttle users who exceed token budgets.
3. Choose Datadog or Honeycomb if your engineering team already relies heavily on them for regular application performance monitoring and wants a single pane of glass.

To help narrow down the ideal stack, tell me:

• What programming language or framework is your application built on?
• Which specific model providers (e.g., OpenAI, Anthropic, AWS Bedrock, Azure) are you using?
• Do you prefer a fully managed SaaS tool, or do you need an open-source / self-hosted solution?