The LLM tooling landscape shifted significantly between 2025 and 2026. Helicone, the most-cited LLM monitoring tool, was reportedly acquired by Mintlify in early 2026 and appears to be in maintenance mode. OpenMeter was acquired by Kong and pivoted to enterprise infrastructure. Two of the most commonly recommended tools in "best LLM monitoring" roundups are no longer actively developed as independent products.
According to Flexera's 2023 State of the Cloud report, 82% of enterprises cite cost management as their top AI challenge. That number makes sense given what we're seeing: teams are spending real money on LLM APIs with no visibility into where it goes. McKinsey's State of AI 2024 adds context: while 72% of organizations have now adopted AI, fewer than half have implemented any cost attribution below the level of total monthly spend — meaning most teams know what AI costs in aggregate but not which features, teams, or experiments are responsible.
This guide covers what's available in 2026: what each tool does, what it costs, and who it's right for.
TL;DR: The LLM monitoring landscape shifted in 2026 — Helicone was acquired by Mintlify and OpenMeter pivoted to enterprise. The market now splits into observability-first tools (Langfuse, LangSmith) and cost-first tools (Tokonomics, LiteLLM). Teams with dedicated cost monitoring save 23% on average (CloudZero, 2024).
Key Takeaways
- 82% of enterprises name AI cost control as their #1 challenge (Flexera, 2023)
- Helicone's acquisition by Mintlify (per Mintlify's own blog — see disclosure below) means no new features as an independent product
- The market splits cleanly: observability-first tools vs. cost-first tools
- Teams with dedicated cost monitoring save 23% on average (CloudZero, 2024)
- Only one tool in this roundup is specifically built for budget-first SMB teams on any language stack
Bias disclosure: Tokonomics is included in this comparison and is the platform behind this blog. Where Tokonomics is described, the claims reflect our own product knowledge. For every other tool, we rely on public pricing pages, documentation, and third-party reports.
This is the hub page for Tokonomics' Tools and Alternatives cluster. Related posts: Helicone vs Tokonomics | Best Helicone Alternatives 2026
What Does the LLM Monitoring Market Look Like Right Now?
82% of enterprises cite cost management as their top AI challenge, according to Flexera's 2023 State of the Cloud report. That figure explains why at least a dozen tools entered the LLM monitoring space in 2023-2025. It also explains why consolidation is happening: the space attracted too many generalist observability plays.
In our experience reviewing these tools for Tokonomics customers, the single most common complaint is that observability platforms surface what happened in an LLM call without surfacing what it cost at the team or feature level. These are different problems.
The market today splits into two categories that look similar but serve different needs.
Observability-first tools (LangSmith, Langfuse, Portkey) are designed for debugging, tracing, and evaluating LLM behavior. They show you what happened inside a chain of LLM calls. Cost visibility exists, but it's secondary to traces, spans, and eval suites.
Cost-first tools (Tokonomics) are designed for budget control. They show you what things cost, enforce budgets, fire alerts, and route by model tier. Trace-level debugging is out of scope by design.
Most teams need both eventually. But if your current problem is "we don't know what we're spending or why," observability tooling won't solve it. Start with cost monitoring. Add observability when chain debugging becomes the bottleneck.
Tool-by-Tool Breakdown
Helicone: Status and the Acquisition Question
Helicone is an LLM monitoring platform that was acquired by Mintlify in early 2026. Its Pro plan was priced at $79/month. Helicone was the most widely cited LLM monitoring tool before the acquisition. It works for existing customers. No new features are visible on any public roadmap.
A note on sourcing: The acquisition claim originates from the Mintlify blog, which is an interested party. We have not found independent press coverage confirming the terms or scope. What we can confirm: Helicone's public blog has not published since early 2026, and Mintlify's own blog references the acquisition directly (Mintlify blog, March 2026). Treat the "maintenance mode" characterization as our inference from those signals, not a confirmed statement from Helicone.
| Pricing | Free tier (10k requests/mo) · Pro $79/mo · Enterprise custom |
| Primary focus | Request logging, cost analytics, prompt management |
| Strengths | Clean UI, solid historical data, wide model support |
| Limitations | No evidence of active development post-acquisition; no hard budget caps; no multi-tenant billing |
| Verdict | Existing customers: fine to stay. New customers: choose an actively maintained tool. |
LangSmith (LangChain): Best for Agent Debugging
LangSmith publishes 3-5 posts per week and maintains a growing enterprise customer base. It is the observability platform for teams building with LangChain and complex agent workflows. Chain debugging is genuinely excellent here. The Stanford HAI 2025 AI Index notes that agentic AI deployments grew 3x in enterprise settings between 2023 and 2024, driving demand for trace-level observability tools — the category where LangSmith excels but budget enforcement tools do not.
| Pricing | Free (5k traces/mo) · Plus $39/mo · Enterprise custom |
| Primary focus | Traces, evaluations, prompt hub, datasets |
| Strengths | Best-in-class chain debugging, deep LangChain integration, evaluation suite |
| Limitations | Python/JS only; no cost-first workflows; no budget enforcement; no per-tenant billing |
| Verdict | Best choice if you're building LangChain agents and need trace-level debugging. Poor fit for cost control or any-stack teams. |
LangSmith surfaces token cost data inside each trace, which looks like cost monitoring. It isn't. You can see what a single run cost. You cannot set a budget, route by cost tier, or receive an alert when a feature's spend crosses a threshold. These are different capabilities.
Langfuse: Best Self-Hosted Observability
Langfuse has built a strong community around its open-source observability platform, reporting 40,000+ users. It's the most credible independent LLM observability tool for teams who want self-hosted control.
| Pricing | Open-source (self-host free) · Cloud $59/mo · Enterprise custom |
| Primary focus | Traces, scores, evaluations, prompt management |
| Strengths | Open-source, strong community, Python/JS/REST SDK, self-hostable |
| Limitations | Cost analytics are secondary; no hard budget caps; no multi-tenant cost isolation |
| Verdict | Best choice for teams who want self-hosted observability and don't need cost enforcement. |
Citation Capsule: Langfuse's open-source model has attracted over 40,000 community users as of 2026, making it the largest independent LLM observability project by reported user count. Its cloud plan starts at $59/month. Self-hosting is free. Cost enforcement (budget caps, per-tenant isolation) is outside its scope. (Source: Langfuse public documentation, 2026)
Portkey: Best for Enterprise LLM Gateways
Portkey positions itself as an "AI gateway," which is broader than cost monitoring. It covers routing, caching, fallbacks, and governance. The company published a 2-trillion-token production report, which signals genuine scale. Enterprise traction is real.
| Pricing | Free tier · Growth $79/mo · Enterprise custom |
| Primary focus | LLM gateway, routing, observability, governance |
| Strengths | Production reliability features, fallback routing, enterprise audit trails |
| Limitations | Enterprise-first pricing; complex setup for SMBs; Python/JS SDK focus; no SMB-specific budget tooling |
| Verdict | Best choice for Python/JS enterprise teams who need an LLM gateway with observability. Overkill for SMBs. |
Maxim AI: Worth Independent Verification
Maxim AI frequently publishes "best LLM tools" roundups where they rank themselves prominently. The product is real, but their content should be read critically. Pricing is not publicly listed, which complicates any direct comparison.
| Pricing | Enterprise pricing, not publicly listed |
| Primary focus | LLM cost tracking, observability, quality testing |
| Limitations | Enterprise-first, limited SMB content, pricing opacity |
| Verdict | Worth evaluating for enterprise, but verify independently. |
Does Your Team Actually Save Money with Cost Monitoring?
Teams with dedicated cost monitoring save 23% on AI spend on average, according to CloudZero's 2024 Cloud Cost Intelligence Report. That figure holds across company sizes, though the mechanism differs: large teams recapture cost from forgotten experiments; smaller teams catch runaway features before the invoice arrives.
We built Tokonomics because we hit the same wall our customers describe. We were using a major LLM provider, costs were climbing, and no tool in the existing stack could tell us which feature was responsible. The provider's native dashboard showed a global monthly total. That's it.
The 23% savings figure aligns with what we observe in our own customer onboarding. Teams with zero prior attribution almost always find one or two features responsible for 40-60% of their total spend, once per-feature tagging is in place.
Citation Capsule: CloudZero's 2024 Cloud Cost Intelligence Report found that teams with dedicated AI cost monitoring save an average of 23% on cloud AI spend compared to teams using only provider-native dashboards. The mechanism is attribution: knowing which feature, team, or experiment is responsible for each dollar spent. (Source: CloudZero Cloud Cost Intelligence Report, 2024)
Feature Comparison Matrix
The matrix below uses publicly available information for all tools. Tokonomics data reflects first-party knowledge. All other data is sourced from public pricing pages and documentation as of June 2026.
Feature comparison verified against public documentation, June 2026.
| Feature | Helicone | LangSmith | Langfuse | Portkey | Tokonomics |
|---|---|---|---|---|---|
| Cost tracking | Yes | Partial | Partial | Yes | Yes |
| Budget alerts | Limited | No | No | Partial | Yes |
| Hard spending caps | No | No | No | Partial | Yes |
| Per-tenant cost isolation | No | No | No | Enterprise only | Yes |
| Model routing | No | No | No | Yes | Yes |
| Any-stack support | Partial | No | Partial | Partial | Yes |
| Chain tracing / evals | Partial | Yes | Yes | Yes | No |
| Self-hosted option | No | No | Yes | No | No |
| Active development | Unconfirmed post-acquisition | Yes | Yes | Yes | Yes |
| SMB pricing under $100/mo | $79/mo | $39/mo | $59/mo | $79/mo | $49/mo |
Which Tool Fits Your Budget Strategy?
You need cost control, not chain debugging: Tokonomics. Budget alerts, hard caps, per-tenant isolation, model routing. Any language, 5-minute setup at $49/month.
You're building LangChain agents and need trace debugging: LangSmith. Best-in-class for chain traces. Add a separate cost layer if budget control matters.
You want self-hosted open-source observability: Langfuse. Strong community, self-hostable, good evaluation suite at $59/month cloud or free self-hosted.
You're a Python/JS enterprise team that needs a full LLM gateway: Portkey. Production-grade routing, fallbacks, governance at $79/month.
You're an existing Helicone customer: Evaluate alternatives before your next renewal. The product appears to be in maintenance mode, though we cannot confirm the full scope of the acquisition's impact.
Frequently Asked Questions
Is Helicone still worth using in 2026?
For existing customers with data in the platform, yes — it works. For new projects, we recommend choosing an actively maintained alternative. Mintlify acquired Helicone, per Mintlify's own blog (an interested source), and LLM cost monitoring isn't Mintlify's core roadmap. We have not found independent confirmation of the acquisition's terms.
Do I need both a cost monitoring tool and an observability tool?
For most SMBs: start with cost monitoring only. Once you're spending over $3,000 per month and have complex agent chains, add observability. The primary question for teams under $5,000/month in AI spend is "why is my bill high?" That's cost monitoring, not chain tracing.
Can I use LangSmith for cost tracking?
LangSmith shows cost data inside each trace, but it's not built for cost-first workflows. There's no budget alert, no hard cap, no per-tenant isolation, and no model routing. If your primary question is "which feature is costing me money?" LangSmith won't answer it efficiently.
What's the cheapest way to start LLM cost monitoring?
Provider-native analytics (free) give you a global monthly total. Add a proxy layer at $49/month for per-feature and per-tenant attribution, alerts, and routing. For most teams spending $500-$5,000 per month on AI, that combination covers 95% of cost visibility needs.
How much can I realistically save with dedicated cost monitoring?
CloudZero's 2024 report puts the average at 23% savings for teams with dedicated AI cost monitoring vs. provider dashboards alone. In practice, the biggest wins come from discovering which features are responsible for disproportionate spend, something provider dashboards cannot show you.
What to Do Next
The LLM monitoring space in 2026 is simpler than it looks. Most tools are observability-first. Only one is cost-first. Most target Python/JS enterprise teams. Only one is language-agnostic by design.
Choose Based on Your Actual Problem
If chain debugging is your pain, use Langfuse or LangSmith. If cost control is your pain, use a cost-first tool. If you need both, use both: they complement each other.
Start Before the Bill Surprises You
Teams with dedicated cost monitoring save an average of 23% on AI spend (CloudZero, 2024). That number matters more at $2,000/month than it does at $200/month. But the time to instrument is before the bill surprises you, not after.
Get Attribution First, Then Add Observability
Most teams under $5,000/month in AI spend need one thing: to know which feature, client, or experiment is responsible for each dollar. Provider dashboards don't show this. A proxy-based cost monitoring layer does. Start there, and add trace-level observability when your agent chains grow complex enough to require debugging.
Sources: Flexera 2023 State of the Cloud Report | CloudZero Cloud Cost Intelligence Report, 2024 | McKinsey State of AI 2024 | Stanford HAI AI Index 2025 | Gartner GenAI Predictions 2024 | Helicone acquisition: Mintlify blog, March 2026 (interested party - see disclosure above) | Langfuse community data | Portkey LLMs in Production Report | All pricing pages verified June 2026.
About the author: Zouhair Ait Oukhrib is the founder of Tokonomics. About → | Contact →
All sources retrieved June 2026.