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Market map

AI economics vendor map

The tools that let an enterprise see, control and prove the economics of the AI it uses, from the model call to the board pack. Organised by the problem each one solves, with every entry checked against its own sources.

Last reviewed
vendors and platforms
99vendors and platforms
categories across five control layers
13categories across five control layers
ownership, name or status changes logged since March 2025
13ownership, name or status changes logged since March 2025
listed vendors that are Tokenomics Foundation members
28listed vendors that are Tokenomics Foundation members

Vendor-neutral and unsponsored. The map describes vendors from public sources and does not rate, score or rank them. IBM-owned products are marked IBM-owned. See how the map is built. Tokenomics Foundation membership comes from the Foundation's members page and signals engagement with the discipline, not quality. I'm Ed Epstein, an enterprise account executive at IBM in the Apptio portfolio (Apptio, Cloudability, Turbonomic), and I write and edit AI Economics Hub. IBM sells products in several areas this site covers, including FinOps, technology business management and AI value measurement, and is a member of industry bodies the site discusses, including the Tokenomics Foundation. This is an independent personal publication. The postings on this site are my own and don't necessarily represent IBM's positions, strategies or opinions. No IBM, client or partner confidential information is used. IBM-owned products are marked wherever they appear, and the vendor map describes products without rating them.

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Directory

99 vendors, 13 categories

Showing 99 of 99

Layer A: Build and run

Model Gateways & Routing

11 vendors

Problem it solves: Multi-provider access, routing policy, failover, budget enforcement at the point of consumption.

How to read this category

The gateway is where AI cost policy becomes enforceable rather than reportable. This is the layer FinOps teams most often discover they are missing.

Maps to Usage Transparency in the Five-Layer model; most relevant at maturity levels 1 to 3.

  • Bifrost (Maxim AI)

    Open-source, performance-focused gateway unifying LLM, MCP, and agent traffic.

  • Cloudflare AI Gateway

    Edge-network gateway with caching, rate limiting, analytics, and unified billing for third-party model usage.

  • Kong AI Gateway

    AI routing built on Kong's established API management platform.

  • LangDB

    AI gateway with agent tracing and cost monitoring, compatible with LangChain, Google ADK, OpenAI and other frameworks.

  • LiteLLM

    Open-source LLM proxy and gateway with broad provider coverage, budgets, and key-level spend controls.

  • Not Diamond

    Model router that predicts the best model for each request, now focused on coding agents.

  • OpenRouter

    Managed single endpoint across hundreds of models with aggregated billing.

  • Portkey (Palo Alto Networks Prisma AIRS AI Gateway)

    Acquired

    Full-stack AI gateway combining routing with observability, guardrails, governance, and prompt management.

  • Requesty

    OpenAI-compatible router across 600+ models, with routing, caching, failover, observability and EU data residency.

  • TrueFoundry

    Enterprise AI gateway and MCP gateway with rate limiting, observability and compliance controls, plus model deployment.

  • Vercel AI Gateway

    Managed routing tied to the Vercel platform, with pass-through provider pricing and automatic fallbacks.

Also plays a role here:

Layer A: Build and run

LLM Observability & Evaluation

13 vendors

Problem it solves: Trace-level cost, quality, and workflow context.

How to read this category

This is where cost data meets quality data. Without it, optimisation degrades into cheapest-token chasing.

Maps to Usage Transparency and Output Quality in the Five-Layer model; most relevant at maturity levels 2 to 3.

  • Arize (Phoenix and Arize AX)

    Two products: Phoenix, a free self-hosted tracing and evaluation project, and Arize AX, the commercial platform with online evaluation, drift monitoring and enterprise support.

    • Tracing
    • Evaluation
    • Cost attribution

  • Braintrust

    Evaluation-first platform with logging, prompt management, and a gateway designed to feed its eval workflows.

  • Datadog LLM Observability

    Extension of Datadog's observability platform into LLM tracing, quality, and cost monitoring.

  • Galileo

    AI observability and evaluation platform that turns offline evaluations into production guardrails.

  • Helicone

    Part of Mintlify

    Acquired

    Open-source LLM observability and cost monitoring with an AI gateway for routing, failover and caching.

    • Tracing
    • Cost attribution
    • Gateway
    • Routing
    • Caching

  • Langfuse

    Part of ClickHouse

    Acquired

    Open-source LLM observability with tracing, evaluation, and cost visibility.

    • Tracing
    • Evaluation
    • Cost attribution

  • LangSmith (LangChain)

    Part of LangChain

    Tracing, evaluation, and monitoring platform tightly paired with the LangChain ecosystem.

    • Tracing
    • Evaluation
    • Cost attribution

  • Opik (Comet)

    Open-source AI observability and evaluation platform from Comet for testing and improving agent systems.

  • Traceloop

    Acquired

    LLM reliability platform and maintainer of OpenLLMetry, the OpenTelemetry-based instrumentation for LLM apps; joining ServiceNow.

  • Weights & Biases Weave

    Part of CoreWeave

    LLM tracing and evaluation inside the established W&B ML platform.

    • Tracing
    • Evaluation
    • Cost attribution

  • Promptfoo

    Part of OpenAI (acquisition announced)

    Open-source test harness that compares prompts and models on output quality, estimated cost and latency, with red teaming for security.

    • Evaluation
    • Red teaming

  • TruLens

    Part of Snowflake

    Open-source evaluation and tracing library for LLM apps and agents, built to find the quality and cost frontier between versions.

    • Evaluation
    • Tracing
    • Cost attribution

  • Ragas

    Open-source evaluation framework with metrics for retrieval-augmented generation, agents and tool use. Its role is quality evidence, not financial management.

    • Evaluation

Layer A: Build and run

Infrastructure & Inference Optimisation

10 vendors

Problem it solves: GPU efficiency, container and workload optimisation, commitment management.

How to read this category

Infrastructure optimisers focus on GPU utilisation, Kubernetes efficiency, and cloud commitment management.

GPU cloud providers such as CoreWeave, Lambda, Together, Fireworks, Baseten, and Modal are supply-side infrastructure, not governance tools, and are therefore not profiled as directory entries. They are referenced here as context for where AI workloads run, but the vendor map focuses on buyer-side control and visibility platforms.

Maps to Usage Transparency in the Five-Layer model; most relevant at maturity levels 2 to 3.

  • Cast AI

    Tokenomics member

    Kubernetes optimisation platform with automation-led scaling and strong relevance for GPU-heavy AI infrastructure.

  • IBM Kubecost

    IBM-ownedTokenomics member

    Kubernetes-native cost allocation platform with strong GPU and workload-level visibility for AI infrastructure.

  • IBM Turbonomic

    IBM-ownedTokenomics member

    Application resource management platform that uses automation to improve utilisation and performance across cloud and container environments.

  • Infracost

    Tokenomics member

    Shift-left FinOps: shows the cost impact of infrastructure-as-code changes and applies policies before deployment.

  • nOps

    Tokenomics member

    Automated AWS cost optimisation, centred on hourly commitment management, with pay-for-results pricing.

  • PointFive

    Tokenomics member

    Cloud Efficiency Posture Management platform with engineering-centric waste detection, automated remediation, and AI-adjacent efficiency controls.

  • ProsperOps

    Automation-led FinOps platform focused on commitment optimisation, workload scheduling, and showback metrics.

  • Spot (Flexera)

    AcquiredTokenomics member

    Cloud infrastructure optimisation (Spot Eco, Spot Ocean, Elastigroup) and CloudCheckr, now part of Flexera.

  • Stacklet

    Tokenomics member

    Cloud governance as code built on the open-source Cloud Custodian project, with an AI agent (Jun0) for FinOps tasks.

  • Xosphere

    Tokenomics member

    FinOps platform built around spot instance optimisation (SpotIQ), Kubernetes node selection (ClusterIQ) and AI token optimisation (TokenIQ).

Layer B: Financial governance

FinOps Platforms & AI Cost Management

17 vendors

Problem it solves: Allocation, showback, anomaly detection, and governance across cloud plus AI spend.

How to read this category

FinOps platforms extend cloud cost governance into AI workloads, providing allocation, budgets, and optimisation workflows.

Maps to Usage Transparency and Delivery Alignment in the Five-Layer model; most relevant at maturity levels 2 to 4.

  • Adaptive6

    Tokenomics member

    Cost governance across cloud, AI platforms, code and runtime, with automated pull requests to fix waste.

  • Amnic

    FinOps-oriented platform positioning AI agents around forecasting, anomaly detection, and predictive spend governance.

  • Apptio Cloudability (IBM)

    IBM-ownedTokenomics member

    Mature FinOps suite extending broad cloud cost governance into AI, containers, and unit economics.

  • Cloudchipr

    FinOps platform with cloud and AI cost attribution and automated workflows to act on waste.

  • CloudZero

    Cost intelligence platform that ties cloud and AI spend to teams, products, features, and unit economics.

  • Datadog Cloud Cost Management

    Cloud cost data joined with Datadog performance and observability data, including AI and LLM spend.

  • DoiT

    Tokenomics member

    FinOps platform (Cloud Intelligence) combined with hands-on engineering support, covering anomalies, Kubernetes and workflows.

  • Finout

    Tokenomics member

    Enterprise FinOps platform with dedicated AI cost management, virtual tagging, and shared-cost allocation.

  • Flexera One

    Tokenomics member

    Hybrid ITAM and FinOps suite with cloud cost optimisation and visibility across broader technology estates.

  • Harness Cloud Cost Management

    Engineering-facing cloud cost platform with allocation, showback and chargeback, and AI-assisted optimisation.

  • Kion

    Tokenomics member

    Cloud governance and FinOps platform (Kion FinOps+) that has added Anthropic token spend management.

  • Mavvrik

    AI and hybrid cloud cost management covering GPU, cloud and AI infrastructure, with forecasting and allocation.

  • North

    Tokenomics member

    FinOps platform covering cloud, AI and data spend, with automated savings.

  • Revefi

    Tokenomics member

    AI FinOps and data observability for cloud data platforms and LLMs, using an AI agent called RADEN.

  • Ternary

    Multi-cloud FinOps platform extending forecasting, anomaly detection, and allocation into AI workloads.

  • Umbrella (formerly Anodot cloud cost)

    Renamed

    Cloud financial management platform that uses AI to detect waste, track savings and forecast costs, aimed at enterprises and managed service providers.

  • Vantage

    Tokenomics member

    Multi-cloud cost management platform with AI provider integrations, savings automation, and GPU-efficiency reporting.

Layer B: Financial governance

TBM / ITFM Platforms

5 vendors

Problem it solves: AI cost inside enterprise service, planning, and portfolio structures.

How to read this category

TBM and ITFM platforms translate AI cost into business service models, cost pools, and portfolio planning structures.

Maps to Delivery Alignment and Portfolio Strategy in the Five-Layer model; most relevant at maturity levels 3 to 5.

  • Apptio TBM (IBM)

    IBM-ownedTokenomics member

    TBM and ITFM platform for cost transparency, allocation, planning, and application of TBM structures to AI spend.

  • MagicOrange

    Technology financial management platform recognised in analyst conversations for cost transparency, service insight, and business reporting.

  • Nicus

    ITFM platform for cost transparency, forecasting, and scenario modelling with explicit visibility into AI and cloud costs.

  • ServiceNow ITFM

    Tokenomics member

    ServiceNow platform capabilities that expose IT cost, asset, and service data inside broader enterprise workflow and AI contexts.

  • Yarken

    Tokenomics member

    Platform that combines AI economics, TBM and FinOps for visibility across cloud, SaaS, infrastructure and AI spend.

Layer B: Financial governance

AI Unit Economics & Attribution

2 vendors

Problem it solves: We can see the AI bill but not what each customer, agent, workflow or product feature costs, so we cannot price, budget or protect margin.

How to read this category

A young category that sits between the gateway and the finance team. These tools meter AI usage at transaction level and attribute it to a customer, agent, feature or workflow, so cost can be compared with revenue or value. They matter most to companies that sell AI-powered products and need cost per customer or cost per outcome. Expect overlap with FinOps platforms adding token cost, and with gateways adding metering.

Usage-based billing platforms such as Stripe Billing (which completed its Metronome acquisition on 14 January 2026), Orb and Amberflo are seller-side tools for charging your customers. They are out of scope here, but often appear in the same evaluation.

Maps to Usage Transparency and Portfolio Strategy in the Five-Layer model; most relevant at maturity levels 2 to 4.

  • Crystil

    Tokenomics member

    Cost and margin visibility for AI agents, down to each action, agent and customer.

  • Revenium

    Tokenomics member

    Metering and attribution for AI usage at transaction level, across tokens, APIs and agent workflows, with real-time spend controls.

Layer C: Consumption and workforce

Workforce AI Usage & SaaS AI Spend

7 vendors

Problem it solves: Copilot, ChatGPT Enterprise, Claude, and embedded AI SKUs are bought as software subscriptions outside the FinOps remit. Shadow AI usage is invisible to all of it.

How to read this category

For most enterprises, seat-based workforce AI is already a larger controllable line than API tokens, and almost no FinOps practice owns it.

Maps to Usage Transparency and Productivity Value in the Five-Layer model; most relevant at maturity levels 1 to 3.

  • Larridin

    Measurement layer for enterprise AI: what AI costs, what teams produce with it and which workflows to automate next.

  • Portal26

    GenAI visibility platform spanning shadow AI discovery, licence intelligence, and a 2026 AI value realisation module.

  • Surveil

    Cost, usage and licence intelligence across the Microsoft estate and multicloud, with a module for Microsoft 365 Copilot readiness, adoption and licence optimisation.

  • Torii

    SaaS management platform with shadow IT and shadow AI discovery plus licence optimisation.

  • Worklytics

    Workplace analytics that shows how work and AI adoption happen across the organisation, from collaboration and tool data.

  • Zylo

    SaaS management platform with AI application discovery, licence utilisation, and renewal governance.

  • Microsoft Copilot Analytics

    Part of Microsoft

    Native Microsoft 365 measurement of Copilot and agent adoption, credit consumption and business impact, delivered through Viva Insights and the admin centre.

    • Adoption analytics
    • Outcome tracking
    • Cost attribution

Also plays a role here:

Layer C: Consumption and workforce

AI Coding & Developer Tools

9 vendors

Problem it solves: Per-seat and usage-hybrid AI coding spend with weak FinOps visibility.

How to read this category

AI coding tools represent one of the largest current enterprise AI spend categories, but cost governance maturity varies widely across vendors.

Maps to Usage Transparency and Productivity Value in the Five-Layer model; most relevant at maturity levels 1 to 3.

  • Amazon Q Developer

    AWS-native AI coding and development assistant with per-seat pricing, AWS-integrated billing, and enterprise governance through IAM and AWS Organizations.

  • Claude Code (Anthropic)

    Agentic coding tool spanning terminal, IDE, and enterprise deployments, with usage-based and seat options.

  • Cursor (SpaceX)

    Acquired

    AI-first code editor with deep model integration, growing enterprise adoption, and tiered pricing including a Business plan with centralised billing.

  • Devin Desktop (formerly Windsurf)

    Renamed

    Agentic code editor from Cognition, rebranded from Windsurf in June 2026, centred on managing local and cloud coding agents.

  • Gemini Code Assist (Google)

    Per-seat AI coding assistance with Google Cloud billing integration.

  • GitHub Copilot

    AI coding assistant from Microsoft with Business and Enterprise tiers, offering seat-level billing, admin controls, and a Billing API for usage reporting.

  • JetBrains AI Assistant

    AI coding assistance integrated into JetBrains IDEs (IntelliJ, PyCharm, WebStorm, etc.) with per-seat licensing through JetBrains Toolbox for Business.

  • OpenAI Codex

    Part of OpenAI

    OpenAI's coding agent across CLI, IDE, cloud and code review, billed through ChatGPT workspace credits or the API depending on plan.

    • Adoption analytics
    • Cost attribution
    • Spend limits

  • IBM Bob

    Part of IBM

    IBM-owned

    IBM's agentic software development platform, with built-in consumption analytics (Bobalytics) and modernisation workflows for IBM Z, IBM i and Java.

    • Adoption analytics
    • Cost attribution

Layer C: Consumption and workforce

Engineering Productivity & AI Impact Measurement

6 vendors

Problem it solves: Proving whether AI coding spend changes engineering output, the bridge from cost to value for the largest current enterprise AI use case.

How to read this category

DORA-metric relabelling is not AI measurement. The credible platforms compare AI-assisted and unassisted work and connect it to delivery outcomes.

Maps to Productivity Value in the Five-Layer model; most relevant at maturity levels 3 to 4.

  • DX

    Developer experience and productivity platform with dedicated AI impact measurement across coding assistants.

  • Faros AI

    Engineering intelligence platform measuring AI coding tool adoption, velocity, and quality effects.

  • Jellyfish

    Engineering management platform with AI adoption and impact analytics tied to delivery and investment views.

  • LinearB

    Software delivery analytics with AI impact reporting and workflow automation.

  • Quesma

    Tokenomics member

    Cost and outcome analytics for coding agents (Claude Code, Codex, Cursor), linking session costs to engineering work.

  • Swarmia

    Engineering intelligence platform covering delivery metrics, developer experience and AI tool impact.

Also plays a role here:

Layer D: Value and strategy

AI Value Management & Portfolio Governance

7 vendors

Problem it solves: Connecting AI usage, cost, quality, and productivity to portfolio decisions, the full five-layer problem.

How to read this category

The thinnest category in the directory, but thin coverage here is not proof that capable products do not exist. Portfolio platforms such as Planview, AI governance layers from ServiceNow and SAP, and new value modules from IBM Apptio are all moving into this space, mostly with capabilities announced in 2026 and not yet proven at scale. Check each one's value evidence: few record realised financial benefit rather than adoption or estimated savings.

Maps to Usage Transparency and Output Quality and Productivity Value and Delivery Alignment and Portfolio Strategy in the Five-Layer model; most relevant at maturity levels 4 to 5.

  • Ascerta (formerly Pay-i)

    RenamedTokenomics member

    Measures, adjusts and improves the value of enterprise AI, with products called Atlas, Forge and Convoy.

  • elvex

    AI work platform with built-in outcome and ROI measurement framing.

  • IBM Apptio AI Value & ROI

    Part of IBM

    IBM-ownedTokenomics member

    Links each AI initiative to business outcomes and customer-selected proof metrics, tracking baseline, target, actual and realised results against modelled AI cost.

    • Outcome tracking
    • Cost attribution
    • Portfolio planning

  • Olakai

    AI ROI measurement platform connecting adoption data, productivity signals, and business outcomes across tools.

  • ServiceNow AI Control Tower

    Tokenomics member

    Governance and visibility layer for AI agents, models, and workflows across the ServiceNow platform.

  • Planview

    Strategic portfolio management platform adding agent resource management, AI scenario planning and investment-to-outcome tracking.

    • Portfolio planning
    • Outcome tracking
    • Agent and MCP inventory

  • SAP AI Agent Hub

    Part of SAP

    Renamed

    Discovery and governance of AI agents, models and MCP servers across vendors, linked to enterprise architecture in SAP LeanIX.

    • Agent and MCP inventory

Layer D: Value and strategy

Process Outcomes & Benefits Realisation

2 vendors

Problem it solves: Showing, from operational data, whether AI changed how work actually runs: cycle times, exceptions, rework and cost per completed case.

How to read this category

These are outcome evidence layers, not AI cost tools. They supply the before-and-after process data that a benefits case needs, and they are most useful when joined to cost data from layers A and B.

Maps to Productivity Value and Delivery Alignment in the Five-Layer model; most relevant at maturity levels 3 to 5.

  • Celonis

    Process intelligence platform that reconstructs how processes actually run, providing the operational evidence to test whether AI changed business performance.

    • Process intelligence
    • Outcome tracking

  • UiPath Maestro

    Part of UiPath

    Orchestration of AI agents, robots and people in one process, with process mining and automation-level analytics.

    • Workflow orchestration
    • Process intelligence

Layer E: Native provider controls

Hyperscalers

4 vendors

Problem it solves: Native AI billing, tagging, and optimisation controls from cloud platforms.

How to read this category

Hyperscaler native controls are improving but remain incomplete. They are designed to keep workloads within the platform ecosystem.

Maps to Usage Transparency and Delivery Alignment in the Five-Layer model; most relevant at maturity levels 1 to 3.

  • AWS

    Native AI billing combines model-level Bedrock pricing with AWS tagging, Cost Explorer, and compute optimisation tooling.

  • Google Cloud

    Google Cloud combines Vertex AI pricing with labels and tags, billing export, and recommender-style cloud optimisation controls.

  • Microsoft Azure

    Azure exposes Azure OpenAI and Azure AI Foundry pricing plus native Cost Management, tags, and Advisor recommendations across AI workloads.

  • Oracle Cloud Infrastructure

    Tokenomics member

    OCI provides generative AI cost models plus native Cost Analysis for service-, SKU-, compartment-, and tag-level views.

Layer E: Native provider controls

Model Providers

6 vendors

Problem it solves: Direct model API billing, usage reporting, and spend controls.

How to read this category

Model providers offer varying levels of cost visibility and control. Enterprise governance depth differs significantly across vendors.

Maps to Usage Transparency in the Five-Layer model; most relevant at maturity levels 1 to 2.

  • Anthropic

    Claude API with token-based pricing, caching tiers, batch discounts, and admin APIs for granular usage and cost reporting.

  • Cohere

    Model API with token-, search-, and embedding-based billing plus billed token reporting and production and trial key separation.

  • Google Gemini API

    Gemini Developer API with paid and free tiers, token-based pricing, context caching, and project-level quota tiers.

  • Meta Llama

    Open-weight model family where economics depend largely on the hosting path rather than a single public billing surface.

  • Mistral

    API and workspace platform with transparent pricing, usage tiers, and organisation-level rate and token limits.

  • OpenAI

    Model provider covering both the API and the workforce products: token-based API billing with project budgets, and ChatGPT credit analytics and spend controls for enterprise workspaces.

    • Cost attribution
    • Spend limits
    • Adoption analytics

Analysis

What the market is signalling to buyers

Six patterns drawn from the entries and dated events on this page. Each ends with what we would do about it.

  1. 01

    Consolidation is fast, so contracts need exit terms

    We logged 13 acquisitions, renames or exits among vendors on or near this map since March 2025, and 10 current entries carry an ownership or name change. Products bought eighteen months ago may now sit inside a different company with a different roadmap.

    What to do: Ask for change-of-control terms, data export in an open format and a dated roadmap commitment.

  2. 02

    AI gateways are turning into security products

    Portkey now ships as part of Palo Alto Networks Prisma AIRS. Kong AI Gateway and Cloudflare AI Gateway come from API and network security, and TrueFoundry leads with an MCP gateway for agent tool access. Cost control is becoming one feature of a security purchase.

    What to do: Decide who owns the gateway, platform engineering or security, and check that budgets and attribution survive the change.

  3. 03

    Observability tools are being absorbed by data and infrastructure platforms

    ClickHouse acquired Langfuse, CoreWeave owns Weights & Biases Weave, Mintlify acquired Helicone and Traceloop is joining ServiceNow. Trace data, the raw material for cost per outcome, is moving into larger estates.

    What to do: Prefer OpenTelemetry-based instrumentation and open-source cores so traces stay portable if the vendor changes hands.

  4. 04

    FinOps vendors are repositioning around AI, at different depths

    On 6 October 2026 the homepage titles read “The AI ROI Company” (CloudZero), “FinOps Platform for the Agentic Era” (Finout) and “the complete token engineering platform” (Faros AI). Kion added Anthropic token spend management in a point release. Messaging has moved faster than most products.

    What to do: Ask each vendor to load a month of your own model provider billing data and show allocation by team and use case.

  5. 05

    Coding agents are the fastest-moving cost line

    SpaceX completed its acquisition of Cursor in August 2026, Windsurf became Devin Desktop, and new tools such as Quesma and Not Diamond exist only to explain or reduce coding-agent spend. Seat prices are giving way to usage-based charges.

    What to do: Track cost per merged pull request or per completed task, not cost per seat.

  6. 06

    Value measurement is thin here, and new to the market

    This directory lists 7 entries in AI value management, 2 in process outcomes and 2 in AI unit economics, against 17 FinOps platforms. A short list is not proof of an empty market: Planview and IBM Apptio AI Value & ROI both announced value capabilities in 2026. But only 2 assessed entries are designed to record realised financial benefit rather than adoption or estimated savings.

    What to do: Ask each vendor which kind of value evidence it records, and expect to build part of the value model yourself; see the AI Value Gap and AI ROI pages.

Timeline

Market moves

Acquisitions, renames and exits that change what you are buying. Each links to its source. Removed from the map in this edition: Productiv (ceased operations) and Zluri (now an identity governance product).

  1. 14 August 2026

    SpaceX completes acquisition of Cursor

    The most widely used AI code editor becomes part of a company that trains its own models. Cursor blog

  2. 6 August 2026

    Productiv ceases operations

    A SaaS management vendor exits, a reminder to check vendor viability before multi-year contracts. Productiv

  3. 16 July 2026

    Portkey becomes Prisma AIRS AI Gateway

    Palo Alto Networks makes its Portkey-based AI gateway generally available, about six weeks after closing the acquisition. Palo Alto Networks blog

  4. 2 June 2026

    Windsurf renamed Devin Desktop

    Cascade agent retired on 1 July 2026, replaced by Devin Local. Devin Desktop

  5. 9 March 2026

    OpenAI agrees to acquire Promptfoo

    The open-source evaluation and red-teaming tool is to be folded into OpenAI Frontier; OpenAI says the open-source project will continue. OpenAI announcement

  6. 3 March 2026

    Mintlify acquires Helicone

    An open-source LLM observability tool joins a documentation platform. Helicone blog

  7. March 2026

    Traceloop announces it is joining ServiceNow

    OpenTelemetry-based LLM instrumentation moves towards ServiceNow AI governance. Traceloop blog

  8. 14 January 2026

    Stripe completes Metronome acquisition

    Usage-based billing consolidates into payment platforms. Seller-side billing is outside this map but often in the same evaluation. Stripe newsroom

  9. 2026, date not stated

    ClickHouse acquires Langfuse

    Langfuse says it stays open source and self-hostable. Langfuse blog

  10. 2026, date not stated

    Pay-i rebrands as Ascerta

    Follows an $18 million Series A led by Dell Technologies Capital; moves from cost tracking towards AI value measurement. Ascerta

  11. 14 July 2025

    Cognition takes over Windsurf

    The Windsurf product and team move to Cognition, maker of Devin. SiliconANGLE

  12. 5 May 2025

    CoreWeave acquires Weights & Biases

    Weave, the W&B LLM observability product, now sits inside a GPU cloud provider. CoreWeave press release

  13. 3 March 2025

    Flexera completes acquisition of Spot from NetApp

    Spot Eco, Ocean, Elastigroup and CloudCheckr move into the Flexera portfolio. Thoma Bravo press release

Market structure

Five control layers, from model call to business outcome

No single platform covers the whole chain. Most organisations combine one tool from layer A or E for live control, one from layer B for financial allocation, and build or buy the value view in layer D.

Layer A

Build and run

Engineering infrastructure for AI demand: model routing, spend enforcement, trace-level observability, and GPU efficiency.

34 vendors

Layer B

Financial governance

Cost allocation, showback, and financial governance from cloud workloads to enterprise portfolio views.

24 vendors

Layer C

Consumption and workforce

Workforce AI tools, seat spend, shadow AI discovery, and measuring productivity impact across engineering.

22 vendors

Layer D

Value and strategy

Connecting AI cost, usage, quality, and productivity to portfolio decisions and business outcomes.

9 vendors

Layer E

Native provider controls

Baseline billing, tagging, and optimisation controls from hyperscalers and model providers.

10 vendors

Categories also map to the Five-Layer AI Value Intelligence model and the AI Economics Maturity Model; open “How to read this category” in the directory for each mapping.

Supporting notes

Enabling technologies that change the economics

These parts of the AI stack move unit costs, sometimes more than model price does, but none is a buyer-side governance product. They are described here rather than listed in the directory. An entry is added only where a product gives the buyer a distinct economic control.

Enabling technologies, their economic effect and why they are not directory entries
TechnologyEconomic effectWhy it is not listed
Semantic cachingRedis LangCacheReturns a stored answer when a new prompt means the same as an earlier one, so output tokens are not paid for again. Savings scale with the cache hit rate.A component inside an application or gateway, not a governance product. Several listed gateways include caching.
Inference serving and prefix cachingvLLMReuses computation for prompts that share a prefix, such as a long system prompt, which cuts prefill cost and latency on self-hosted models.Supply-side infrastructure. Its effect shows up in the GPU and infrastructure costs that layer A and B tools measure.
Agent frameworksLangGraph, CrewAI, OpenAI Agents SDKDecide how many model and tool calls one task makes. Loop limits, retries and planning steps often matter more to cost per task than model price.Build tools with no buyer-side cost control of their own. Their cost appears in observability and gateway tools.
Vector databases and embeddingspgvector, Pinecone, WeaviateRetrieval quality sets how much context each prompt carries. Poor retrieval inflates input tokens; storage and re-embedding are recurring costs.Data infrastructure. Their spend belongs in cloud and FinOps cost allocation.
MCP servers and toolsModel Context Protocol serversEach tool an agent can call adds context, calls and sometimes per-call fees, and widens the surface to govern.Governed through gateways and agent inventories, such as TrueFoundry and SAP AI Agent Hub, rather than as products in their own right.

Method

How to read the map

The map organises vendors by the problem each one addresses. It describes what each product does, from public documentation; it does not rate, score or rank vendors, and listing is not a recommendation. Two descriptions help separate products that sound alike:

Spend controls it documents
A reported budget, an alert and a limit that stops consumption are different controls. Described entries show one of: not a spend control, reports spend, alerts on thresholds, enforces limits.
Value evidence it is designed to record
Which kinds of evidence a product records: adoption and usage, estimated time savings, measured process change, realised financial benefit. Adoption is not time saved, and neither is realised financial benefit.

Both are taken from public documentation for entries added or revised on 8 October 2026; other entries have not yet been described this way. Confirm capabilities and pricing with the vendor. Read how the map is built.

Gaps

Where the market is still thin

Gaps in what we could verify from public documentation, not proof that no product addresses them. Several vendors announced capabilities in these areas during 2026 that have not yet been assessed in use.

  1. 1One view from model call to business outcome. No vendor covers the full chain; expect to integrate.
  2. 2Cost attribution for agents that call several models and tools in one task.
  3. 3A cross-vendor view of workforce AI spend and shadow AI. Microsoft and OpenAI now report on their own tools, but rarely on anyone else's.
  4. 4Quality-adjusted cost per outcome. Evaluation tools now report cost beside quality, but rarely beside business outcomes.
  5. 5Live FinOps data flowing into TBM and ITFM service models.
  6. 6Forecasting AI cost from adoption curves and agent scaling, rather than last month's bill.

Scope

What is in and out

Buyer-side only

Tools an enterprise uses to govern AI it consumes. Seller-side billing platforms such as Metronome (now Stripe, completed 14 January 2026), Orb, Lago and Amberflo are out of scope.

No GPU clouds

CoreWeave, Lambda, Together, Fireworks, Baseten, Modal and similar are supply, not governance. They appear in notes where relevant.

CloudHealth not assessed

A scope decision, not a judgement. Its AI-specific cost capability was not assessed for this edition; existing customers should check current documentation.

One primary category per entry

Each product appears once, under its primary buyer problem. Secondary roles show as capability tags and cross-references, and product entries name their parent company. Bedrock and Azure OpenAI sit inside the AWS and Azure entries.

One evidence role in the AI value lifecycle sits outside this map: AI risk and model-governance platforms. They supply approval evidence rather than AI cost and value control, so they are not profiled here.

Missing a vendor, or found an error? Corrections are logged publicly on the corrections page. Changes to this map are listed in the changelog.

Related reading

By Ed Epstein, who works at IBM. The postings on this site are my own and don't necessarily represent IBM's positions, strategies or opinions. About and disclosure · Methodology