AI Cost Monitoring Platforms 2026: Helicone, Vantage, Datadog LLM
Most teams discover they have an LLM cost problem the same way: the cloud bill arrives, and there's a line item that's 3x what anyone expected. The model was cheap per token, the usage grew faster than anyone tracked, and now someone has to explain to finance why the "AI thing" costs more than the database.
Retroactive cost analysis is painful. Real-time cost monitoring is not optional for any production AI system that has grown beyond a handful of users.
This guide compares the three platforms that come up most when teams get serious about LLM cost monitoring: Helicone (purpose-built for LLM observability), Vantage (cloud cost management that's expanded into AI), and Datadog's LLM Observability product (for teams already on Datadog).
What LLM cost monitoring actually needs to show you
Before comparing tools, it's worth being specific about what data you need.
Per-request cost. Not just total cost, but what each individual API call costs. This lets you identify which requests are expensive, which patterns to optimize, and where cost-per-outcome analysis is possible.
Cost by dimension. You need to slice costs by user, by feature, by model, by time period, and by environment (production vs. staging). Without dimensional breakdowns, "your LLM costs $10,000/month" is a useless statement. "Your top 50 users generate 40% of your LLM costs" is actionable.
Real-time vs. billed cost. LLM provider billing often has a delay. Real-time token tracking is what you use for operational decisions (is cost spiking right now?). Billed cost reconciliation is what you use for accounting. You ideally want both.
Cost attribution to business events. Knowing that a specific user session cost $1.47 matters less than knowing that your "research report" feature costs $0.83 per report generated. Connecting LLM costs to product-level outcomes is the hardest part of cost monitoring and the most valuable.
Helicone
Helicone is a proxy-based observability platform specifically designed for LLM applications. You route your OpenAI or Anthropic API calls through Helicone's URL, and it logs everything: request, response, token counts, latency, model, and a full set of custom properties you can attach via headers.
Setup. For OpenAI, change the base URL from https://api.openai.com/v1 to https://oai.helicone.ai/v1 and add your Helicone API key as a header. That's it. No code changes beyond two configuration lines. You start seeing data within seconds of the first request.
For Anthropic, the same approach works: change the base URL and add the header. Helicone supports streaming responses and properly attributes token counts for streamed completions.
Cost dashboards. Helicone's default dashboard shows total cost over time, cost by model, requests per day, and latency distribution. The "Properties" feature lets you attach arbitrary key-value pairs to requests via headers:
headers = {
"Helicone-Property-UserId": user_id,
"Helicone-Property-Feature": "research-report",
"Helicone-Property-Environment": "production",
}Once you've instrumented these properties, you can slice your cost dashboard by any of them. Cost per user, cost per feature, production vs. staging, all of these become one-click filters.
Caching. Helicone has a built-in caching layer. Enable it for eligible requests (identical prompts, deterministic outputs), and Helicone serves cached responses without hitting the provider. It tracks cache savings separately so you can see exactly how much the caching is saving.
Pricing. Free tier: 100,000 requests per month. Pro: $20/month for 1 million requests. Teams: $200/month with more seats and advanced features. Self-hosted is available.
Where it falls short. Helicone is excellent for request-level cost tracking but doesn't have the multi-cloud or non-LLM cost context that Vantage provides. If you want to see your LLM costs alongside your database, compute, and storage costs in a single view, Helicone isn't the right tool for that.
Vantage
Vantage is a cloud cost management platform that started with AWS, GCP, and Azure and has expanded to include LLM provider costs. It connects directly to your cloud provider billing accounts and LLM provider billing APIs, pulling cost data daily.
Setup. For LLM cost tracking, Vantage connects to OpenAI's usage API and Anthropic's usage API (where available) to pull billing data. This is different from Helicone's proxy approach: Vantage reads billing data, not request data. This means you get billed costs, not real-time per-request costs.
What Vantage does well. The multi-cloud view is the value proposition. If your infrastructure spans AWS, some GCP services, and several LLM providers, Vantage shows all of it in a unified cost explorer. You can see that your AI workloads cost $8,000/month against your $40,000/month total cloud bill. For engineering managers and CTOs doing financial planning, this context is valuable.
Vantage's anomaly detection runs across your entire cloud spend. If your OpenAI costs spike 50% day-over-day, Vantage flags it alongside any other unexpected cost increases in your infrastructure. This cross-service anomaly detection is something neither Helicone nor Datadog's LLM product does naturally.
Pricing. Vantage's pricing is percentage-of-savings for cloud cost recommendations, plus a flat fee for the core platform. Starting at around $299/month for small teams. More for larger organizations. It's positioned as a cost optimization tool that pays for itself, not a cheap add-on.
Where it falls short. Since Vantage reads billing data rather than intercepting requests, you get daily cost data, not real-time. You can't use Vantage to alert on a cost spike that's happening right now. You also don't get the request-level breakdowns that Helicone provides.
Datadog LLM Observability
Datadog added LLM Observability to its product suite in 2023 and expanded it significantly through 2024 and 2025. If you're already running Datadog for APM, infrastructure monitoring, and logs, adding LLM Observability integrates into your existing dashboards and alert policies.
Setup. Integration uses Datadog's tracing SDKs. You wrap your LLM calls with Datadog's LLM Observability decorator or use the Auto Instrumentation feature which patches LLM SDK calls automatically for OpenAI, Anthropic, and LangChain.
from ddtrace.llmobs.decorators import llm
@llm(model_provider="anthropic", name="my_agent_call")
def call_claude(user_message: str) -> str:
response = anthropic_client.messages.create(
model="claude-3-5-sonnet-20241022",
messages=[{"role": "user", "content": user_message}],
)
return response.content[0].textThis automatically captures token usage, latency, model name, input/output pairs, and any custom metadata you add.
Cost dashboards. Datadog computes costs from token usage at current pricing (it maintains a pricing table for major models). The resulting cost metrics slot into Datadog's normal metric infrastructure: you can graph them, create monitors, set anomaly detection, and join them with any other metric in your system.
The big advantage here is joining LLM cost metrics with application metrics. You can create a dashboard that shows LLM cost alongside API traffic, database query time, and infrastructure cost. For engineering teams already doing this kind of multi-signal analysis in Datadog, this is genuinely more useful than a separate LLM-specific tool.
Evaluation features. Datadog LLM Observability includes built-in evaluators for hallucination, toxicity, and prompt injection. These run on samples of your production traffic and write quality scores as metrics alongside your cost and latency data. Not as sophisticated as Phoenix's eval system, but functional and integrated.
Pricing. LLM Observability is priced separately from Datadog's core platform. At around $0.002 per LLM span, with a 1 million span minimum per month ($2,000/month minimum). This is significantly more expensive than Helicone for most teams. It makes sense only if you're already a significant Datadog customer and value the integration over the cost.
How to choose
Start with Helicone if you're not yet on Datadog or Vantage and want LLM cost visibility fast. The setup is genuinely 10 minutes, the cost breakdowns are good, and the free tier handles meaningful scale.
Use Vantage if you need to show LLM costs in context with your full cloud bill. For CFO-level reporting or multi-cloud cost optimization, Vantage's broader view is worth the higher price.
Use Datadog LLM Observability if you're an existing Datadog customer with existing APM instrumentation, and you want LLM cost data to flow into your existing dashboards, monitors, and incident workflows. Don't add Datadog just for LLM cost monitoring; the price-to-value ratio doesn't work unless you're already paying for the platform.
What none of these tools do well: cost per outcome
The hardest and most valuable form of LLM cost monitoring is attributing cost to business outcomes. Not "this user spent $0.43 today" but "each completed onboarding cost $0.12 in LLM calls" or "our research report feature costs $0.83 per report and we charge $2.00, so margin is fine."
None of the three tools above do this automatically. It requires instrumentation in your application: when a business event completes (report generated, booking confirmed, support ticket resolved), calculate the total LLM cost of that session and write it as a business metric alongside the outcome.
Helicone's Properties feature is the most practical path to this: tag every LLM call with a session ID, let the session complete, then query Helicone's API to sum up costs for that session ID, and write the result to your analytics.
Once you have cost-per-outcome data, your cost decisions become product decisions rather than infrastructure decisions. At that point, you're not just monitoring costs; you're managing unit economics.
The token-level tracking that feeds into these platforms is covered in the token tracking tools guide.