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Best Palantir alternatives for mid-market companies in 2026
Eight Palantir alternatives for companies of 100 to 2,000 people and PE portfolios: what each one is, who it fits, what it publishes about price, and where it falls short.
By
The Lunon Team
12 minute read
IN SHORT
Palantir’s public rate card lists engineering at £150,000 per person per quarter and a single-organisation Foundry licence at £3 million a year. Three engineers for a year is £1.8 million before any software.
Palantir’s SMB programme removes the forward deployed engineer, which is the part of the model a mid-market company can least replace from its own staff.
Of eight alternatives compared, three publish a price. The rest, including us, scope and quote. Ask every vendor who builds, what you own, and what runs on the stack you already have.
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 14 rate card, the closest thing to a public price list, lists a Foundry licence at £66,000 per server core per year, discovery packages at £50,000 to £250,000, a single-organisation Foundry licence at £3 million a year, and implementation and engineering services at £150,000 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 licence. At the published rate, three engineers for a year is £1.8 million before any software.
That is the price of a transformation programme. A company with 300 employees usually doesn’t want a transformation programme. It wants the pricing desk off a spreadsheet by March.
The gap is real and measured. The US Census Bureau puts AI use at 37 percent of firms with 250 or more employees against 17 to 20 percent of businesses overall. RSM’s 2026 middle-market survey found 86 percent of mid-market firms have AI running somewhere, but only 17 percent are attempting anything enterprise-wide, and the two barriers they name are data quality (53 percent) and integration (47 percent). Those are 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 programme 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 programme removes the 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.
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
1. Lunon
Full disclosure: this is us. Hold this entry to the same standard as the rest.
Lunon is an AI-native consulting firm. We embed forward deployed engineers with your team, build working AI systems on the stack you already run, and leave the system and the operating model behind. It is Palantir’s delivery model without Palantir’s platform.
Best for: US mid-market companies and private-equity portfolio companies, roughly 100 to 2,000 employees, with an operating problem that needs a working system this quarter: a quoting desk, an underwriting queue, a diligence workflow, a reporting cycle that eats a week a month.
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 forward deployed engineering page.
2. Ode (formerly Fractional AI)
Fractional AI built embedded applied-AI teams that shipped a system and rolled off. In 2026 it became Ode, a services company backed by Anthropic and a group of private-equity investors.
What it is: Embedded engineers building Claude-based systems.
Best for: Mid-size companies in regulated, unglamorous sectors: community banks, regional health systems, mid-sized manufacturers. Its public case studies list the models and tools used and the measured impact, which is rarer than it should be.
Pricing transparency: Not published.
Honest limitation: A single-model house by design, 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.
3. Forward Deployed
The firm most like us on paper: embedded AI engineers for private-equity portfolios and operating teams, sold as a 30/60/90-day programme, from San Francisco, New York and London.
Best for: PE operating partners 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. Ask for referenceable outcomes at your company size.
4. 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 platform, and ties its fees to outcomes.
Best for: Fortune 100 and Fortune 500 companies. Its case studies are telecoms, payors and top-50 manufacturers.
Pricing transparency: Not published. Outcome-based contracts.
Honest limitation: Explicitly enterprise. A 500-person company is not its customer, and it says so.
5. Palantir for Builders
The honest “stay with Palantir” option: Foundry and AIP, with your people 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, and the platform stays theirs. If your team could model the business unaided, you probably would not be reading this.
6. Adaptrix
A German “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. From €30,000 a year, inference included. The most transparent vendor on this list.
Deployment: EU datacenters. 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.
7. 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: Not published, though it quotes Gotham’s list price to argue it is a fraction of it.
Honest limitation: Investigative software. If the problem is operational decision-making across finance, sales and supply chain, it is the wrong shape of tool.
8. 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 depending on edition. 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.
Which one is right for you
EXHIBIT
Eight Palantir alternatives sorted by the buyer’s priority. Three of the eight publish a price; the rest scope and quote.
Source: Lunon review of each vendor’s published materials, September 2026. Palantir figures from the FY2025 Form 10-K and the UK G-Cloud 14 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 programme 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 £150,000 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, and we leave.
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 and PE portfolio companies, Ode for Claude-native builds, Forward Deployed for a 30/60/90 programme. 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 14 rate card (£66,000 per server core per year, £150,000 per engineer per quarter, £3 million a year for a single-organisation Foundry licence) 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 programme 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 the 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 embedded in 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 an embedded team can build those on the systems next to it.
Where to go next
The delivery model, the operating loop and how we protect client material are on our forward deployed engineering page. If you would rather see it than read about it, get in touch and we will scope one operating problem with you.
Third-party claims come from the vendors’ own published materials and public documents, checked in September 2026. Positioning and pricing change. Where a vendor does not publish pricing, we have said so rather than estimated.
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