Best Palantir alternatives for mid-market companies in 2026
Most mid-market companies don’t go looking for a Palantir alternative. They go looking for what Palantir costs, and the search for alternatives starts a few minutes later.
Palantir doesn’t publish commercial pricing, but the public reference points explain the reaction. Its FY2025 annual report puts average revenue across its top twenty customers at $93.9 million. Its UK G-Cloud 15 rate card, the closest thing to a public price list, lists a Foundry license at £66,000 per server core per year, discovery packages at £50,000 to £250,000, a single-organization platform license at about £3.4 million a year, and implementation and engineering services at £175,000 per person per quarter.
That last line is the one to read twice. Palantir’s model runs on forward deployed engineers: their people, embedded in your company, building the data model and the applications on top of it. Everest Group calls it a category of one, and it is why the software works when it works. It is also the cost most buyers miss, because it isn’t a license. At the published rate, three engineers for a year is £2.1 million before any software.
That is the price of a transformation program. A company with 300 employees usually doesn’t want a transformation program. It wants the pricing desk off a spreadsheet by March.
The gap is real and measured. The US Census Bureau’s business survey puts AI use at 44.1 percent of firms with 250 or more employees against 24.2 percent of businesses overall (collected 19 October to 1 November 2026; the large-firm figure moves from one survey wave to the next). RSM’s 2026 middle-market survey, which covers firms already using AI, found 86 percent have it partly or fully integrated, but only 17 percent are pursuing enterprise-wide transformation. Across all respondents the top barriers are data quality (34 percent), security and privacy (30 percent) and legacy integration (28 percent). Those are mostly not model problems. They are engineering problems, which is exactly what Palantir sells and exactly what the mid-market can’t buy at Palantir’s price.
What Palantir actually offers smaller companies
Palantir for Builders, with a Palantir for SMBs page underneath it, opens Foundry and AIP to startups and small and mid-sized companies. It is a real program with named customers, including Selkirk Sport, the European Cricket Network and NorthWind.
Read the fine print, though. In Palantir’s own words, “it is the same enterprise-grade software but we’re leaning on the talent of our customers to do the building.” The SMB program removes Palantir’s own forward deployed engineer. That is the part of the model that made Palantir work for large enterprises, and the part a 400-person company is least able to replace from its own staff. No price, timeline or minimum commitment is published. The route in is a form. Palantir also lists third-party service providers who can do the building, and says it has not verified them.
How to evaluate a Palantir alternative
Five questions separate the options below, and every entry is written against them.
Who does the building, and do they leave? Someone has to model your business: the entities, the definitions, the exceptions. If it is your own team, you have bought a tool. If it is their team on an open-ended basis, you have bought a dependency. The answer you want is their team, on a defined scope, with a handover.
What do you own when it is done? Code and pipelines on your stack, or configuration inside theirs?
Is the price knowable before the first call? Not “is it cheap”. Is it published?
Does it run on what you already have? Most mid-market companies already pay for a warehouse and an ERP. A second platform is a second migration.
Can you check the answers? Every figure traceable to its source, every model swappable, every decision explainable to an auditor or a board.
The alternatives
Listed alphabetically, not ranked, because the right answer depends on your priority. Lunon is on this list and we wrote it, so judge our entry by the same five questions.
Accenture Edge
Launched in June 2026, this is the serious big-firm answer to “who else serves my size”: a new Accenture business aimed at companies with $300 million to $3 billion in revenue.
Who builds: Accenture, with partner platforms from AWS, Google Cloud, Microsoft, Oracle, SAP, Salesforce and Workday and a set of ready-to-deploy solutions.
Best for: Companies at the top of the mid-market that want a global integrator around the ERP and cloud they already run.
Pricing transparency: Not published.
Honest limitation: Under five months old, launched in “priority markets”, and packaged around partner products, so it is not a Palantir-style ontology build. Its revenue floor likely excludes the smaller companies this guide is written for.
Adaptrix
A Spanish “decision engine”: it connects to your systems, generates a semantic layer, and answers operational questions with an explanation attached. It also publishes the most useful cost breakdown of Palantir on the internet, which is the right thing to say about a competitor’s page.
Best for: EU mid-market companies that want operational analytics under GDPR, without a services engagement.
Pricing transparency: Published in part. From €30,000 a year with inference included, for up to 25 users on shared infrastructure. Higher tiers are by quote. Still the most transparent vendor on this list.
Deployment: EU infrastructure. On-premise and air-gapped operation are on the roadmap, not shipping.
Honest limitation: It is an analytics product, not an engineering team. It will tell you margin is falling. It will not rebuild the pricing process that is causing it. SOC 2 and ISO 27001 are also listed as roadmap, not held, and its case studies are explicitly fictional. It was founded in June 2025.
Ciridae
Ciridae builds custom systems for construction, field service and distribution, starting from one workflow: three days to a prototype, three weeks to a live workflow, three months to what it calls an operating system. It raised a $20 million seed led by Accel, with Andreessen Horowitz and General Catalyst. General Catalyst also backs Lunon, so weigh this entry knowing that.
Who builds: Its engineers and deployment strategists, alongside your operators.
Best for: Mid-market companies in those trades that want a fast, opinionated build.
Pricing transparency: Not published on the pages we read.
Honest limitation: Founded in 2025, seed stage, and focused on specific industries. Ask what you own at the end, because we could not determine that from its site.
Databricks or Snowflake plus an integrator
The build-it-yourself route, and for some companies the right one. It is also the route most of our own engagements run on, because the client already pays for the warehouse.
What it is: A general-purpose data platform. You bring the platform, an integrator or in-house team brings the modelling, and together you build the equivalent of an ontology and the applications on top.
Best for: Companies with an existing data-engineering function and unusual requirements no packaged product matches.
Pricing transparency: Partial, and the best on this list. Databricks publishes pay-as-you-go pricing with a free trial. Snowflake publishes a credit table at $2 to $4 per credit by edition in US regions. Integrator day rates are the larger and less predictable number.
Time to value: Months. No version of this route is fast.
Honest limitation: You own the outcome and the maintenance. Every pipeline and definition becomes something your team keeps alive. A feature if you have the team, a liability if you don’t.
DataWalk
The most direct alternative to Palantir Gotham, and it says so on its own site.
What it is: Link analysis, entity resolution and investigative querying over large connected datasets, deployable on-premise or air-gapped.
Best for: Fraud, anti-money-laundering, KYC, intelligence and law-enforcement work.
Pricing transparency: No current published price. Its current page says only that it costs a small fraction of Gotham; a 2022 document compared $35,000 per core with $141,000.
Honest limitation: Investigative software. If the problem is operational decision-making across finance, sales and supply chain, it is the wrong shape of tool.
Distyl AI
Founded by former Palantir people and valued at $1.8 billion in September 2025. Distyl sells forward deployed teams plus its own agent products, and ties part of its project fee to client objectives.
Best for: Fortune 100 and Fortune 500 companies. Its case studies are telecoms, payors, top-50 manufacturers and an auto-finance lender.
Pricing transparency: Not published. Part of the project fee is tied to outcomes.
Honest limitation: Its stated customers are the Fortune 500, so we would not expect a 500-person company to be its focus. That last part is our inference. Most of its case studies do not name the client.
Forward Deployed
The firm most like us on paper: forwardeployed.com sends an engineer to work with your team, sold as a 30/60/90-day program, with stated customers from private-equity portfolios to mid-market operators and enterprise teams.
Best for: PE operating partners and operators who want a defined first-90-days structure with deliverables per phase.
Pricing transparency: Not published.
Honest limitation: Its published evidence is podcasts and field notes rather than measured results, and we found no named customers. Ask for referenceable outcomes at your company size.
Lunon
Full disclosure: this is us. Hold this entry to the same standard as the rest.
Lunon is an AI transformation company based in New York. Our engineers work inside your company, build AI agents on the stack you already run, and stay through full rollout. It is Palantir’s delivery model without Palantir’s platform.
Best for: US mid-market companies, roughly 100 to 2,000 employees, with an operating problem that needs a working system this quarter: a quoting desk, an order queue, a demand forecast, a monthly close that eats a week.
Who builds: Our engineers, on site where it matters, on a defined scope. We start with one operating outcome, not a platform rollout.
What you own: Everything. We don’t sell software. We build on your warehouse, your ERP and your cloud. We don’t build models either. We route each step of a system to whichever frontier model is strongest at it, so nothing is locked to a model vendor.
Pricing transparency: Not published. We scope each engagement to one operating problem and price that scope before it starts, so the number is known before anyone is on site.
Deployment: Your data stays in your systems. Every engagement starts under NDA. Material is hosted in the United States, never used to train models, and returned or deleted at close.
Proof: Our open-source deep research system ranked first on DeepResearch Bench, ahead of Google’s and OpenAI’s. It was built in a week, and you can read the code.
Honest limitation: We are a small firm. We don’t do classified or government work, we don’t sell investigative link analysis, and if you want a platform vendor with thousands of engineers and a ten-year roadmap, that is Palantir, and it exists because it works. We also won’t take an engagement where nobody on your side owns the outcome.
The delivery model is described on our how we work page.
Ode (formerly Fractional AI)
Fractional AI built embedded applied-AI teams that shipped a system and rolled off. In May 2026 it was acquired by Ode, a services company backed by Anthropic, Blackstone, Hellman & Friedman and others including Goldman Sachs, GIC and Sequoia. Ode bought Casper Studios in August 2026.
What it is: Embedded engineers building on Anthropic’s models, working alongside engineers from Anthropic.
Best for: Mid-size companies in regulated, unglamorous sectors: community banks, regional health systems, mid-sized manufacturers. The case studies on the old Fractional site list the tools used and the measured impact, which is rarer than it should be. They predate Ode and include GPT and Gemini as well as Claude.
Pricing transparency: Not published.
Honest limitation: Tied to one model vendor, and a new entity with very large backers. The trajectory that capital implies is upward, toward enterprise accounts. Ask who will be on your engagement in a year.
Palantir for Builders
The honest “stay with Palantir” option: Foundry and AIP, with your people or a third-party partner doing the building.
Best for: A company with a real data-engineering team that wants Palantir’s ontology without Palantir’s engineers.
Pricing transparency: Not published. No timeline or minimum either.
Honest limitation: The build is on you or a partner Palantir has not verified, and the platform stays theirs. If your team could model the business unaided, you probably would not be reading this.
Which one is right for you
Ten Palantir alternatives by the buyer’s priority, listed alphabetically. Two publish a price; the rest scope and quote.
Source: Lunon review of each vendor’s published materials, November 2026. Palantir figures from the FY2025 Form 10-K and the UK G-Cloud 15 pricing document.
Three cases where we would tell you not to hire Lunon. At government or defence scale you need accreditation and a supplier relationship we don’t offer; Palantir is the serious option. If you want an analytics product with a price on the website and no engineers in the building, Adaptrix is built for that and we are not. And if your core problem is tracing relationships between people, accounts and transactions, buy purpose-built investigative software.
The gap we built Lunon to fill is the one Palantir’s SMB program leaves open: the engineers. A company of a few hundred people can’t staff a Palantir-style build from its own team and can’t pay £175,000 a quarter per engineer for someone else’s. We put the engineers in, on your stack, at a price that size of company can absorb.
Frequently asked questions
What is the best Palantir alternative for a mid-market company?
It depends on which half of Palantir you are replacing. Palantir sells a platform and an embedded engineering team. If the team is the part you can’t staff or afford, hire the team without the platform: Lunon for US mid-market companies, Ode for builds on Anthropic’s models, Forward Deployed for a 30/60/90 program, Accenture Edge for a global integrator. If the platform is what you want, Palantir for Builders or Adaptrix. For investigations, DataWalk.
What does Palantir cost?
Palantir doesn’t publish general commercial pricing. The public reference points are the UK G-Cloud 15 rate card (£66,000 per server core per year, £175,000 per engineer per quarter, about £3.4 million a year for a single-organization platform license) and the FY2025 annual report’s $93.9 million average revenue across its top twenty customers. Those describe large enterprise and public-sector deals. The SMB program may be priced very differently, but it doesn’t say.
Can small businesses use Palantir?
Yes. Palantir for Builders and its SMB page exist for exactly that, and they name customers in that bracket. What they don’t include is Palantir’s own forward deployed engineer, a price, a timeline or a minimum commitment. The size question is answered. The staffing and affordability questions are not.
What is a forward deployed engineer, and do I need one?
A forward deployed engineer is a software engineer who works inside the customer’s business who builds the data model and the working applications on top of it, rather than a consultant who writes the recommendation and leaves. Palantir made the role famous. You need one if the thing stopping you is not a decision but a build: the data is in four systems, the workflow lives in a spreadsheet, and nobody on staff can wire it together in production.
Palantir Foundry or Databricks for a mid-size company?
Databricks if you have a data team and want to own the result; it publishes its pricing and you can start on a free trial. Foundry if you want the ontology and applications packaged, and can absorb an unpublished price. Most mid-size companies we work with already run one of Databricks, Snowflake or Fabric, so the practical question is usually who builds on it, not which platform to buy.
Do I have to replace Palantir entirely?
No. If Foundry is deployed and working, the useful question is which problems are uneconomic to solve there, and whether a team working inside your company can build those on the systems next to it.
Where to go next
The delivery model and how we protect client material are on our how we work page. If you would rather see it than read about it, get in touch and we will scope one operating problem with you. Comparing AI transformation firms more broadly? Read the best AI transformation companies in 2026, or forward deployed engineering vs AI consulting.
Third-party claims come from the vendors’ own published materials and public documents, checked in November 2026. Positioning and pricing change. Where a vendor does not publish pricing, we have said so rather than estimated.
