Harvey AI Alternative for Mid-Sized Law Firms

I started Aewita because mid-sized firms kept telling me the same thing: they wanted a serious legal AI product, they had the budget, and they could not get a real conversation with the vendor every analyst article told them to pick. Harvey’s sales team is pointed at AmLaw 100 partners with a head of knowledge management, a security committee, and a six-figure annual training budget. If you are a 12-attorney litigation boutique or a 60-attorney regional firm, Harvey is not saying no. It is just not built to say yes quickly.

This piece is a direct comparison for firms between 10 and 100 attorneys looking for a Harvey AI alternative that fits mid-sized firm procurement, pricing, and deployment realities. I will be specific about where each product is stronger, and I will be specific about our numbers.

Why Harvey AI is a poor fit for mid-sized firms

Harvey is a strong product. The team came out of Allen & Overy, they took early capital from serious investors, and they built genuine distribution inside BigLaw. None of that is wrong. It is just aimed elsewhere.

The practical friction looks like this. Harvey does not publish pricing. Every engagement is an enterprise contract, typically with seat minimums, a multi-year term, and a procurement cycle that runs through your managing partner, your IT director, your outside general counsel, and often your largest client’s outside-counsel guidelines desk. That cycle is reasonable for a 500-lawyer firm. For a 40-lawyer firm, it is three months of meetings before anyone types a prompt.

There is a second issue that matters more than price. Harvey runs on a third-party AI provider’s models. That means every prompt your associate writes, every clause your partner pastes into a draft, every witness-interview note you feed the assistant, passes through someone else’s infrastructure. Harvey’s enterprise agreements limit what the provider can do with that data, and those agreements are real. But the prompt still leaves Harvey’s boundary. Your clients’ outside-counsel guidelines may treat that as a subprocessor to add. Sometimes they treat it as a subprocessor to decline.

You should not have to run a subprocessor review to ask an AI a research question.

What mid-sized firms actually need from a Harvey AI alternative

When I talk to managing partners at firms between 10 and 100 attorneys, they tend to want four things in order:

1. Per-seat pricing they can read on a website. Not a quote. Not a call with a sales engineer. A number.
2. A trial that uses the real product. Not a curated sandbox. Real case law, real drafting, real answers.
3. No third-party AI subprocessor. One vendor, one boundary, one DPA.
4. Numbers on accuracy. Not adjectives. A published hallucination rate, with a confidence interval.

Aewita was designed against that list. Our pricing page shows $99 per attorney per month or $720 per attorney per year. That is it. No seat minimum. No annual commit required. 14-day free trial with the full product. Cancel anytime. A solo litigator pays the same per-seat price as a 200-attorney firm. The contract is a credit-card checkout.

Architectural privilege: we built the AI and we host the AI

The biggest architectural difference between Aewita and Harvey is simple. We built the AI. We host the AI. Every query runs on infrastructure we operate. Your prompts, your documents, and your citations never leave the Aewita boundary. There is no third-party AI provider in the data path.

This is not a marketing phrase. It is the reason a partner in a health-care practice can use Aewita without opening a subprocessor review with her hospital clients. It is the reason a government-contracts lawyer can use Aewita without a CUI-adjacent disclosure conversation. It is the reason a plaintiffs’ firm with a confidential settlement docket can use Aewita without renegotiating the protective order.

Our security practices page lays out the architecture in detail. The short version: we built the model, we trained the model, and every call terminates inside infrastructure we operate. There is no external AI provider to add to your DPA.

Hallucination rate, in numbers not adjectives

Every legal AI vendor talks about accuracy. Very few publish a number. This is the part of the evaluation where mid-sized firms get stuck, because the only honest way to compare two platforms is to compare measured outputs, and the platforms with nothing measured to share tend to talk the loudest.

Here is our number, verbatim. In internal testing, Aewita observed zero hallucinated outputs across 800 consecutive queries — statistically, a rough upper bound under 0.3% at 95% confidence. That is a measured rate, across a structured evaluation, on the same product you would use in a trial.

Most competitors in this market, including Harvey, have not published a comparable figure that I can find. They describe accuracy qualitatively. They cite satisfied customer quotes. Those are meaningful; they are not the same thing as a measured rate you can audit. If you are being asked to trust an AI tool to check cite-checking work, ask for a number. If the vendor will not produce one, that is information.

Coverage: every U.S. court opinion from 1700 to today

Aewita indexes every published U.S. court opinion from 1700 to today, plus every federal statute and every state statute across all 50 states and the District of Columbia. 792 document types. 22 practice areas. We made that choice deliberately. A real-property lawyer pulling chain-of-title from colonial Massachusetts needs 18th-century cases. A constitutional appellate lawyer citing foundational caselaw needs 19th-century cases. A contracts practitioner needs current UCC amendments across 50 states. A public finance lawyer needs the statutes of the state that issued the bond.

That coverage is table stakes for a U.S. legal AI platform. It is also the first place I would press a Harvey alternative to show its work. Ask any vendor to confirm completeness for your practice-area corpus before you sign anything.

For what Aewita’s research surface looks like on a real question, our research product page walks through the full flow, and our playbooks page shows how firm-specific drafting patterns get encoded.

$99/month vs. enterprise-contract: the side-by-side

Here is the comparison a managing partner actually needs.

	Aewita	Harvey AI
List price	$99/mo per attorney, or $720/yr	Not published
Seat minimum	None	Yes (enterprise)
Annual commit	Optional	Typically multi-year
Free trial	14 days, full product	Pilot by contract
Procurement	Credit card	MSA + DPA negotiation
Time to first query	Minutes	Weeks to months
AI provider	Self-hosted (no third party)	Third-party model provider
Published hallucination rate	<0.3% at 95% CI	Not published
U.S. case coverage	1700–today, complete	Not publicly specified
Statute coverage	Federal + all 50 states + D.C.	Not publicly specified

Where Harvey is actually stronger

I want to be fair. There are firms for which Harvey is the right answer. If you are a top-25 AmLaw shop, your knowledge-management leadership is already in a Harvey conversation, and your clients are comfortable with a third-party AI subprocessor, Harvey is a serious product backed by serious capital. If your matters are heavily cross-border into the UK or EU, Harvey’s non-U.S. corpora are more mature than ours today. If you want a customer-success org with people who have run 500-lawyer rollouts before, Harvey has that muscle.

For the mid-sized segment, though, Harvey is optimizing for the firm you do not have. You do not need a rollout specialist. You need a product your associates can use tomorrow morning.

Compliance by architecture, not by terms of service

ABA Model Rules 1.1 (competence), 1.6 (confidentiality), and 5.3 (supervisory responsibility) all attach to AI vendor choices. You can satisfy those rules with a third-party-AI product if you paper everything correctly, train your associates on the restrictions, monitor the vendor’s subprocessor list, and respond to each breach notification. Or you can satisfy them architecturally. Aewita is the latter path. Your data path terminates inside the platform. There is no second vendor to supervise. You can read more on our full comparison page.

That is why I keep saying we compete on architecture and on price, not on features. Features get copied. Architectural choices are hard to reverse.

How to evaluate a Harvey alternative in one afternoon

Here is what I would do if I were a managing partner at a 40-attorney firm evaluating this market this quarter:

1. Start a 14-day Aewita trial on your own credit card. No committee, no IT ticket.
2. Run five real questions from your current docket. Not demo questions. Real ones, with real jurisdictions and real dates.
3. Check every citation the answer produces. Open the case. Read the cited page. Verify the holding. Our verifier handles this automatically, but I want you to do it by hand once.
4. Paste a redlined clause from your hardest active deal into the drafting surface. Ask for two alternative formulations. Evaluate them against your firm’s style.
5. Hand the platform to one skeptic at the firm for 72 hours. Not the biggest fan of AI. The partner who has already written off this category. See what she says.

If after that week you decide Aewita is not right for your firm, you have lost nothing. If you decide it is, you have a platform in production without a six-month procurement cycle.

If you want to see the flow end-to-end with someone from our team, you can request a demo. Otherwise, the trial is the better first step; the product is the pitch.

Per-attorney legal AI used to mean Big Tech infrastructure rented through a middleman. It does not have to. A mid-sized firm can have a self-hosted, citation-verified, measured-hallucination-rate research and drafting platform for $99 per attorney per month. Start there. We built the AI. We host the AI. Your data stays in the boundary. Your associates start this week. That is the whole pitch.